Tag Archives: finite differences

Simultaneous perturbation vs finite differences for linear dynamics estimation and control signal optimization

Recently in my posts I’ve been using finite differences to approximate the gradient of loss functions and dynamical systems, with the intention of creating generalizable controllers that can be run on any system without having to calculate out derivatives beforehand. Finite differences is pretty much the most straight-forward way of approximating a gradient that there is: vary each parameter up and down (assuming we’re doing central differencing), one at a time, run it through your function and estimate the parameters effect on the system by calculating the difference between resulting function output. To do this requires 2 samples of the function for each parameter.

But there’s always more than one way to peel an avocado, and another approach that’s been used with much success is the Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm, which was developed by Dr. James Spall (link to overview paper). SPSA is a method of gradient approximation, like finite differences, but, critically, the difference is that it varies all of the parameters at once, rather than one at a time. As a result, you can get an approximation of the gradient with far fewer samples from the system, and also when you don’t have explicit control over your samples (i.e. the ability to vary each parameter one at a time).

Gradient approximation

Given some function \textbf{f} dependent on some set of parameters \textbf{x}, we’re used to finding the gradient of \textbf{f}(\textbf{x}) using FDSA (finite differences stochastic approximation) written in this form:

\textbf{f}_x = \frac{\textbf{f}(\textbf{x} + \Delta \textbf{x}) - \textbf{f}(\textbf{x} - \Delta \textbf{x})}{2 \Delta \textbf{x}} = \Delta x^{-1} \frac{\textbf{f}(\textbf{x} + \Delta \textbf{x}) - \textbf{f}(\textbf{x} - \Delta \textbf{x})}{2},

where \Delta\textbf{x} is a perturbation to the parameter set, and the subscript on the left-hand side denotes the derivative of \textbf{f}(\textbf{x}) with respect to \textbf{x}.

And that’s how we’ve calculated it before, estimating the gradient of a single parameter at a time. But, we can rewrite this for a set perturbations \Delta \textbf{X} = [\Delta\textbf{x}_0, ... , \Delta\textbf{x}_N]^T:

\textbf{f}_\textbf{x} = \Delta \textbf{X}^{-1} \textbf{F},


\textbf{F} = [\frac{\textbf{f}(\textbf{x} + \Delta \textbf{x}_0) - \textbf{f}(\textbf{x} - \Delta \textbf{x}_0)}{2}, ... , \frac{\textbf{f}(\textbf{x} + \Delta \textbf{x}_0) - \textbf{f}(\textbf{x}_N - \Delta \textbf{x}_N)}{2}]^T,

which works as long as \Delta\textbf{X} is square. When it’s not square (i.e. we have don’t have the same number of samples as we have parameters), we run into problems, because we can’t calculate \Delta\textbf{X}^{-1} directly. To address this, let’s take a step back and then work forward again to get a more general form that works for non-square \Delta \textbf{X} too.

By rewriting the above, and getting rid of the inverse by moving \Delta\textbf{x} back to the other side, we have:

\Delta\textbf{X} \; \textbf{f}_\textbf{x} = \textbf{F}

Now, the standard trick to move a matrix that’s not square is to just make it square by multiplying it by its transpose to get a square matrix, and then the whole thing by the inverse:

\Delta\textbf{X}^T \Delta\textbf{X} \; \textbf{f}_\textbf{x} = \Delta\textbf{X}^T \textbf{F}

(\Delta\textbf{X}^T \Delta\textbf{X})^{-1} (\Delta\textbf{X}^T \Delta\textbf{X}) \textbf{f}_\textbf{x} = (\Delta\textbf{X}^T \Delta\textbf{X})^{-1} \Delta\textbf{X}^T \textbf{F}

\textbf{f}_\textbf{x} = (\Delta\textbf{X}^T \Delta\textbf{X})^{-1} \Delta\textbf{X}^T \textbf{F}

Alright! Now we’re comfortable with this characterization of gradient approximation using a form that works with non-square perturbation matrices.

Again, in FDSA, we only vary one parameter at a time. This means that there will only ever be one non-zero entry per row of \Delta \textbf{X}. By contrast, in SPSA, we vary multiple parameters, and so rows of \Delta\textbf{X} will be just chalk full of non-zero entries.

