Dynamic Event-Triggered Composite Learning Robot Control With Relaxed Computing Burden

Composite learning can guarantee the exponential stability of adaptive robot control under a condition of interval excitation (IE) that weakens the classical condition of persistent excitation. However, the existing composite learning law must be real-time updated even if tracking and estimation errors have already converged, resulting in the waste of computational resources. This paper proposes a dynamic event-triggered composite learning robot control (ET-CLRC) strategy to relax computing burden, where a dynamic ET condition with adaptive thresholds is designed such that control and adaptation laws are updated only when the predefined condition is satisfied. The proposed method is distinguished from existing ET adaptive robot control methods in the following aspects: 1) The designed event consists of tracking errors and internal variables without involving realtime updated information on control and adaptation laws; 2) the practical exponential convergence of tracking and estimation errors under the IE condition is derived while avoiding the Zeno behavior. Experiments on an industrial robot with seven degrees of freedom have shown that the proposed ET-CLRC can achieve control performance similar to that of real-time updated CLRC while significantly relaxing the computational burden.