Adil M. Ahmad

Purpose: This paper aims to study the issues of adaptive fuzzy control for a category of switched under-actuated systems with input nonlinearities and external disturbances. Design/methodology/approach: A control scheme based on sliding mode surface with a hierarchical structure is introduced to enhance the responsiveness and robustness of the studied systems. An equivalent control and switching control rules are co-designed in a hierarchical sliding mode control (HSMC) framework to ensure that the system state reaches a given sliding surface and remains sliding on the surface, finally stabilizing at the equilibrium point. Besides, the input nonlinearities consist of non-symmetric saturation and dead-zone, which are estimated by an unknown bounded function and a known affine function. Findings: Based on fuzzy logic systems and the hierarchical sliding mode control method, an adaptive fuzzy control method for uncertain switched under-actuated systems is put forward. Originality/value: The “cause and effect” problems often existing in conventional backstepping designs can be prevented. Furthermore, the presented adaptive laws can eliminate the influence of external disturbances and approximation errors. Besides, in contrast to arbitrary switching strategies, the authors consider a switching rule with average dwell time, which resolves control problems that cannot be resolved with arbitrary switching signals and reduces conservatism.

In this paper, a low-computation adaptive self-triggered tracking control scheme is proposed for a class of strict-feedback nonlinear systems with input saturation. By introducing two novel error transformation functions, the designed low-computation adaptive control scheme can overcome the problem of complexity explosion in the absence of any filters, such that the developed control scheme is more applicable. In addition, to save communication resources in networked systems, a self-triggered communication strategy is proposed which can predict the next trigger point based on the current information. Compared with traditional event-triggered mechanisms, the computational burden arising from continuous monitoring of threshold conditions was successfully avoided. Furthermore, the input saturation problem considered in this paper prevents the overload phenomenon caused by signal jumps, and the adverse effects are compensated by introducing an auxiliary system. The effectiveness of the developed control scheme is verified through a simulation example.

This article investigates the active disturbance rejection‐based distributed event‐triggered bipartite consensus problem of nonaffine nonlinear multiagent systems with input saturation. To reduce the update frequency of the control signal, an event‐triggered mechanism is employed for each follower. Additionally, the active disturbance rejection technology, as a combination of the extended state observer and the tracking differentiator, is introduced to estimate the uncertainties of the system and address the problem of explosion of complexity in the backstepping design process. Compared with the traditional distributed bipartite consensus schemes using neural networks/fuzzy logic systems, the design complexity of the control law is reduced in the proposed scheme. Meanwhile, the tracking error is maintained within a predefined range via the funnel function. Finally, the stability of the system is proved under the Lyapunov stability analysis theory, and the effectiveness of the proposed strategy is verified via simulation examples.

This article studies the adaptive optimized leader-follower consensus control problem for a class of discrete-time multi-agent systems with asymmetric input saturation constraints and hybrid faults based on the optimized backstepping technique. Different from the conventional saturation model, we consider an individual asymmetric saturation constraint for each actuator instead of a common upper and lower bound for all actuators. Besides, due to the complexity and scale of the actuators, a set of hybrid faults is also considered, with the main focus on the partial fault and bias fault for the multi-agent systems. To eliminate the effects of saturation and faults, a simplified smooth function is constructed to approximate the asymmetric saturation model, and the designed compensation signals are used to cope with the two main fault types such that the fault-tolerance and control performance of the multi-agent systems can be effectively improved. Subsequently, integrating all mechanisms into an adaptive optimized backstepping design framework, the long-term strategic utility functions and the virtual control signals generated in the backstepping procedure are approximated to the desired results by adopting the radial basis function neural network (RBFNN) and the actor-critic framework of reinforcement learning, and the weights in the actor-critic network are adjusted in the light of a classic gradient descent method. According to the forward difference Lyapunov function approach, it is proved that the closed-loop system can be stabilized and all errors are semiglobally uniformly ultimately bounded. Finally, the validity of the proposed control scheme is demonstrated through a simulation example.

This article presents an event-based adaptive decentralised output feedback control scheme for interconnected systems with Bouc-Wen hysteresis and unmeasured system states. To reduce some unnecessary data transmissions, a novel dynamic threshold adjustable event-triggering mechanism is proposed. In contrast with the traditional static threshold event-triggering mechanism, communication efficiency is greatly enhanced. Then, a neural networks-based observer is constructed to address the problem of unmeasured states, and the dynamic surface control method is used to address the ‘explosion of complexity’ that comes up when traditional backstepping design processes are used. Meanwhile, the Nussbaum function is introduced to eliminate the effect of unknown hysteresis. By resorting to the Lyapunov stability theory, it can be verified that all signals in the close-loop system are uniformly ultimately bounded. Finally, a simulation example is given to verify the effectiveness of the developed control scheme.