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<chapter style="counter-reset: chapter 7"><h1>Linear Quadratic Regulators</h1>
    
  <p>While solving the dynamic programming problem for continuous systems is
  very hard in general, there are a few very important special cases where the
  solutions are very accessible. Most of these involve variants on the case of
  linear dynamics and quadratic cost.  The simplest case, called the linear
  quadratic regulator (LQR), is formulated as stabilizing a time-invariant
  linear system to the origin.</p>

  <p>The linear quadratic regulator is likely the most important and influential
  result in optimal control theory to date.  In this chapter we will derive the
  basic algorithm and a variety of useful extensions.</p>

  <section><h1>Basic Derivation</h1>

    <p>Consider a linear time-invariant system in state-space form, $$\dot{\bx}
    = {\bf A}\bx + \bB\bu,$$ with the infinite-horizon cost function given by
    $$J = \int_0^\infty \left[ \bx^T {\bf Q} \bx + \bu^T {\bf R} \bu \right] dt,
    \quad {\bf Q} = {\bf Q}^T \succeq {\bf 0}, {\bf R} = {\bf R}^T \succ 0.$$
    Our goal is to find the optimal cost-to-go function $J^*(\bx)$ which
    satisfies the HJB: $$\forall \bx, \quad 0 = \min_\bu \left[ \bx^T {\bf Q}
    \bx + \bu^T {\bf R} \bu + \pd{J^*}{\bx} \left( {\bf A}\bx + \bB\bu \right)
    \right].$$</p>

    <p> There is one important step here -- it is well known that for this
    problem the optimal cost-to-go function is quadratic.  This is easy to
    verify. Let us choose the form: $$J^*(\bx) = \bx^T {\bf S} \bx, \quad {\bf
    S} = {\bf S}^T \succeq 0.$$ The gradient of this function is $$\pd{J^*}{\bx}
      = 2 \bx^T {\bf S}.$$  </p>

    <p> Since we have guaranteed, by construction, that the terms inside the
    $\min$ are quadratic and convex (because ${\bf R} \succ 0$), we can take the
    minimum explicitly by finding the solution where the gradient of those terms
    vanishes: $$\pd{}{\bu} = 2\bu^T {\bf R} + 2 \bx^T {\bf S} \bB = 0.$$ This
    yields the optimal policy $$\bu^* = \pi^*(\bx) = - {\bf R}^{-1} \bB^T {\bf
    S} \bx = - \bK \bx.$$</p>

    <p>Inserting this back into the HJB and simplifying yields $$0 = \bx^T
    \left[ {\bf Q} - {\bf S B R}^{-1}\bB^T{\bf S} + 2{\bf SA} \right]\bx.$$ All
    of the terms here are symmetric except for the $2{\bf SA}$, but since
    $\bx^T{\bf SA}\bx = \bx^T{\bf A}^T{\bf S}\bx$, we can write $$0 = \bx^T
    \left[ {\bf Q} - {\bf S B R}^{-1}\bB^T{\bf S} + {\bf SA} + {\bf A}^T{\bf S}
    \right]\bx.$$ and since this condition must hold for all $\bx$, it is
    sufficient to consider the matrix equation $$0 = {\bf S} {\bf A} + {\bf A}^T
    {\bf S} - {\bf S} \bB {\bf R}^{-1} \bB^T {\bf S} + {\bf Q}.$$ This extremely
    important equation is a version of the <em>algebraic Riccati equation</em>.
    Note that it is quadratic in ${\bf S}$, making its solution non-trivial, but
    it is well known that the equation has a single positive-definite solution
    if and only if the system is controllable and there are good numerical
    methods for finding that solution, even in high-dimensional problems.  Both
    the optimal policy and optimal cost-to-go function are available from
    <drake></drake> by calling <code> (K,S) =
    LinearQuadraticRegulator(A,B,Q,R)</code>.</p>

    <p>If the appearance of the quadratic form of the cost-to-go seemed
    mysterious, consider that the solution to the linear system $\dot\bx = (\bA
    - \bB\bK)\bx$ takes the form $\bx(t) = e^{(\bA-\bB\bK)t}\bx(0)$, and try
    inserting this back into the integral cost function.  You'll see that the
    cost takes the form $J=\bx^T(0) {\bf S} \bx(0)$.</p>

    <p>It is worth examining the form of the optimal policy more closely. Since
    the value function represents cost-to-go, it would be sensible to move down
    this landscape as quickly as possible.  Indeed, $-{\bf S}\bx$ is in the
    direction of steepest descent of the value function. However, not all
    directions are possible to achieve in state-space. $-\bB^T {\bf S} \bx$
    represents precisely the projection of the steepest descent onto the control
    space, and is the steepest descent achievable with the control inputs $\bu$.
    Finally, the pre-scaling by the matrix ${\bf R}^{-1}$ biases the direction
    of descent to account for relative weightings that we have placed on the
    different control inputs.  Note that although this interpretation is
    straight-forward, the slope that we are descending (in the value function,
    ${\bf S}$) is a complicated function of the dynamics and cost.</p>

    <example><h1>LQR for the Double Integrator</h1>

      <p>Now we can use LQR to reproduce our <a
      href="dp.html#hjb_double_integrator">HJB example</a> from the previous
      chapter:</p>

      <div><pre><code class="python">import numpy as np
from pydrake.all import LinearQuadraticRegulator

# Define the double integrator's state space matrices.
A = np.array([[0, 1], [0, 0]])
B = np.array([[0], [1]])
Q = np.eye(2)
R = np.eye(1)

(K, S) = LinearQuadraticRegulator(A, B, Q, R)
print("K = " + str(K))
print("S = " + str(S))</code></pre></div>

      <p>As in the hand-derived example, our numerical solution returns $${\bf
      K} = [ 1, \sqrt{3} ], \qquad{\bf S} = \begin{bmatrix} \sqrt{3} & 1 \\ 1 &
      \sqrt{3} \end{bmatrix}.$$</p>
    </example>