Gradient approximation to estimate \textbf{f}_\textbf{x} and \textbf{f}_\textbf{u} for LQR control

This paper, by Drs. Jan Peters and Stepfan Schaal, is where I first stumbled across the above formulation of gradient approximation and read about SPSA (side note: I really recommend reading instead the Scholarpedia article on policy gradients, because it has fixes to a couple notation mistakes from the paper). Reading about this I thought, oh interesting, an alternative to FDSA for gradient approximation, let’s see how it well it does replacing FDSA in a linear quadratic regulator (LQR).

Implementing this was pretty simple. Just had to modify the calc_derivs function, which I use to estimate the derivative of the arm with respect to the state and control signal, in my LQR controller code by changing from standard finite differences to simultaneous perturbation:

def calc_derivs(self, x, u):
    """" calculate gradient of plant dynamics using Simultaneous
    Perturbation Stochastic Approximation (SPSA). Implemented
    based on (Peters & Schaal, 2008).

    x np.array: the state of the system
    u np.array: the control signal
    # Initialization and coefficient selection
    num_iters = 20

    eps = 1e-4
    delta_K = None
    delta_J = None
    for ii in range(num_iters):
        # Generation of simultaneous perturbation vector
        # choose each component from a Bernoulli +-1 distribution
        # with probability of .5 for each +-1 outcome.
        delta_k = np.random.choice([-1,1],
                                   size=len(x) + len(u),
                                   p=[.5, .5])
        # Function evaluations
        inc_x = np.copy(x) + eps * delta_k[:len(x)]
        inc_u = np.copy(u) + eps * delta_k[len(x):]
        state_inc = self.plant_dynamics(inc_x, inc_u)
        dec_x = np.copy(x) - eps * delta_k[:len(x)]
        dec_u = np.copy(u) - eps * delta_k[len(x):]
        state_dec = self.plant_dynamics(dec_x, dec_u)

        delta_j = ((state_inc - state_dec) /
                        (2.0 * eps)).reshape(-1)

        # Track delta_k and delta_j
        delta_K = delta_k if delta_K is None else \
            np.vstack([delta_K, delta_k])
        delta_J =  delta_j if delta_J is None else \
            np.vstack([delta_J, delta_j])

    f_xu = np.dot(np.linalg.pinv(np.dot(delta_K.T, delta_K)),
            np.dot(delta_K.T, delta_J))
    f_x = f_xu[:len(x)]
    f_u = f_xu[len(x):]

    return f_x.T , f_u.T

A couple notes about the above code. First, you’ll notice that the f_x and f_b matrices are both calculated at the same time. That’s pretty slick! And that calculation for f_xu is just a straight implementation of the matrix form of gradient approximation, where I’ve arranged things so that f_x is in the top part and f_u is in the lower part.

The second thing is that the perturbation vector delta_k is generated from a Bernoulli distribution. The reason behind this is that we want to have a bunch of different samples that pretty reasonably spread the state space and move all the parameters independently. Making each perturbation some distance times -1 or 1 is an easy way to achieve this.

Thirdly, there’s the num_iters variable. This is a very important variable, as it dictates how many random samples of our system we take before we estimate the gradient. I’ve found that to get this to work for both the 2-link arm and the more complex 3-link arm, it needs to be at least 20. Or else things explode and die horribly. Just…horribly.

OK let’s look at the results:

The first thing to notice is that I’ve finally discovered the Seaborn plotting package. The second is that SPSA does as well as FDSA.

You may ask: Is there any difference? Well, if we time these functions, on my lil’ laptop, for the 2-link arm it takes SPSA approximately 2.0ms, but it takes FDSA only 0.8ms. So for the same performance the SPSA is taking almost 3 times as long to run. Why? This boils down to how many times the system dynamics need to be sampled by each algorithm to get a good approximation of the gradient. For a 2-link arm, FDSA has 6 parameters (\textbf{q}, \dot{\textbf{q}}, and \textbf{u}) that it needs to sample twice (we’re doing central differencing), for a total of 12 samples. And as I mentioned above, the SPSA algorithm needs 20 samples to be stable.

For the 3-link arm, SPSA took about 3.1ms on average and FDSA (which must now perform 18 samples of the dynamics) still only 2.1ms. So number of samples isn’t the only cause of time difference between these two algorithms. SPSA needs to perform that a few more matrix operations, including a matrix inverse, which is expensive, while FDSA can calculate the gradient of each parameter individually, which is much less expensive.

