Event-Triggered Sliding Mode Control Under Partial Model Information: Design Framework and Experimental Validation

This paper develops an event-triggered sliding mode control (ETSMC) strategy for partially unknown disturbed systems via adaptive dynamic programming, where the system matrix is considered to be unknown to the designer. Both the sliding function and ETSMC scheme are constructed without using system matrix. An input-based event-triggered mechanism is introduced between the plant and the sliding mode controller to reduce the communication frequency. Compared with existing results on ETSMC, the proposed event-triggered mechanism can guarantee the reachability to the ideal sliding surface
$s(t)=0$
and thus the external disturbances can be eliminated completely. An online policy iteration algorithm is formulated to implement the partial-model-free ETSMC strategy. It is proven that in all policy iteration steps, the reachability of the prescribed sliding surface and the optimal control performance of the sliding mode dynamics are ensured simultaneously by the proposed online updated ETSMC scheme as well as the Zeno phenomenon of the proposed event generator can be excluded. Finally, the effectiveness and the applicability of the proposed ETSMC scheme are illustrated by a numerical example and a real experiment on the permanent magnet synchronous motor speed regulation system. Note to Practitioners—ETSMC is an effective robust control strategy for the practical networked control systems that can compensate the matched disturbances in the plant as well as reduce the information transmission frequency between the plant and controller. However, the design of the existing ETSMC strategies depends on the completely known system dynamics, which is difficult or expensive to be obtained in many engineering applications. Meanwhile, the ideal sliding motion cannot be attained by the existing ETSMC approaches so that the disturbance rejection performance is degraded unsatisfactorily. To address these concerns, this paper develops a novel partial-model-free ETSMC strategy based on adaptive dynamic programming for disturbed systems to achieve the optimal control performance without using the system matrix. The reachability of the ideal sliding surface and the exclusion of the Zeno behavior as well as the convergence of the online policy iteration algorithm are analyzed theoretically. The engineering applicability of the novel ETSMC scheme is verified in the speed regulation problem of the permanent magnet synchronous motor.