    <subsection><h1>Local stabilization of nonlinear systems</h1>

      <p>LQR is extremely relevant to us even though our primary interest is in
      nonlinear dynamics, because it can provide a local approximation of the
      optimal control solution for the nonlinear system.  Given the nonlinear
      system $\dot{\bx} = f(\bx,\bu)$, and a stabilizable operating point,
      $(\bx_0,\bu_0)$, with $f(\bx_0,\bu_0) = 0.$  We can define a relative
      coordinate system $$\bar\bx = \bx - \bx_0, \quad \bar\bu = \bu - \bu_0,$$
      and observe that $$\dot{\bar\bx} = \dot{\bx} = f(\bx,\bu),$$ which we can
      approximate with a first-order Taylor expansion to $$\dot{\bar\bx} \approx
      f(\bx_0,\bu_0) + \pd{f(\bx_0,\bu_0)}{\bx} (\bx - \bx_0) +
      \pd{f(\bx_0,\bu_0)}{\bu} (\bu - \bu_0) = {\bf A}\bar{\bx} +
      \bB\bar\bu.$$</p>

      <p>Similarly, we can define a quadratic cost function in the error
      coordinates, or take a (positive-definite) second-order approximation of a
      nonlinear cost function about the operating point (linear and constant
      terms in the cost function can be easily incorporated into the derivation
      by parameterizing a full quadratic form for $J^*$, as seen in the Linear
      Quadratic Tracking derivation below).</p>

      <p>The resulting controller takes the form $\bar\bu^* = -{\bf K}\bar\bx$
      or $$\bu^* = \bu_0 - {\bf K} (\bx - \bx_0).$$  For convenience,
      <drake></drake> allows you to call <code>controller =
      LinearQuadraticRegulator(system, context, Q, R)</code> on most dynamical
      systems (including block diagrams built up of many subsystems); it will
      perform the linearization for you.</p>

      <example><h1>LQR for Acrobots, Cart-Poles,
      and Quadrotors</h1>

        <p>LQR provides a very satisfying solution to the canonical "balancing"
        problem that we've <a href="acrobot.html">already
        described for a number of model systems</a>.  Here is the notebook with those examples, again:</p>
      
        <script>document.write(notebook_link('acrobot'))</script>
  
        <p>I find it very compelling that the same derivation (and effectively
        identical code) can stabilize such a diversity of systems!</p>
      </example>

    </subsection>

  </section> <!-- end basic derivation -->

  <section id="finite_horizon"><h1>Finite-horizon formulations</h1>

    <p>Recall that the cost-to-go for finite-horizon problems is time-dependent,
    and therefore the HJB sufficiency condition requires an additional term for
    $\pd{J^*}{t}$. $$ \forall \bx, \forall t\in[t_0,t_f],\quad 0 = \min_\bu
    \left[ \ell(\bx,\bu) + \pd{J^*}{\bx}f(\bx,\bu) + \pd{J^*}{t} \right]. $$</p>

    <subsection><h1>Finite-horizon LQR</h1>

      <p> Consider systems governed by an LTI state-space equation of the form
      $$\dot{\bx} = {\bf A}\bx + \bB\bu,$$ and a finite-horizon cost function, $J = h(\bx(t_f)) + \int_0^{t_f} \ell(\bx(t), \bu(t)) dt,$
      with
      \begin{gather*} h(\bx) = \bx^T {\bf Q}_f \bx,\quad {\bf Q}_f = {\bf Q}_f^T
      \succeq {\bf 0} \\ \ell(\bx,\bu) = \bx^T {\bf Q} \bx + \bu^T {\bf R}
      \bu, \quad {\bf Q} = {\bf Q}^T \succeq 0, {\bf R}={\bf R}^T \succ 0
      \end{gather*}

      Writing the HJB, we have $$ 0 = \min_\bu \left[\bx^T {\bf Q} \bx + \bu^T
      {\bf R}\bu + \pd{J^*}{\bx} \left({\bf A} \bx + \bB \bu \right) +
      \pd{J^*}{t} \right]. $$  Due to the positive definite quadratic form on
      $\bu$, we can find the minimum by setting the gradient to zero:

      \begin{gather*}
      \pd{}{\bu} = 2 \bu^T {\bf R} + \pd{J^*}{\bx} \bB  = 0 \\
      \bu^* = \pi^*(\bx,t) = - \frac{1}{2}{\bf R}^{-1} \bB^T \pd{J^*}{\bx}^T
      \end{gather*}

      In order to proceed, we need to investigate a particular form for the
      cost-to-go function, $J^*(\bx,t)$.  Let's try a solution of the form:
      $$J^*(\bx,t) = \bx^T {\bf S}(t) \bx, \quad {\bf S}(t) = {\bf S}^T(t)\succ
      {\bf 0}.$$ In this case we have $$\pd{J^*}{\bx} = 2 \bx^T {\bf S}(t),
      \quad \pd{J*}{t} = \bx^T \dot{\bf S}(t) \bx,$$ and therefore

      \begin{gather*} \bu^* = \pi^*(\bx,t) = - {\bf R}^{-1} \bB^T {\bf S}(t) \bx
      \\ 0 = \bx^T \left[ {\bf Q} - {\bf S}(t) \bB {\bf R}^{-1} \bB^T {\bf S}(t)
      + {\bf S}(t) {\bf A} + {\bf A}^T {\bf S}(t) + \dot{\bf S}(t) \right]
      \bx.\end{gather*}

      Therefore, ${\bf S}(t)$ must satisfy the condition (known as the
      continuous-time <em>differential Riccati equation</em>): $$-\dot{\bf S}(t)
      = {\bf S}(t) {\bf A} + {\bf A}^T {\bf S}(t) - {\bf S}(t) \bB {\bf R}^{-1}
      \bB^T {\bf S}(t) + {\bf Q},$$ and the terminal condition $${\bf S}(t_f) =
      {\bf Q}_f.$$ Since we were able to satisfy the HJB with the minimizing
      policy, we have met the sufficiency condition, and have found the optimal
      policy and optimal cost-to-go function.</p>

      <p>Note that the infinite-horizon LQR solution described in the prequel is
      exactly the steady-state solution of this equation, defined by $\dot{\bf
      S}(t) = 0$. Indeed, for controllable systems this equation is stable
      (backwards in time), and as expected the finite-horizon solution converges
      on the infinite-horizon solution as the horizon time limits to infinity.
      </p>

      <todo>Example to show how the tvlqr solution converges to the tilqr
      solution for the double integrator example, and make the connection back
      to the value iteration visualizations that we did in the previous
      chapter.</todo>

