Due to the ubiquitous existence of external disturbances, the design of distributed control algorithms with robustness is an urgent demand for multi-agent system applications. This technical note investigates the robust distributed model predictive control (MPC) problem for a group of nonlinear agents (subsystems) subject to control input constraints and external disturbances. A robustness constraint is proposed to handle the external disturbances. Based on this, a novel robust distributed MPC scheme is designed for the overall agent system. Furthermore, the feasibility of the robust distributed MPC scheme and the robust stability of the overall agent system are analyzed, respectively. The conditions under which the proposed MPC is feasible and the overall agent system is robustly stabilized are established. Finally, the application of the robust distributed MPC to a group of cart-damper-spring systems verifies the theoretical results.

The cooperative control of marine vehicles finds wide applications in many marine missions and tasks. This paper investigates the receding horizon formation tracking control problem of a fleet of underactuated autonomous underwater vehicles (AUVs), in which the follower AUVs are required to track the leader with prescribed formation pattern, and the control inputs of the follower AUVs are subject to practical constraints. An auxiliary stabilizable control law is first designed, based on which a novel optimization problem is proposed and a new receding horizon control (RHC) algorithm is designed to generate control inputs. The theoretical feasibility conditions of the RHC-based tracking algorithm and the stability conditions of the closed-loop systems are provided. Simulation studies are conducted, and the simulation results verify the effectiveness of the proposed algorithm and theoretical results.

The event-triggered control is a promising solution to cyber-physical systems, such as networked control systems, multiagent systems, and large-scale intelligent systems. In this paper, we propose an event-triggered model predictive control (MPC) scheme for constrained continuous-time nonlinear systems with bounded disturbances. First, a time-varying tightened state constraint is computed to achieve robust constraint satisfaction, and an event-triggered scheduling strategy is designed in the framework of dual-mode MPC. Second, the sufficient conditions for ensuring feasibility and closed-loop robust stability are developed, respectively. We show that robust stability can be ensured and communication load can be reduced with the proposed MPC algorithm. Finally, numerical simulations and comparison studies are performed to verify the theoretical results.

This paper investigates the predictive control scheme and the associated stability issue for the constrained nonlinear networked control systems (NCSs), where both the sensor-to-controller packet dropout and the controller-to-actuator packet dropout are considered simultaneously. The model predictive control based framework is proposed to compensate for the two-channel packet dropouts. This framework consists of two main aspects: 1) to design the control packets by solving a constrained optimization problem and 2) to synthesize an efficient packet transmission and compensation mechanism based on the Transmission Control Protocol. To study the stability of the resultant nonlinear NCS, we propose a novel Lyapunov function, based on which the conditions for ensuring the regional input-to-state practical stability are developed. Finally, the proposed control strategy is applied to a spring-and-cart system to demonstrate the applicability and effectiveness.

This note studies the self-triggered MPC problem for constrained discrete-time nonlinear systems. A novel self-triggered MPC algorithm enabling the codesign of triggering and control is proposed, in which the communication cost is explicitly considered in the total cost function, and both the control and triggering intervals can be optimally designed. For constrained nonlinear systems, the theoretical conditions on ensuring feasibility and closed-loop stability are developed, and the achieved control and communication performance is proved to be guaranteed. In particular, the parameter design approaches for satisfying feasibility and stability conditions are further presented in the case of quadratic cost functions. Furthermore, stronger results are provided for linear case. Finally, the effectiveness and advantages are verified via simulation and comparison studies.