OK so SPSA not really impressive here. BUT! As I discovered, there are other means of employing SPSA.

Gradient approximation to optimize the control signal directly

In the previous set up we were using SPSA to estimate the gradient of the system under control, and then we used that gradient to calculate a control signal that minimized the loss function (as specified inside the LQR). This is one way to use gradient approximation methods. Another way to use these methods is approximate the gradient of the loss function directly, and use that information to iteratively calculate a control signal that minimizes the loss function. This second application is the primary use of the SPSA algorithm, and is what’s described by Dr. Spall in his overview paper.

In this application, the algorithm works like this:

  1. start with initial input to system
  2. perturb input and simulate results
  3. observe loss function and calculate gradient
  4. update input to system
  5. repeat to convergence

Because in this approach we’re iteratively optimizing the input using our gradient estimation, having a noisy estimate is no longer a death sentence, as it was in the LQR. If we update our input to the system with several noisy gradient estimates the noise will essentially just cancel itself out. This means that SPSA now has a powerful advantage over FDSA: Since in SPSA we vary all parameters at once, only 2 samples of the loss function are used to estimate the gradient, regardless of the number of parameters. In contrast, FDSA needs to sample the loss function twice for every input parameter. Here’s a picture from (Spall, 1998) that shows the two running against each other to optimize a 2D problem:

This gets across that even though SPSA bounces around more, they both reach the solution in the same number of steps. And, in general, this is the case, as Dr. Spall talks about in the paper. There’s also a couple more details of the algorithm, so let’s look at it in detail. Here’s the code, which is just a straight translation into Python out of the description in Dr. Spall’s paper:

# Step 1: Initialization and coefficient selection
max_iters = 5
converge_thresh = 1e-5

alpha = 0.602 # from (Spall, 1998)
gamma = 0.101
a = .101 # found empirically using HyperOpt
A = .193
c = .0277

delta_K = None
delta_J = None
u = np.copy(self.u) if self.u is not None \
        else np.zeros(self.arm.DOF)
for k in range(max_iters):
    ak = a / (A + k + 1)**alpha
    ck = c / (k + 1)**gamma

    # Step 2: Generation of simultaneous perturbation vector
    # choose each component from a bernoulli +-1 distribution with
    # probability of .5 for each +-1 outcome.
    delta_k = np.random.choice([-1,1], size=arm.DOF, p=[.5, .5])

    # Step 3: Function evaluations
    inc_u = np.copy(u) + ck * delta_k
    cost_inc = self.cost(np.copy(state), inc_u)
    dec_u = np.copy(u) - ck * delta_k
    cost_dec = self.cost(np.copy(state), dec_u)

    # Step 4: Gradient approximation
    gk = np.dot((cost_inc - cost_dec) / (2.0*ck), delta_k)

    # Step 5: Update u estimate
    old_u = np.copy(u)
    u -= ak * gk

    # Step 6: Check for convergence
    if np.sum(abs(u - old_u)) < converge_thresh:

The main as-of-yet-unexplained parts of this code are the alpha, gamma, a, A, and c variables. What’s their deal?

Looking inside the loop, we can see that ck controls the magnitude of our perturbations. Looking a little further down, ak is just the learning rate. And all of those other parameters are just involved in shaping the trajectories that ak and ck follow through iterations, which is a path towards zero. So the first steps and perturbations are the biggest, and each successively becomes smaller as the iteration count increases.

There are a few heuristics that Dr. Spall goes over, but there aren’t any hard and fast rules for setting a, A, and c. Here, I just used HyperOpt to find some values that worked pretty well for this particular problem.