      <p>The solution ${\bf S}(t)$ will be the result of numerical integration.
      On weakly controllable systems (like the <a
      href="trajopt.html#perching">perching airplane</a>), I've found that even
      error-controlled integration can lead to crippling numerical artifacts
      such as the loss of symmetry or positive definiteness. For a more robust
      numerical solution, we can instead integrate a factorization of ${\bf S}$
      that ensures these properties are maintained. Taking ${\bf S}(t) = {\bf
      P}(t){\bf P}^T(t)$, and again using the fact that $\bx^T{\bf P}\dot{\bf
      P}^T\bx = \bx^T\dot{\bf P}{\bf P}^T \bx$, we can write the "square-root
      form" of the Riccati differential equation: $$-\dot{\bf P}(t) = \bA^T
      {\bf P}(t) - \frac{1}{2}{\bf S}(t)\bB{\bf R}^{-1}\bB^T{\bf P}(t) +
      \frac{1}{2}{\bf Q}{\bf P}^{-T}(t), \quad {\bf P}(t_f) = {\bf
      Q}_f^\frac{1}{2}.$$ This form does require that ${\bf P}(t)$ is
      invertible, which in turn requires that ${\bf Q}_f \succ 0.$</p>

    </subsection>

    <subsection><h1>Time-varying LQR</h1>

      <p>The derivation above holds even if the dynamics are given by
      $$\dot{\bx} = {\bf A}(t)\bx + {\bf B(t)}\bu.$$  Similarly, the cost
      functions ${\bf Q}$ and ${\bf R}$ can also be time-varying.  This is
      quite surprising, as the class of time-varying linear systems is a quite
      general class of systems. It requires essentially no assumptions on how
      the time-dependence enters, except perhaps that if ${\bf A}$ or $\bB$ is
      discontinuous in time then one would have to use the proper techniques to
      accurately integrate the differential equation.</p>

    </subsection>

    <subsection id=finite_horizon_nonlinear><h1>Local trajectory stabilization for nonlinear systems</h1>

      <p> One of the most powerful applications of time-varying LQR involves
      linearizing around a nominal trajectory of a nonlinear system and using
      LQR to provide a trajectory controller.  This will tie in very nicely
      with the algorithms we develop in the <a href="trajopt.html">chapter on
      trajectory optimization</a>.</p>
        
      <p>Let us assume that we have a nominal trajectory, $\bx_0(t), \bu_0(t)$
      defined for $t \in [t_1, t_2]$.  Similar to the time-invariant analysis,
      we begin by defining a local coordinate system relative to the
      trajectory: $$\bar\bx(t) = \bx(t) - \bx_0(t), \quad \bar\bu(t) = \bu(t) -
      \bu_0(t).$$  Now we have $$\dot{\bar\bx} = \dot{\bx} - \dot{\bx}_0 =
      f(\bx,\bu) - f(\bx_0, \bu_0),$$ which we can again approximate with a
      first-order Taylor expansion to $$\dot{\bar\bx} \approx f(\bx_0,\bu_0) +
      \pd{f(\bx_0,\bu_0)}{\bx} (\bx - \bx_0) + \pd{f(\bx_0,\bu_0)}{\bu} (\bu -
      \bu_0) - f(\bx_0,\bu_0) = {\bf A}(t)\bar{\bx} + \bB(t)\bar\bu.$$  This
      very similar to using LQR to stablize a fixed-point, but with some
      important differences.  First, the linearization is time-varying.
      Second, our linearization is valid for any state along a feasible
      trajectory (not just fixed-points), because the coordinate system is
      moving along with the trajectory.</p>

      <p>Similarly, we can define a quadratic cost function in the error
      coordinates, or take a (positive-definite) second-order approximation of a
      nonlinear cost function along the trajectory (linear and constant
      terms in the cost function can be easily incorporated into the derivation
      by parameterizing a full quadratic form for $J^*$, as seen in the Linear
      Quadratic Tracking derivation below).</p>

      <p>The resulting controller takes the form $\bar\bu^* = -{\bf
      K}(t)\bar\bx$ or $$\bu^* = \bu_0(t) - {\bf K}(t) (\bx - \bx_0(t)).$$
      <drake></drake> provides a <a
      href="https://drake.mit.edu/doxygen_cxx/group__control.html#ga58307d2135757a498c434e96d7b99853"><code>
        FiniteHorizonLinearQuadraticRegulator</code></a>
      method; if you pass it a nonlinear systems it will perform the
      linearization in the proper coordinates for you using automatic
      differentiation.</p>

      <p>Remember that stability is a statement about what happens as time goes
      to infinity.  In order to talk about stabilizing a trajectory, the
      trajectory must be defined for all $t \in [t_1, \infty)$.  This can be
      accomplished by considering a finite-time trajectory which terminates at
      a stabilizable fixed-point at a time $t_2 \ge t_1$, and remains at the
      fixed point for $t \ge t_2$.  In this case, the finite-horizon Riccati
      equation is initialized with the infinite-horizon LQR solution: $S(t_2) =
      S_\infty$, and solved backwards in time from $t_2$ to $t_1$ for the
      remainder of the trajectory.  And <i>now</i> we can say that we have
      <i>stabilized</i> the trajectory!</p>

    </subsection>    

    <subsection><h1>Linear Quadratic Optimal Tracking</h1>

      <p>For completeness, we consider a slightly more general form of the
      linear quadratic regulator.  The standard LQR derivation attempts to drive
      the system to zero.  Consider now the problem:

      \begin{gather*} \dot{\bx} = {\bf A}\bx + \bB\bu \\ h(\bx) = (\bx -
      \bx_d(t_f))^T {\bf Q}_f (\bx - \bx_d(t_f)), \quad {\bf Q}_f = {\bf Q}_f^T
      \succeq 0 \\ \ell(\bx,\bu,t) = (\bx - \bx_d(t))^T {\bf Q} (\bx - \bx_d(t))
      + (\bu - \bu_d(t))^T {\bf R} (\bu - \bu_d(t)),\\ {\bf Q} = {\bf Q}^T
      \succeq 0, {\bf R}={\bf R}^T \succ 0 \end{gather*}

      Now, guess a solution

      \begin{gather*} J^*(\bx,t) = \bx^T {\bf S}_{xx}(t) \bx + 2\bx^T {\bf
      s}_x(t) + s_0(t), \quad {\bf S}_{xx}(t) = {\bf S}_{xx}^T(t)\succ {\bf 0}.
      \end{gather*}

