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Model predictive control (MPC) is capable to deal with multiconstraint systems in real control processes; however, the heavy computation makes it difficult to implement. In this paper, a dual-mode control strategy based on event-triggered MPC (ETMPC) and state-feedback control for continuous linear time-invariant systems including control input constraints and bounded disturbances is developed. First, the deviation between the actual state trajectory and the optimal state trajectory is computed to set an event-triggered mechanism and reduce the computational load of MPC. Next, the dual-mode control strategy is designed to stabilize the system. Both recursive feasibility and stability of the strategy are guaranteed by constructing a feasible control sequence and deducing the relationship of parameters, especially the inter-event time and the upper bound of the disturbances. Finally, the theoretical results are supported by numerical simulation. In addition, the effects of the parameters are discussed by simulation, which gives guidance to balance computational load and control performance. KEYWORDSbounded disturbances, continuous LTI system, event-triggered, input constraints, model predictive control 1216

SUMMARYThis paper considers the receding horizon tracking control of the unicycle-type robot subject to coupled input constraint based on virtual structure. The tracking position of the follower is considered to be a virtual structure point with respect to a Frenet-Serret frame fixed on the leader, and the desired control input of the follower not only depend on the input of the leader but also the separation vector. Firstly, a sufficient input condition for the leader robot is given to enable the follower to track its desired position while satisfying its inputs constraint. Secondly, receding horizon control scheme is designed for the follower robot, in which the recursive feasibility is guaranteed by developing a diamond-shaped positively invariant terminal-state region and its corresponding controller. Finally, simulation results are provided to verify the effectiveness of the scheme proposed.

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This paper proposes a robust self-triggered model predictive control (MPC) with an adaptive prediction horizon scheme for constrained nonlinear discrete-time systems subject to additive disturbances. At each triggering instant, the controller provides an optimal control sequence by solving an optimal control problem (OCP), and at the same time, determines the next triggering time and prediction horizon. By implementing the algorithm, the average sampling frequency is reduced and the prediction horizon is adaptively decreased as the system state approaches a terminal region. Meanwhile, an upper bound of performance loss is guaranteed when compared with a nominal periodic sampling MPC. Feasibility of the OCP and stability of the closed-loop system are established. Simulation results verify the effectiveness of the scheme. Index Terms-Self-triggered control, model predictive control (MPC), adaptive prediction horizon, nonlinear systems. I. INTRODUCTION M ODEL predictive control (MPC) has the advantages of explicitly handling input and state constraints and optimizing the performance [1]. Generally, traditional MPC requires a quite heavy computation, especially for nonlinear systems, to solve an optimization control problem (OCP) at each step. This may prevent its application to "fast" systems such as unmanned ground vehicles, quadrotors and servo systems, etc. Therefore the design of MPC with reduced computational load is an urgent demand for its application. Event-triggered and self-triggered MPC are studied in [2]-[6] which aim at achieving a better trade-off between system performance and resource saving. For instance, an eventtriggered MPC for continuous-time nonlinear systems with constraints is developed in [2] based on Lyapunov theory. By checking the deviation between the actual state and the

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