Sparse and Constrained Stochastic Predictive Control for Networked Systems

This article presents a novel class of control policies for networked control of Lyapunov-stable linear systems with bounded inputs. The control channel is assumed to have i.i.d. Bernoulli packet dropouts and the system is assumed to be affected by additive stochastic noise. Our proposed class of policies is affine in the past dropouts and saturated values of the past disturbances. We further consider a regularization term in a quadratic performance index to promote sparsity in control. We demonstrate how to augment the underlying optimization problem with a constant negative drift constraint to ensure mean-square boundedness of the closed-loop states, yielding a convex quadratic program to be solved periodically online. The states of the closed-loop plant under the receding horizon implementation of the proposed class of policies are mean square bounded for any positive bound on the control and any non-zero probability of successful transmission

Optimal scheduling and control for constrained multi-agent networked control systems

In this paper, we study optimal control and communication schedule co-design for multi-agent networked control systems, with assuming shared parallel communication channels and uncertain constrained linear time-invariant discrete-time systems. To that end, we specify the communication demand for each system using an associated robust control invariant set and reachability analysis. We use these communication demands and invariant sets to formulate tube-based model predictive control and offline/online communication schedule co-design problems. Since the scheduling part includes an infinite dimension integer problem, we propose heuristics to find suboptimal solutions that guarantee robust constraints satisfaction and recursive feasibility. The effectiveness of our approach is illustrated through numerical simulations

Self-Triggered Stochastic MPC for Linear Systems With Disturbances

In this letter, we present a self-triggering mechanism for stochastic model predictive control (SMPC) of discrete-time linear systems subject to probabilistic constraints, where the controller and the plant are connected by a shared communication network. The proposed triggering mechanism requires that only one control input is allowed to be transmitted through the network at each triggering instant which is then applied to the plant for several steps afterward. By doing so, communication is effectively reduced both in terms of frequency and total amount. We establish the theoretical result for recursive feasibility in the light of proper reformulation of constraints on the nominal system trajectories, and also provide stability analysis for the proposed self-triggered SMPC. A numerical example illustrates the efficiency of the proposed scheme in reducing the communication as well as ensuring meeting the probabilistic constraints