      In this case, we have $$\pd{J^*}{\bx} = 2 \bx^T {\bf S}_{xx}(t) + 2{\bf
      s}_{x}^T(t),\quad \pd{J^*}{t} = \bx^T \dot{\bf S}_{xx}(t) \bx + 2\bx^T
      \dot{\bf s}_x(t) + \dot{s}_0(t).$$ Using the HJB, $$ 0 = \min_\bu
      \left[(\bx - \bx_d(t))^T {\bf Q} (\bx - \bx_d(t)) + (\bu - \bu_d(t))^T
      {\bf R} (\bu - \bu_d(t)) + \pd{J^*}{\bx} \left({\bf A} \bx + \bB \bu
      \right) + \pd{J^*}{t} \right], $$ we have

      \begin{gather*} \pd{}{\bu} = 2 (\bu - \bu_d(t))^T{\bf R} + (2\bx^T{\bf
      S}_{xx}(t) + 2{\bf s}_x^T(t))\bB = 0,\\ \bu^*(t) =  \bu_d(t) - {\bf
      R}^{-1} \bB^T\left[{\bf S}_{xx}(t)\bx + {\bf s}_x(t)\right]
      \end{gather*}

      The HJB can be satisfied by integrating backwards

      \begin{align*}
      -\dot{\bf S}_{xx}(t) =& {\bf Q} - {\bf S}_{xx}(t) \bB {\bf R}^{-1} {\bf
      B}^T {\bf S}_{xx}(t) + {\bf S}_{xx}(t) {\bf A} + {\bf A}^T {\bf S}_{xx}(t)\\
      -\dot{\bf s}_x(t) =& - {\bf Q} \bx_d(t) + \left[{\bf A}^T - {\bf S}_{xx}
        \bB {\bf R}^{-1} \bB^T \right]{\bf s}_x(t) + {\bf
        S}_{xx}(t) \bB \bu_d(t)\\
      -\dot{s}_0(t) =&  \bx_d(t)^T {\bf Q} \bx_d(t) - {\bf
        s}_x^T(t) \bB {\bf R}^{-1} \bB^T {\bf s}_x(t) + 2{\bf
        s}_x(t)^T \bB \bu_d(t),
      \end{align*}

      from the final conditions

      \begin{align*}
      {\bf S}_{xx}(t_f) =& {\bf Q}_f\\
      {\bf s}_{x}(t_f) =& -{\bf Q}_f \bx_d(t_f) \\
      s_0(t_f) =& \bx_d^T(t_f) {\bf Q}_f \bx_d(t_f).
      \end{align*}

      Notice that the solution for ${\bf S}_{xx}$ is the same as the simpler LQR
      derivation, and is symmetric (as we assumed).  Note also that $s_0(t)$ has
      no effect on control (even indirectly), and so can often be ignored.</p>

      <p>A quick observation about the quadratic form, which might be helpful
      in debugging.  We know that $J(\bx,t)$ must be uniformly positive.  This
      is true iff ${\bf S}_{xx}\succ 0$ and $s_0 > {\bf s}_x^T {\bf
      S}_{xx}^{-1} {\bf s}_x$, which comes from evaluating the function at
      $\bx_{min}(t)$ defined by $\left[ \pd{J^*(\bx,t)}{\bx}
      \right]_{\bx=\bx_{min}(t)} = 0$.</p>

      <todo>test this on an example, get my notation consistent (s(t)^T vs
      s^T(t), etc.</todo>
    </subsection>

    <subsection><h1>Linear Final Boundary Value Problems</h1>

      <p> The finite-horizon LQR formulation can be used to impose a strict
      final boundary value condition by setting an infinite ${\bf Q}_f$.
      However, integrating the Riccati equation backwards from an infinite
      initial condition isn't very practical.  To get around this, let us
      consider solving for ${\bf P}(t) = {\bf S}(t)^{-1}$.  Using the matrix
      relation $\frac{d {\bf S}^{-1}}{dt} = - {\bf S}^{-1} \frac{d {\bf S}}{dt}
      {\bf S}^{-1}$, we have: $$-\dot{\bf P}(t) = - {\bf P}(t){\bf Q P}(t) +
      {\bf B R}^{-1} \bB - {\bf A P}(t) - {\bf P}(t){\bf A}^T,$$ with the final
      conditions $${\bf P}(t_f) = 0.$$ This Riccati equation can be integrated
      backwards in time for a solution.</p>

      <p> It is very interesting, and powerful, to note that, if one chooses
      ${\bf Q} = 0$, therefore imposing no position cost on the trajectory
      before time $T$, then this inverse Riccati equation becomes a linear ODE
      which can be solved explicitly. <todo>% add explicit solution here</todo>
      These relationships are used in the derivation of the controllability
      Grammian, but here we use them to design a feedback controller.</p>

    </subsection>

  </section> <!-- end finite horizon -->

  <section><h1>Variations and extensions</h1>

    <!--
    <subsection><h1>Minimum-time LQR</h1>

      \begin{gather*}
      \dot{\bx} = {\bf A}\bx + \bB\bu \\
      h(\bx) = \bx^T {\bf Q}_f \bx,
      \quad {\bf Q}_f = {\bf Q}_f^T \succeq 0 \\
      \ell(\bx,\bu,t) = 1 + \bx^T {\bf Q} \bx + \bu {\bf R} \bu,\\
      {\bf Q} = {\bf
      Q}^T \succeq 0, {\bf R}={\bf R}^T  \succ 0
      \end{gather*}

    </subsection>
    -->

    <subsection id="dt_riccati"><h1>Discrete-time Riccati Equations</h1>

      <p>Essentially all of the results above have a natural correlate for
      discrete-time systems. What's more, the discrete time versions tend to be
      simpler to think about in the model-predictive control (MPC) setting that
      we'll be discussing below and in the next chapters.</p>

