Key result
Novel self-triggered adaptive MPC reduces computation and communication burden while maintaining ideal control performance.
Why the study?
Existing self-triggered MPC methods determine triggering instants based on single-instant state differences, motivating a strategy to reduce computation and communication burdens while maintaining control performance in discrete-time nonlinear networked control systems.
A novel self-triggered adaptive MPC strategy reduces computational and communication burdens for discrete time nonlinear networked control systems.
May ease resource use in nonlinear control; leaves open empirical validation before practical adoption.
For discrete time nonlinear networked control systems, a novel self-triggered adaptive model predictive control (MPC) strategy is developed. Different from the existing self-triggered MPC methods that determine the triggering instants based on the difference between the optimal and real states at one single instant, the proposed approach updates the MPC system according to the differential form of the state error of two consecutive sampling moments to effectively reduce the computation and communication burden while maintaining the ideal control performance. In addition, this paper introduces a new adaptive prediction horizon mechanism to the self-triggered MPC, so that the amplitude of prediction horizon contraction is sufficiently large to further reduce the computational burden of the MPC method. Finally, the recursive feasibility and robust stability of this proposed strategy are proved strictly by theoretical analysis, and the simulation comparison results are shown to verify the proposed framework.
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He et al. (2023) studied discrete time nonlinear networked control systems. self-triggered adaptive model predictive control (MPC) strategy vs. existing self-triggered MPC methods was evaluated on computation and communication burden, recursive feasibility, and robust stability. A novel self-triggered adaptive model predictive control strategy effectively reduced computation and communication burden while maintaining ideal control performance in discrete time nonlinear systems.
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