The FDSA version of this is also very straight-forward:

# Step 1: Initialization and coefficient selection
max_iters = 10
converge_thresh = 1e-5

eps = 1e-4
u = np.copy(self.u) if self.u is not None \
        else np.zeros(self.arm.DOF)
for k in range(max_iters):

    gk = np.zeros(u.shape)
    for ii in range(gk.shape[0]):
        # Step 2: Generate perturbations one parameter at a time
        inc_u = np.copy(u)
        inc_u[ii] += eps
        dec_u = np.copy(u)
        dec_u -= eps

        # Step 3: Function evaluation
        cost_inc = self.cost(np.copy(state), inc_u)
        cost_dec = self.cost(np.copy(state), dec_u)

        # Step 4: Gradient approximation
        gk[ii] = (cost_inc - cost_dec) / (2.0 * eps)

    old_u = np.copy(u)
    # Step 5: Update u estimate
    u -= 1e-5 * gk

    # Step 6: Check for convergence
    if np.sum(abs(u - old_u)) < converge_thresh:

You’ll notice that in both the SPSA and FDSA code we’re no longer sampling plant_dynamics, we’re instead sampling cost, a loss function I defined. From just my experience playing around with these algorithms a bit, getting the loss function to be appropriate and give the desired behaviour is definitely a bit of an art. It feels like much more of an art than in other controllers I’ve coded, but that could just be me.

The cost function that I’m using is pretty much the first thing you’d think of. It penalizes distance to target and having non-zero velocity. Getting the weighting between distance to target and velocity set up so that the arm moves to the target but also doesn’t overshoot definitely took a bit of trial and error, er, I mean empirical analysis. Here’s the cost function that I found worked pretty well, note that I had to make special cases for the different arms:

 def cost(self, x, u): 
        dt = .1 if self.arm.DOF == 3 else .01
        next_x = self.plant_dynamics(x, u, dt=dt)
        vel_gain = 100 if self.arm.DOF == 3 else 10
        return (np.sqrt(np.sum((self.arm.x - self.target)**2)) * 1000 \
                + np.sum((next_x[self.arm.DOF:])**2) * vel_gain)

So that’s all the code, let’s look at the results!

For these results, I used a max of 10 iterations for optimizing the control signal. I was definitely surprised by the quality of the results, especially for the 3-link arm, compared to the results generated by a standard LQR controller. Although I need to note, again, that it was a fair bit of me playing around with the exact cost function to get these results. Lots of empirical analysis.

The two controllers generate results that are identical to visual inspection. However, especially in the 3-link arm, the time required to run the FDSA was significantly longer than the SPSA controller. It took approximately 140ms for the SPSA controller to run a single loop, but took FDSA on average 700ms for a single loop of calculating the control signal. Almost 5 times as long! For the same results! In directly optimizing the control signal, SPSA gets a big win over standard FDSA. So, if you’re looking to directly optimize over a loss function, SPSA is probably the way you want to go.


First off, I thought it was really neat to directly apply gradient approximation methods to optimizing the control signal. It’s something I haven’t tried before, but definitely makes sense, and can generate some really nice results when tuned properly. Automating the tuning is definitely I’ll be discussing in future posts, because doing it by hand takes a long time and is annoying.

In the LQR, the gradient approximation was best done by the FDSA. I think the main reasons for this is that in solving for the control signal the LQR algorithm uses matrix inverses, and any errors in the linear approximations to the dynamics are going to be amplified quite a bit. If I did anything less than 10-15 iterations (20 for the 3-link arm) in the SPSA approximation then things exploded. Also, here the SPSA algorithm required a matrix inverse, where the FDSA didn’t. This is because we only varied one parameter at a time in FDSA, and the effects of changing each was isolated. In the SPSA case, we had to consider the changes across all the variables and the resulting effects all at once, essentially noting which variables changed by how much and the changes in each case, and averaging. Here, even with the more complex 3-link arm, FDSA was faster, so I’m going to stick with it in my LQR and iLQR implementations.

In the direct control signal optimization SPSA beat the pants off of FDSA. It was almost 5 times faster for control of the 3-link arm. This was, again, because in this case we could use noisy samples of the gradient of the loss function and relied on noise to cancel itself out as we iterated. So we only needed 2 samples of the loss function in SPSA, where in FDSA we needed 2*num_parameters. And although this generated pretty good results I would definitely be hesitant against using this for any more complicated systems, because tuning that cost function to get out a good trajectory was a pain. If you’re interested in playing around with this, you can check out the code for the gradient controllers up on my GitHub.

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Linear-Quadratic Regulation for non-linear systems using finite differences

One of the standard controllers in basic control theory is the linear-quadratic regulator (LQR). There is a finite-horizon case (where you have a limited amount of time), and an infinite-horizon case (where you don’t); in this post, for simplicity, we’re only going to be dealing with the infinite-horizon case.