      <p>Consider the discrete time dynamics: $$\bx[n+1] = {\bf A}\bx[n] + {\bf
      B}\bu[n],$$ and we wish to minimize $$\min \sum_{n=0}^{N-1} \bx^T[n] {\bf
      Q} \bx[n] + \bu^T[n] {\bf R} \bu[n], \qquad {\bf Q} = {\bf Q}^T \succeq 0,
      {\bf R} = {\bf R}^T \succ 0.$$  The cost-to-go is given by $$J(\bx,n-1) =
      \min_\bu \bx^T {\bf Q} \bx + \bu^T {\bf R} \bu + J({\bf A}\bx + {\bf
      B}\bu, n).$$ If we once again take $$J(\bx,n) = \bx^T {\bf S[n]} \bx,
      \quad {\bf S}[n] = {\bf S}^T[n] \succ 0,$$ then we have $$\bu^*[n] = -{\bf
      K[n]}\bx[n] = -({\bf R} + {\bf B}^T {\bf S}[n] {\bf B})^{-1} {\bf B}^T
      {\bf S}[n] {\bf A} \bx[n],$$ yielding $${\bf S}[n-1] = {\bf Q} + {\bf
      A}^T{\bf S}[n]{\bf A} - ({\bf A}^T{\bf S}[n]{\bf B})({\bf R} + {\bf B}^T
      {\bf S}[n] {\bf B})^{-1} ({\bf B}^T {\bf S}[n]{\bf A}), \quad {\bf S}[N]
      = 0,$$ which is the famous <i>Riccati difference equation</i>. The
      infinite-horizon LQR solution is given by the (positive-definite)
      fixed-point of this equation: $${\bf S} = {\bf Q} + {\bf A}^T{\bf S}{\bf
      A} - ({\bf A}^T{\bf S}{\bf B})({\bf R} + {\bf B}^T {\bf S} {\bf B})^{-1}
      ({\bf B}^T {\bf S}{\bf A}).$$  Like in the continuous time case, this
      equation is so important that we have special numerical recipes for
      solving this discrete-time algebraic Riccati equation (DARE).  <drake>
      </drake> delegates to these numerical methods automatically when you
      evaluate the <code>LinearQuadraticRegulator</code> method on a system that
      has only discrete state and a single periodic time step.</p> 

      <example><h1>Discrete-time vs Continuous-time LQR</h1>

        <p>You can explore the relationship between the discrete-time and continuous-time formulations in this notebook:</p>
  
        <script>document.write(notebook_link('lqr'))</script>
  
      </example>

      <p>In reinforcement learning, it is popular to consider the
      infinite-horizon "discounted" cost: $\min \sum_{n=0}^\infty \gamma^n
      (\bx^T[n]{\bf Q}\bx[n] + \bu^T[n]{\bf R}\bu[n]).$  The optimal controller
      is $$\bu^* = -\gamma(\bR + \gamma \bB^T {\bf S} \bB)^{-1} \bB^T {\bf S}
      \bA \bx.$$ and the corresponding Riccati equation is $${\bf S} = {\bf Q} +
      \gamma {\bf A}^T{\bf S}{\bf A} - \gamma^2({\bf A}^T{\bf S}{\bf B})({\bf
      R} + \gamma {\bf B}^T {\bf S} {\bf B})^{-1} ({\bf B}^T {\bf S}{\bf A}),$$
      whose solution can be found using
        <code>DiscreteAlgebraicRiccatiEquation</code>$(\sqrt{\gamma} {\bf A},
        {\bf B}, {\bf Q}, \frac{1}{\gamma} {\bf R}).$</p>

      <example id="fvi"><h1>LQR via Fitted Value Iteration</h1>

        <p>In the dynamic programming chapter, we developed general purpose
        tools for <a href="dp.html#function_approximation">approximating value
        functions with function approximators</a>.  I think it is particularly
        illustrative to see how these work out for LQR.  Let's consider the
        discrete-time, infinite-horizon, discounted case.</p>

        <p>For LQR, we know that the optimal value function will take a
        quadratic form, $\bx^T {\bf S}\bx.$ Although it is quadratic in $\bx$,
        this form is linear in the parameters, ${\bf S}$. We can
        leverage some of the special tools for fitted value iteration with a
        <i>linear function approximator</i>.</p>
        
        <script>document.write(notebook_link('lqr'))</script>
        
        <p>I've included two versions of the value iteration update in the
        notebook -- one that samples over both $\bx$ and $\bu$, and one that
        samples only over $\bx$ and uses the LQR policy (given the current
        estimated cost-to-go) to determine $\bu$.  The difference is not
        subtle when $\gamma \rightarrow 1$. Take a look!</p>

        <p>We must remember that there are multiple solutions to the Riccati
        equation -- the solution to the LQR problem is the (unique) positive
        definite solution.  But there are non-positive definite solutions, too,
        which lead to unstable controllers -- these solutions do achieve zero
        Bellman residual. Every solution to the Riccati equation is a fixed
        point of the (fitted) value iteration update, but only the
        positive-definite solution is a stable fixed point of the
        algorithm.</p>

        <p>To see this, write $\hat{J}(\bx) = \bx^{T} ({\bf S}^* + {\bf
        \Delta})\bx$, where ${\bf S}^*$ is any solution to the Riccati
        equation, and ${\bf \Delta}$ is a small deviation matrix. If we write
        the error dynamics through the fitted value iteration update, we can
        see that $${\bf \Delta}_{i+1} = (\bA - \bB\bK_i)^T {\bf \Delta}_i (\bA
        - \bB \bK_i),$$ where ${\bf K}_i$ is the optimal controller given ${\bf
        S}_i = {\bf S}^* + {\bf \Delta}_i \approx \bK^*.$  This error converges
        to zero if and only if $(\bA - \bB\bK^*)$ is stable, which is only if
        ${\bf S}^* \succ 0$.</p>

        <!-- Error dynamics derivation 
          S_{i+1} = Q + K'RK + (A-BK)'S_i(A-BK)
          S_i = S* + E_i => S_{i+1} = S* + (A-BK)'E_i(A-BK) =>
          E_i = (A-BK)'E_i(A-BK)
        -->
        
        <p>Go ahead and try many different initial ${\bf S}$ in the code to
        verify it for yourself.</p>
      </example>

    </subsection>

    <subsection><h1>LQR with input and state constraints</h1>
      <p>A natural extension for linear optimal control is the consideration of
      strict constraints on the inputs or state trajectory.  Most common are
      linear inequality constraints, such as $\forall n, |\bu[n]| \le 1$ or
      $\forall n, \bx[n] \ge -2$ (any linear constraints of the form ${\bf Cx} +
      {\bf Du} \le {\bf e}$ can be solved with the same tools).  Much is known
      about the solution to this problem in the discrete-time case, but it's
      computation is signficantly harder than the unconstrained case.  Almost
      always, we give up on solving for the best control policy in closed form,
      and instead solve for the optimal control trajectory $\bu[\cdot]$ from a
      particular initial condition $\bx[0]$ over some finite horizon.
      Fortunately, this problem is a convex optimization and we can often solve
      it quickly and reliably enough to solve it at every time step, effectively
      turning a motion planning algorithm into a feedback controller; this idea
      is famously known as model-predictive control (MPC).  We will provide the
      details in the <a href="trajopt.html">trajectory
      optimization chapter</a>.</p>
        
