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Enforce Barrier Certificate Constraints for PID Controllers

This example shows how to enforce barrier certificate constraints for a PID controller application using the Barrier Certificate Enforcement block.

Overview

For this example, the plant dynamics are described by the following equations [1].

x˙1=-x1+(x12+1)u1

x˙2=-x2+(x22+1)u2

The goal for the plant is to track desired trajectories, defined as:

θ˙=0.1π

x˙1d=-rcos(θ)

x˙2d=rsin(θ)

For an example that applies a predicted constraint function to the same PID control application, see Enforce Constraints for PID Controllers.

Configure model parameters and initial conditions.

r = 1.5;   % Radius for desired trajectory
Ts = 0.1;  % Sample time
Tf = 22;   % Duration
x0_1 = -r; % Initial condition for x1
x0_2 = 0;  % Initial condition for x2

Design PID Controllers

Before applying constraints, design PID controllers for tracking the reference trajectories. The barrierCertificatePID model contains two PID controllers with tuned gains. For more information on tuning PID controllers in Simulink® models, see Introduction to Model-Based PID Tuning in Simulink.

Open the Simulink model.

constrained = 0; % Disable barrier certificate constraint enforcement
mdl = 'barrierCertificatePID';
open_system(mdl)

Simulate the PID controllers and plot their tracking performance.

% Simulate the model.
out = sim(mdl);

% Extract trajectories.
logData = out.logsout;
x1_traj = zeros(size(out.tout));
x2_traj = zeros(size(out.tout));
for ct = 1:size(out.tout,1)
    x1_traj(ct) = logData{3}.Values.Data(:,:,ct);
    x2_traj(ct) = logData{4}.Values.Data(:,:,ct);
end

x1_des = logData{1}.Values.Data;
x2_des = logData{2}.Values.Data;

% Plot trajectories.
figure('Name','Tracking with Constraint');
plot(x1_des,x2_des,'r')
xlabel('x1')
ylabel('x2')
hold on
plot(x1_traj,x2_traj,'b:','LineWidth',2)
hold on
plot(x1_traj(1),x2_traj(1),'g*')
hold on
plot(x1_traj(end),x2_traj(end),'go')
legend('Desired','Trajectory','Start','End','Location','best')

Figure Tracking with Constraint contains an axes object. The axes object contains 4 objects of type line. These objects represent Desired, Trajectory, Start, End.

Barrier Certificates

For this example, you enforce barrier certificate constraints for the same plant model and PID controllers.

The feasible region for the plant is given by {x:x11,x21}. Therefore, the barrier certificates are given by h(x)=[1-x11-x2]0.

The partial derivative of h(x) over x is given by q(x)=[-100-1].

The Barrier Certificate Enforcement block accepts plant dynamics in the form x˙=f(x)+g(x)u. For this application, f(x)=[-x1-x2] and g(x)=[x12+100x22+1].

Simulate PID Controller with Barrier Certificate Constraint

To view the constraint implementation, open the Constraint > Constrained subsystem.

Enable barrier certificate constraint enforcement.

constrained = 1;

Run the model and plot the simulation results. The plot shows that the plant states are less than one.

% Simulate the model.
out = sim(mdl);

% Extract trajectories.
logData = out.logsout;
x1_traj = zeros(size(out.tout));
x2_traj = zeros(size(out.tout));
for ct = 1:size(out.tout,1)
    x1_traj(ct) = logData{3}.Values.Data(:,:,ct);
    x2_traj(ct) = logData{4}.Values.Data(:,:,ct);
end

x1_des = logData{1}.Values.Data;
x2_des = logData{2}.Values.Data;

% Plot trajectories.
figure('Name','Tracking with Constraint');
plot(x1_des,x2_des,'r')
xlabel('x1')
ylabel('x2')
hold on
plot(x1_traj,x2_traj,'b:','LineWidth',2)
hold on
plot(x1_traj(1),x2_traj(1),'g*')
hold on
plot(x1_traj(end),x2_traj(end),'go')
legend('Desired','Trajectory','Start','End','Location','best')

Figure Tracking with Constraint contains an axes object. The axes object contains 4 objects of type line. These objects represent Desired, Trajectory, Start, End.

The Barrier Certificate Enforcement block successfully constrains the control actions such that the plant states remain less than one.

Close the model.

bdclose(mdl)

References

[1] Robey, Alexander, Haimin Hu, Lars Lindemann, Hanwen Zhang, Dimos V. Dimarogonas, Stephen Tu, and Nikolai Matni. "Learning Control Barrier Functions from Expert Demonstrations." Preprint, submitted April 7, 2020. https://arxiv.org/abs/2004.03315

See Also

Related Topics