The LQR is designed to handle a very specific kind of problem. First, it assumes you are controlling a system with linear dynamics, which means you can express them as

\dot{\textbf{x}} = \textbf{A}\textbf{x} + \textbf{B}\textbf{u},

where \textbf{x} and \dot{\textbf{x}} are the state and its time derivative, \textbf{u} is the input, and \textbf{A} and \textbf{B} capture the effects of the state and input on the derivative. And second, it assumes that the cost function, denoted J, is a quadratic of the form

J = \int_0^{\infty} \left( (\textbf{x} - \textbf{x}^*)^T \textbf{Q} (\textbf{x} - \textbf{x}^*) + \textbf{u}^T \textbf{R} \textbf{u} \right) dt

where \textbf{x}^* is the target state, and \textbf{Q} = \textbf{Q}^T \geq 0 and \textbf{R} = \textbf{R}^T \geq 0 are weights on the cost of not being at the target state and applying a control signal. The higher \textbf{Q} is, the more important it is to get to the target state asap, the higher \textbf{R} is, the more important it is to keep the control signal small as you go to the target state.

The goal of the LQR is to calculate a feedback gain matrix \textbf{K} such that

\textbf{u} = -\textbf{K} \textbf{x},

drives the system to the target. When the system is a linear system with a quadratic cost function, this can be done optimally. There is lots of discussion elsewhere about LQRs and their derivation, so I’m not going to go into that with this post. Instead, I’m going to talk about applying LQRs to non-linear systems, and using finite differences to do it, which works when you have a readily accessible simulation of the system on hand. The fun part is that by using finite differences you can get this to work without working out the dynamics equations yourself.

Using LQRs on non-linear systems

As you may have noticed, non-linear systems violate the first assumption of a linear quadratic regulator; that the system is linear. That doesn’t mean that we can’t apply it, it just means that it’s not going to be optimal. How poorly the LQR will perform depends on a few things, two important factors being how non-linear the system dynamics actually are, and how often you’re able to update the feedback gain matrix \textbf{K}. To apply LQR to non-linear systems we’re just going to close our eyes and pretend that the system dynamics are linear, i.e. they fit the form

\dot{\textbf{x}} = \textbf{A}\textbf{x} + \textbf{B}\textbf{u}.

We’ll do this by approximating the actual dynamics of the system linearly. We’ll then solve for our gain value \textbf{K}, generate our control signal for this timestep, and then re-approximate the dynamics again at the next time step and solve for \textbf{K} from the new state. The more non-linear the system dynamics are, the less appropriate \textbf{K} will be for generating our control signal \textbf{u} as we move away from the state \textbf{K} was calculated in; this is why update time of the LQR can become an important factor.

Using finite-differences to approximate system dynamics

An important question, then, is how do we find this system approximation? How can we calculate the \textbf{A} and \textbf{B} matrices that we then use to solve for \textbf{K}? If we know the dynamics of the system to be

\dot{\textbf{x}} = f(\textbf{x}, \textbf{u}),

then we can calculate

\textbf{A} = \frac{\partial f(\textbf{x}, \textbf{u})}{\partial \textbf{x}}, \;\;\;\; \textbf{B} = \frac{\partial f(\textbf{x}, \textbf{u})}{\partial \textbf{u}}.

If you’re going to try this for the 3-link arm, though, get out Mathematica. Do not try this by hand. If you disregard my warning and foolhardily attempt such a derivation you will regret, repent, and then appeal to Wolfram Alpha for salvation. These equations quickly become terrible and long even for seemingly not-so-complicated systems.

There are a few ways to skirt this. Here we’re going to assume that the system under control is a simulation, or that we at least have access to an accurate model, and use the finite differences method to compute these values. The idea behind finite differences is to approximate the rate of change of the function f at the point x by sampling f near x and using the difference to calculate \dot{f}(x). Here’s a picture for a 1D system:

So here, our current state x is the blue dot, and the red dots represent the sample points x + \Delta x and x - \Delta x. We can then calculate

\dot{f}(x) \approx \frac{f(x+\Delta x) - f(x-\Delta x)}{2\Delta x},

and you can see the actual rate of change of f at x plotted in the blue dashed line, and the approximated rate of change calculated using finite differences plotted in the red dashed line. We can also see that the approximated derivative is only accurate near x (the blue dot).