      <p>We do actually understand what the optimal policy of the
      inequality-constrained LQR problem looks like, thanks to work on "explicit
      MPC" <elib>Alessio09</elib> -- the optimal policy is now piecewise-linear
      (though still continuous), with each piece describe by a polytope, and the
      optimal cost-to-go is piecewise-quadratic on the same polytopes.
      Unfortunately, the number of pieces grows exponentially with the number of
      constraints and the horizon of the problem, making it impractical to
      compute for all but very small problems.  There are, howeer, a number of
      promising approaches to approximate explicit MPC (c.f.
      <elib>Marcucci17</elib>).</p>

      <p>One important case that does have closed-form solutions is LQR with
      linear <i>equality</i> constraints (c.f. <elib>Posa15</elib>, section
      IIIb).  This is particularly relevant in the case of stabilizing robots
      with kinematic constraints such as a closed kinematic chain, which appears
      in four-bar linkages or even for the linearization of a biped robot with
      two feet on the ground.</p>

      <todo>Add the equality-constrained LQR derivation here.</todo>

    </subsection>

    <subsection id="lmi"><h1>LQR as a convex optimization</h1>

      <p>One can also design the LQR gains using linear matrix inequalities
      (LMIs). I will defer the derivation til we cover the policy gradient view
      of LQR, because the LMI formulation is based on a change of variables
      from the basic policy evaluation criterion.  If you want to look ahead,
      you can find that formulation <a
      href="policy_search.html#lqr_lmi">here</a>.</p>

      <p>Solving the algebraic Riccati equation is still the preferred way of
      computing the LQR solution.  But it is helpful to know that one could
      also compute it with convex optimization.  In addition to deepening our
      understanding, this can be useful for generalizing the basic LQR solution
      (e.g. for <a href="lyapunov.html#common-lyapunov-linear">robust
      stabilization</a>) or to solve for the LQR gains jointly as part of a
      bigger optimization.</p>

    </subsection>

    <subsection id="sls"><h1>Finite-horizon LQR via least
    squares</h1>
  
      <p>We can also obtain the solution to the discrete-time finite-horizon
      (including the time-varying or tracking variants) LQR problem using
      optimization -- in this case it actually reduces to a simple
      least-squares problem.  The presentation in this section can be viewed as
      a simple implementation of the <a
      href="https://en.wikipedia.org/wiki/Youla%E2%80%93Kucera_parametrization">Youla
      parameterization</a> (sometimes called "Q-parameterization") from
      controls.  Small variations on the formulation here play an important
      role in the minimax variants of LQR (which optimize a worst-case
      performance), which we will discuss in the robust control chapter (e.g.
      <elib>Lofberg03+Sadraddini20</elib>).</p>

      <p>First, let us appreciate that the default parameterization is not
      convex.  Given \begin{gather*} \min \sum_{n=0}^{N-1} \bx^T[n] {\bf Q}
      \bx[n] + \bu^T[n] {\bf R} \bu[n], \qquad {\bf Q} = {\bf Q}^T \succeq 0,
      {\bf R} = {\bf R}^T \succ 0 \\ \subjto \quad \bx[n+1] = {\bf A} \bx[n] +
      {\bf B}\bu[n], \\ \bx[0] = \bx_0 \end{gather*} if we wish to search over
      controllers of the form $$\bu[n] = {\bf K}_n \bx[n],$$ then we have
      \begin{align*}\bx[1] &= {\bf A}\bx_0 + {\bf B}{\bf K}_0\bx_0, \\ \bx[2] &=
      {\bf A}({\bf A} + {\bf BK}_0)\bx_0 + {\bf BK}_1({\bf A} + {\bf BK}_0)\bx_0
      \\ \bx[n] &= \left( \prod_{i=0}^{n-1} ({\bf A} + {\bf BK}_i) \right) \bx_0
      \end{align*} As you can see, the $\bx[n]$ terms in the cost function
      include our decision variables multiplied together -- resulting in a
      non-convex objective.  The trick is to re-parameterize the decision
      variables, and write the feedback in the form: $$\bu[n] = \tilde{\bf K}_n
      \bx_0,$$ leading to \begin{align*}\bx[1] &= {\bf A}\bx_0 + {\bf
      B}\tilde{\bf K}_0\bx_0, \\ \bx[2] &= {\bf A}({\bf A} + {\bf B}\tilde{\bf
      K}_0)\bx_0 + {\bf B}\tilde{\bf K}_1 \bx_0 \\ \bx[n] &= \left( {\bf A}^n +
      \sum_{i=0}^{n-1}{\bf A}^{n-i-1}{\bf B}\tilde{\bf K}_{i} \right) \bx_0
      \end{align*}  Now all of the decision variables, $\tilde{\bf K}_i$, appear
      linearly in the solution to $\bx[n]$ and therefore (convex) quadratically
      in the objective.</p>  
      
      <p>We still have an objective function that depends on $\bx_0$, but we
      would like to find the optimal $\tilde{\bf K}_i$ <i>for all</i>
      $\bx_0$. To achieve this let us evaluate the optimality conditions of this
      least squares problem, starting by taking the gradient of the objective
      with respect to $\tilde{\bf K}_i$, which is: $$\bx_0 \bx_0^T \left(
      \tilde{\bf K}_i^T \left({\bf R} + \sum_{m=i+1}^{N-1} {\bf B}^T ({\bf
      A}^{m-i-1})^T {\bf Q A}^{m-i-1} {\bf B}\right) + \sum_{m=i+1}^{N-1} ({\bf
      A}^m)^T {\bf Q A}^{m-i-1} {\bf B}\right).$$  We can satisfy this
      optimality condition for all $\bx_0$ by solving the <i>linear</i> matrix
      equation: $$\tilde{\bf K}_i^T \left({\bf R} + \sum_{m=i+1}^{N-1} {\bf B}^T
      ({\bf A}^{m-i-1})^T {\bf Q A}^{m-i-1} {\bf B}\right) + \sum_{m=i+1}^{N-1}
      ({\bf A}^m)^T {\bf Q A}^{m-i-1} {\bf B} = 0.$$  We can always solve for
      $\tilde{\bf K}_i$ since it's multiplied by a (symmetric) positive definite
      matrix (it is the sum of a positive definite matrix and many positive
      semi-definite matrices), which is always invertible.</p>

