Neural‐based adaptive event‐triggered tracking control for flexible‐joint robots with random noises

Abstract

In this study, a novel adaptive neural network control scheme is proposed to resolve the tracking control problem for flexible-joint robots with random noises. More precisely, the controlled system in this study is a multi-input and multi-output stochastic nonlinear system, employing the traditional backstepping design to study such a system will greatly increase the amount of calculation. To resolve this problem, the command filtered technology is applied to the adaptive neural network design framework. More importantly, with the aid of the event-triggered strategy, the proposed control algorithm can reduce the communication burden to a certain extent. Besides, the proposed method can also ensure that the tracking error converges to a small neighborhood of the origin. Finally, the simulation example is given to verify the effectiveness of the proposed algorithm.

Learning‐based robust control methodologies under information constraints

“…On the other hand, in Reference 14, the authors developed a new proportional‐integral‐derivative‐based tracking control for uncertain Euler–Lagrange systems subject to actuation failures and saturation. In Reference 15, the authors proposed a novel adaptive neural network control scheme to resolve the tracking control problem for flexible‐joint robots with random noises. In addition, the command filtered technology is applied to the adaptive neural network design framework.…”

Non-Negative Adaptive Mechanism-Based Sliding Mode Control for Parallel Manipulators with Uncertainties

“…Among them, robust control techniques are used to cancel complex uncertainties and external disturbances as well as provide higher robustness. Based on computational intelligence like neural networks, fuzzy systems, robust tracking control strategies have been proposed in [6][7][8][9][10][11][12]. However, the learning techniques always need a huge computation because of the training complication in fuzzy rules or neural weights.…”

Dynamic event‐triggered decentralized control of interconnected systems

“…If and only if specific well‐designed event trigger conditions are violated, the control input signals are updated in event‐triggered control. Owing to this feature, event‐triggered control has been extensively applied in practical applications, for instance, autonomous land vehicles, 13 rigid spacecraft, 14 smart grids, 15 and flexible‐joint robots 16 . The development of event‐triggered mechanisms (ETMs) is now mostly focused on static and dynamic ETMs.…”

“…Owing to this feature, event-triggered control has been extensively applied in practical applications, for instance, autonomous land vehicles, 13 rigid spacecraft, 14 smart grids, 15 and flexible-joint robots. 16 The development of event-triggered mechanisms (ETMs) is now mostly focused on static and dynamic ETMs. The generally used static ETM is commonly composed of a rule given on the state of the system as in the work of Tabuada.…”