Event‐triggered distributed H _∞ control of physically interconnected mobile Euler–Lagrange systems with slipping, skidding and dead zone

On Distributed Implementation of Switch-Based Adaptive Dynamic Programming

“…For example, in the numerical experiments of Section IV, it is natural to partition the state according to the queues at different intersections in a traffic system; or to partition the state according to the temperatures in different rooms in a building. From this point of view, the partition of the state can be done in a similar way as large-scale systems are partitioned into smaller subsystems [23]- [27].…”

“…The peculiar nature of the switching input makes the available divide-et-impera methodologies inapplicable to this setting. For example, in hierarchical reinforcement learning [23], [24] or distributed ADP methods [25]- [27], it is common to treat the neighboring agents as a continuous disturbance whose effect is to be minimized in the norm. Unfortunately, this approach is not appropriate to model the switching interactions among agents in switch-based ADP.…”

Direct Neural Network Adaptive Tracking Control for Uncertain Non-Strict Feedback Systems With Nonsymmetric Dead-Zone

“…However, many results in these papers were based on traditional backstepping technique as well as the approximation features of FlSs or NN [18,24], as we known that in the recursive process of these approximation and backstepping-based approaches, as the order increased, the design procedure can cause 'explosion of complexity' [26,28,34], many adaptive parameters were needed to be adjusted [30][31][32][33][34] even together with dynamic surface control (DSC) method [13], therefore, the online computation burden is rather heavy, especially in dealing with MIMO or non-strict feedback nonlinear systems [18,24]. Different from these results [30][31][32][33][34], or the optimal control method to compensate the dead-zone [37], in this paper, we will explore a direct novel alleviating computation NN control method for nonlinear non-strict feedback systems.…”

“…Adaptive neural control [30] and fuzzy decentralized control [31] were proposed for unknown control directions systems and strong interconnected nonlinear systems in unmodeled dynamics. Based on robust optimal control method, [37] discussed the event-triggered physically interconnected mobile Euler-Lagrange systems.…”