      <p>If you need to recover the original ${\bf K}_i$ parameters, you can
      extract them recursively with \begin{align*} \tilde{\bf K}_0 &= {\bf K}_0,
      \\ \tilde{\bf K}_n &= {\bf K}_n \prod_{i=0}^{n-1} ({\bf A} + {\bf BK}_i),
      \qquad 0 < n \le N-1. \end{align*}  But often this is not actually
      necessary.  In some applications it's enough to know the performance cost
      under LQR control, or to handle the response to disturbances explicitly
      with the disturbance-based feedback (which I've already promised for the
      robust control chapter).  After all, the problem formulation that we've
      written here, which makes no mention of disturbances, assumes the model is
      perfect and the controls $\tilde{\bf K}_n \bx_0$ are just as suitable for
      deployment as ${\bf K}_n\bx[n]$.</p>

      <p>"System-Level Synthesis" (SLS) is the name for an important and
      slightly different approach, where one optimizes the <i>closed-loop
      response</i> directly<elib>Anderson19</elib>. Although SLS is a very
      general tool, for the particular formulation we are considering here it
      reduces to creating additional decision variables ${\bf \Phi}_i$, such at
      that $$\bx[n] = {\bf \Phi}_n \bx[0],$$  and writing the optimization above
      as \begin{gather*} \min_{\tilde{\bf K}_*, {\bf \Phi}_*} \sum_{n=0}^{N-1}
      \bx^T[0] \left( {\bf \Phi}_n^T {\bf Q \Phi}_n + \tilde{\bf K}_n^T{\bf R}
      \tilde{\bf K}_n \right) \bx[0], \\ \subjto \qquad \forall n, \quad {\bf
      \Phi}_{n+1} = {\bf A \Phi}_n + {\bf B}\tilde{\bf K}_n. \end{gather*} </p>
      
      <p>Once again, the algorithms presented here are not as efficient as
      solving the Riccati equation if we only want the solution to the simple
      case, but they become very powerful if we want to combine the LQR
      synthesis with other objectives/constraints.  For instance, if we want to
      add some sparsity constraints (e.g. enforcing that some elements of
      $\tilde{\bf K}_i$ are zero), then we could solve the quadratic
      optimization subject to linear equality constraints
      <elib>Wang14</elib>.</p>
  
    </subsection>
    
    <todo>
      Minimum-time LQR
    </todo>
    
  </section>

  <section><h1>Exercises</h1>
  
  </section>

  <section><h1>Notes</h1>
  
    <subsection id="finite_horizon_derivation"><h1>Finite-horizon LQR derivation (general form)</h1>

      <p>For completeness, I've included here the derivation for continuous-time finite-horizon LQR with all of the bells and whistles.</p>

      <p>Consider an time-varying affine (approximation of a) continuous-time
      dynamical system in state-space form: $$\dot{\bx} = {\bf A}(t)\bx + {\bf
      B}(t)\bu + {\bf c}(t),$$ and a running cost function in the general
      quadratic form: \begin{gather*} \ell(t, \bx,\bu) = \begin{bmatrix} \bx \\
      1 \end{bmatrix}^T {\bf Q}(t) \begin{bmatrix} \bx \\ 1 \end{bmatrix} +
      \begin{bmatrix} \bu \\ 1 \end{bmatrix}^T {\bf R}(t) \begin{bmatrix} \bu \\
      1 \end{bmatrix} + 2\bx^T{\bf N}(t)\bu, \\ \forall t\in[t_0, t_f], \quad
      \bQ(t) = \begin{bmatrix} \bQ_{xx}(t) & \bq_x(t) \\ \bq_x^T(t) & q_0(t)
      \end{bmatrix}, \bQ_{xx}(t) \succeq 0, \quad \bR(t) = \begin{bmatrix}
      \bR_{uu}(t) & {\bf r}_u(t) \\ {\bf r}_u^T(t) & r_0(t) \end{bmatrix},
      \bR_{uu}(t) \succ 0.\end{gather*}  Observe that our LQR "optimal tracking"
      derivation fits in this form, as we can always write $$(\bx - \bx_d(t))^T
      \bQ_t (\bx - \bx_d(t)) + (\bu - \bu_d(t))^T \bR_t (\bu - \bu_d(t)) + 2
      (\bx - \bx_d(t))^T {\bf N}_t (\bu - \bu_d(t)),$$ by taking \begin{gather*}
      \bQ_{xx} = \bQ_t,\quad \bq_x = -\bQ_t \bx_d - {\bf N}_t\bu_d, \quad q_0 =
      \bx_d^T \bQ_t \bx_d + 2 \bx_d^T {\bf N}_t \bu_d, \\ \bR_{uu} = \bR_t,
      \quad {\bf r}_u = -\bR_t \bu_d - {\bf N}_t^T \bx_d, \quad r_0 = \bu_d^T
      \bR_t \bu_d, \quad {\bf N} = {\bf N}_t.\end{gather*} Of course, we can
      also add a quadratic final cost with $\bQ_f$. Let's search for a positive
      quadratic, time-varying cost-to-go function of the form: \begin{gather*}
      J(t, \bx)=\begin{bmatrix} \bx \\ 1 \end{bmatrix}^T {\bf S}(t)
      \begin{bmatrix} \bx \\ 1 \end{bmatrix}, \quad {\bf S}(t) = \begin{bmatrix}
      {\bf S}_{xx}(t) & {\bf s}_x(t) \\ {\bf s}_x^T(t) & s_0(t) \end{bmatrix},
      {\bf S}_{xx}(t) \succ 0, \\ \frac{\partial J}{\partial \bx} = 2\bx^T{\bf
      S}_{xx} + 2{\bf s}_x^T, \quad \frac{\partial J}{\partial t} =
      \begin{bmatrix} \bx \\ 1 \end{bmatrix}^T\dot{\bf S} \begin{bmatrix} \bx \\
      1 \end{bmatrix}. \end{gather*} Writing out the HJB: \begin{gather*}
      \min_\bu \left[\ell(\bx,\bu) + \frac{\partial J}{\partial \bx} \left[{\bf
      A}(t)\bx + {\bf B}(t)\bu + {\bf c}(t) \right] + \frac{\partial J}{\partial
      t} \right] = 0, \end{gather*} we can find the minimizing $\bu$ with
      \begin{gather*} \frac{\partial}{\partial \bu} = 2\bu^T{\bf R}_{uu} + 2{\bf
      r}_u^T + 2\bx^T{\bf N} + (2\bx^T{\bf S}_{xx} + 2{\bf s}_x^T){\bf B} = 0 \\
      \bu^* = -{\bf R}_{uu}^{-1} \begin{bmatrix} {\bf N} + {\bf S}_{xx} \bB \\
      {\bf r}^T_{u} + {\bf s}^T_{x}\bB \end{bmatrix}^T \begin{bmatrix} \bx \\ 1
      \end{bmatrix} = -{\bf K}(t) \begin{bmatrix} \bx \\ 1 \end{bmatrix} = -{\bf
      K}_x(t) \bx - {\bf k}_0(t). \end{gather*} Inserting this back into the HJB gives
      us the updated Riccati differential equation. Since this must hold for all
      $\bx$, we can collect the quadratic, linear, and offset terms and set them
      each individually equal to zero, yielding: \begin{align*} -\dot{\bf
      S}_{xx} =& \bQ_{xx} - ({\bf N} + {\bf S}_{xx} \bB){\bf R}_{uu}^{-1}({\bf
      N} + {\bf S}_{xx} \bB)^T + {\bf S}_{xx}{\bf A} + {\bf A}^T{\bf S}_{xx}, \\
      -\dot{\bf s}_x =& \bq_x - ({\bf N} + {\bf S}_{xx} \bB){\bf R}_{uu}^{-1}
      ({\bf r}_u + \bB^T {\bf s}_x ) + {\bf A}^T{\bf s}_x + {\bf S}_{xx}{\bf c},
      \\ -\dot{s}_0 =& q_0 + r_0 - ({\bf r}_u + \bB^T {\bf s}_x)^T{\bf
      R}_{uu}^{-1} ({\bf r}_u + \bB^T {\bf s}_x) + 2{\bf s}_x^T {\bf c},
      \end{align*} with the final conditions ${\bf S}(t_f) = {\bf Q}_f.$
      </p>