Back in our multi-dimensional system, to use finite differences to calculate the derivative with respect to the state and the input we’re going to vary each of the dimensions of the state and input by some small amount one at a time, calculating the effects of each one by one. Here’s a chunk of pseudo-code to hopefully clarify this idea:

eps = 1e-5
A = np.zeros((len(current_state), len(current_state))
for ii in range(len(current_state)):
    x = current_state.copy()
    x[ii] += eps
    x_inc = simulate_system(state=x, input=control_signal)
    x = current_state.copy()
    x[ii] -= eps
    x_dec = simulate_system(state=x, input=control_signal)
    A[:,ii] = (x_inc - x_dec) / (2 * eps)

B = np.zeros((len(current_state), len(control_signal))
for ii in range(len(control_signal)):
    u = control_signal.copy()
    u[ii] += eps
    x_inc = simulate_system(state=current_state, input=u)
    u = control_signal.copy()
    u[ii] -= eps
    x_dec = simulate_system(state=current_state, input=u)
    B[:,ii] = (x_inc - x_dec) / (2 * eps)

Now we’re able to generate our \textbf{A} and \textbf{B} matrices we have everything we need to solve for our feedback gain matrix \textbf{K}! Which is great.

Note on using finite differences in continuous vs discrete setup

Something that’s important to straighten out too is what exactly is returned by the simulate_system function in the code above. In the continuous case, your system is captured as

\dot{\textbf{x}} = \textbf{A}\textbf{x} + \textbf{B}\textbf{u},,

where in the discrete case your system is defined

\textbf{x}(t+1) = \textbf{A}\textbf{x}(t) + \textbf{B}\textbf{u}(t).

If you are calculating your feedback gain matrix \textbf{K} using the continuous solution to the algebraic Riccati equation, then you need to be returning \dot{\textbf{x}}(t). If you’re solving for \textbf{K} using the discrete solution to the algebraic Riccati equation you need to return \textbf{x}(t+1). This was just something that I came across as I was coding and so I wanted to mention it here in case anyone else stumbled across it!

Applying LQR to 2 and 3 link arm control

Alright! Let’s have a look at how the LQR does controlling non-linear systems. Below we have the control of a 2-link arm compared to a 3-link arm, and you can see the control of the 2-link arm is better. This is a direct result of the dynamics of a 3-link arm being significantly more complex.

2linkarm 3linkarm

Note on controlling at different timesteps

When I was first testing the LQR controller I expected the effects of different control update times to be a lot more significant than it was. As it turns out, for controlling a 3-link arm, there’s not really a visible difference in a controller that is updating every .01 seconds vs every .001 seconds vs every .0001 seconds. Let’s have a look:

3link.01 3link.001 3link.0001

Can’t even tell, eh? Fun fact, the simulation took 1 minute 30 seconds at .01 seconds time step and 45 minutes at .0001 seconds time step. The left-most animation is the .01 seconds and the right-most the .0001 seconds. But why is there seemingly so little difference? Well, this boils down to the dynamics of the 3-link arm changing actually pretty slowly. Below I’ve plotted just a few of the elements from the \textbf{A}, \textbf{B}, and \textbf{K} matrices over .5 seconds of simulation time:

A matrixB matrixK matrix

So, there are some obvious points where sampling the dynamics at a .01 time step is noticeably less accurate, but all in all there’s not a huuuggge difference between sampling at .01 and .0001 seconds. If you’re just watching the end-effector path it’s really not very noticeable. You can see how the elements of \textbf{A} and \textbf{B} are changing fairly slowly; this means that \textbf{K} is going to be an effective feedback gain for a fair chunk of time. And the computational savings you get by sampling the dynamics and regenerating \textbf{K} every .01 seconds instead of every .0001 seconds are pretty big. This was just another thing that I came across when playing around with the LQR, the take away being don’t just assume you need to update your system crazy often. You might get very comparable performance for much less computational cost.


All in all, the LQR controller is pretty neat! It’s really simple to set up, and generic. We don’t need any specific information about the system dynamics, like we do for effective operational space control (OSC). When we estimate the dynamics with finite differences, all need is a decent system model that we can sample. Again, the more non-linear the system, of course, the less effective a LQR will be. If you’re interested in playing around with one, or generating the figures that I show above, the code is all up and running on my Github for you to explore.

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