Event-triggered robust model predictive control of continuous-time nonlinear systems

This paper proposes a novel optimal adaptive event-triggered control algorithm for nonlinear continuous-time systems. The goal is to reduce the controller updates, by sampling the state only when an event is triggered to maintain stability and optimality. The online algorithm is implemented based on an actor/critic neural network structure. A critic neural network is used to approximate the cost and an actor neural network is used to approximate the optimal event-triggered controller. Since in the algorithm proposed there are dynamics that exhibit continuous evolutions described by ordinary differential equations and instantaneous jumps or impulses, we will use an impulsive system approach. A Lyapunov stability proof ensures that the closed-loop system is asymptotically stable. Finally, we illustrate the effectiveness of the proposed solution compared to a time-triggered controller.

Event-triggered optimal adaptive control algorithm for continuous-time nonlinear systems

Cyber-Physical Systems (CPSs) resulting from the interconnection of computational, communication, and control (cyber) devices with physical processes are wide spreading in our society. In several CPS applications it is crucial to minimize the communication burden, while still providing desirable closed-loop control properties. To this effect, a promising approach is to embrace the recently proposed event-triggered control paradigm, in which the transmission times are chosen based on well-defined events, using state information. However, few general event-triggered control methods guarantee closed-loop improvements over traditional periodic transmission strategies. Here, we provide a new class of event-triggered controllers for linear systems which guarantee better quadratic performance than traditional periodic time-triggered control using the same average transmission rate. In particular, our main results explicitly quantify the obtained performance improvements for quadratic average cost problems. The proposed controllers are inspired by rollout ideas in the context of dynamic programming.

/pdf/rollout-event-triggered-control-beyond-periodic-control-2ic8m1nvwt.pdf

Rollout Event-Triggered Control: Beyond Periodic Control Performance

In this paper, we propose a paradigm of control, called a maximum hands-off control. A hands-off control is defined as a control that has a short support per unit time. The maximum hands-off control is the minimum support (or sparsest) per unit time among all controls that achieve control objectives. For finite horizon continuous-time control, we show the equivalence between the maximum hands-off control and $L^{1}$ -optimal control under a uniqueness assumption called normality. This result rationalizes the use of $L^{1}$ optimality in computing a maximum hands-off control. The same result is obtained for discrete-time hands-off control. We also propose an $L^{1}/L^{2}$ -optimal control to obtain a smooth hands-off control. Furthermore, we give a self-triggered feedback control algorithm for linear time-invariant systems, which achieves a given sparsity rate and practical stability in the case of plant disturbances. An example is included to illustrate the effectiveness of the proposed control.

Maximum Hands-Off Control: A Paradigm of Control Effort Minimization

Robust self-triggered min–max model predictive control for discrete-time nonlinear systems

https://heemels.tue.nl/content/papers/BerGom_CONF12a.pdf

J.D.J. Barradas Berglind

Papers

Self-triggered MPC for constrained linear systems and quadratic costs