      <todo>Provide the sqrt formulation of the S1 equation here</todo>

      <p>In the discrete-time version, we have... </p>

      <p>Phew!  Numerical solutions to these equations can be obtained by
      calling the <a
      href="https://drake.mit.edu/doxygen_cxx/group__control.html#ga58307d2135757a498c434e96d7b99853"><code>FiniteHorizonLinearQuadraticRegulator</code></a>
      methods in <drake></drake>.  May you never have to type them in and unit
      test them yourself.</p>

    </subsection>

  </section>

</chapter>
<!-- EVERYTHING BELOW THIS LINE IS OVERWRITTEN BY THE INSTALL SCRIPT -->

<div id="references"><section><h1>References</h1>
<ol>

<li id=Alessio09>
<span class="author">A. Alessio and A. Bemporad</span>, 
<span class="title">"A survey on explicit model predictive control"</span>, 
<span class="publisher">Int. Workshop on Assessment and Future Directions of Nonlinear Model Predictive Control</span> , <span class="year">2009</span>.

</li><br>
<li id=Marcucci17>
<span class="author">Tobia Marcucci and Robin Deits and Marco Gabiccini and Antonio Bicchi and Russ Tedrake</span>, 
<span class="title">"Approximate Hybrid Model Predictive Control for Multi-Contact Push Recovery in Complex Environments"</span>, 
<span class="publisher">Humanoid Robots (Humanoids), 2017 IEEE-RAS 17th International Conference on</span> , <span class="year">2017</span>.
[&nbsp;<a href="http://groups.csail.mit.edu/robotics-center/public_papers/Marcucci17.pdf">link</a>&nbsp;]

</li><br>
<li id=Posa15>
<span class="author">Michael Posa and Scott Kuindersma and Russ Tedrake</span>, 
<span class="title">"Optimization and stabilization of trajectories for constrained dynamical systems"</span>, 
<span class="publisher">Proceedings of the International Conference on Robotics and Automation (ICRA)</span> , pp. 1366-1373, May, <span class="year">2016</span>.
[&nbsp;<a href="http://groups.csail.mit.edu/robotics-center/public_papers/Posa15.pdf">link</a>&nbsp;]

</li><br>
<li id=Lofberg03>
<span class="author">J. Lofberg</span>, 
<span class="title">"Approximations of closed-loop minimax {MPC}"</span>, 
<span class="publisher">42nd {IEEE} {International} {Conference} on {Decision} and {Control} ({IEEE} {Cat}. {No}.03CH37475)</span> , vol. 2, pp. 1438--1442 Vol.2, Dec, <span class="year">2003</span>.

</li><br>
<li id=Sadraddini20>
<span class="author">Sadra Sadraddini and Russ Tedrake</span>, 
<span class="title">"Robust Output Feedback Control with Guaranteed Constraint Satisfaction"</span>, 
<span class="publisher">In the Proceedings of 23rd ACM International Conference on Hybrid Systems: Computation and Control</span> , pp. 12, April, <span class="year">2020</span>.
[&nbsp;<a href="http://groups.csail.mit.edu/robotics-center/public_papers/Sadraddini20.pdf">link</a>&nbsp;]

</li><br>
<li id=Anderson19>
<span class="author">James Anderson and John C. Doyle and Steven Low and Nikolai Matni</span>, 
<span class="title">"System {Level} {Synthesis}"</span>, 
<span class="publisher">arXiv:1904.01634 [cs, math]</span>, apr, <span class="year">2019</span>.

</li><br>
<li id=Wang14>
<span class="author">Yuh-Shyang Wang and Nikolai Matni and John C. Doyle</span>, 
<span class="title">"Localized {LQR} {Optimal} {Control}"</span>, 
<span class="publisher">arXiv:1409.6404 [cs, math]</span>, September, <span class="year">2014</span>.

</li><br>
</ol>
</section><p/>
</div>

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