In this article, the singularity-free adaptive fuzzy fixed-time control problem is studied for an uncertain n-link robotic system with position tracking error constraint. The controlled can be described as a multiple-input-multiple-output system.To implement user-defined performance, improved conversion mechanism based on performance functions presented such that converted limited to interval greater than zero, and appropriate barrier Lyapunov function (BLF) constructed avoid breach of...

This paper addresses the adaptive finite-time decentralized control problem for time-varying output-constrained nonlinear large-scale systems preceded by input saturation. The intermediate functions designed are approximated neural networks. Time-varying barrier Lyapunov used to ensure that system output constraints never breached. An scheme is devised combining backstepping approach with function theory. Under action of proposed approach, stability and desired performance can be obtained in...

This article addresses the problem of decentralized adaptive fuzzy fixed-time control for a class pure-feedback interconnected nonlinear systems with full-state tracking error constraints. The logic (FLSs) are adopted to model unknown functions. Combining properties prescribed performance functions (PPFs) barrier Lyapunov (BLFs), predefined state dynamic and good accuracy guaranteed. Then, on basis backstepping recursive design technique, structurally simple controller is designed....

This article investigates the problem of event-based decentralized adaptive fuzzy output-feedback finite-time control for large-scale nonlinear systems. The full-state tracking error constraints, unmeasured states, and external disturbances are simultaneously considered in controlled unknown auxiliary functions modeled by using logic systems, a state observer is established to estimate states. By taking new transformation method based on prescribed performance constructing corresponding...

This article proposes a variable-gain state observer-based sliding mode self-healing control (VSO-based SMSHC) method for wastewater treatment process (WWTP) with changeable external disturbances and sensor failures. To reconstruct the unmeasured system states, novel VSO is developed, in which gains are adjusted by following an automatic mechanism adapting to variation of disturbances. An adaptive compensation coefficient failure factor designed counteract effects on observation tracking...

Dear Editor, This letter is concerned with the data-driven fault compensation tracking control for a coupled wastewater treatment process (WWTP) subject to sensor faults. Invariant set theory introduced eliminate completely bounded and differentiable conditions of non-affine dynamics explicitly express inputs. An adaptive mechanism constructed accommodate effects By employing cubic absolute-value Lyapunov criteria, it shown that all signals are error converges an adjustable neighborhood near...

With the increase of wastewater treatment volume and insufficiency preventive maintenance measures, potential safety hazards process (WWTP) are gradually emerging. The unplanned sensor failures may result in false information feedback reduce reliability control system. This paper attempts to formulate a working mode substitution-based performance self-recovery (WMS-based PSRC) strategy for WWTP against failures. Therein, substitution indication-based switching function scheme is developed...

A method to predict the performance characteristics of switched reluctance motor (SRM) drive systems under normal and fault operating conditions is presented. The uses a genetic algorithm (GA) based artificial neural networks (ANNs) approach which applied for its interpolation capabilities highly nonlinear in order obtain fast accurate prediction SRM system.

Summary This paper studies the problem of adaptive fuzzy asymptotic tracking control for multiple input output nonlinear systems in nonstrict‐feedback form. Full state constraints, quantization, and unknown direction are simultaneously considered systems. By using logic systems, functions identified. A modified partition variables is introduced to handle difficulty caused by structure. In each step backstepping design, symmetric barrier Lyapunov designed avoid breach issues...

Summary This paper is concerned with the neural‐based decentralized adaptive control for interconnected nonlinear systems prescribed performance and unknown dead zone outputs. In controller design procedure, neural networks are employed to identify auxiliary functions, obstacle caused by output nonlinearity resolved via introducing Nussbaum function. Then, a reliable developed through incorporating backstepping method technique. light of Lyapunov stability theory, it verified that proposed...

Summary This paper investigates the problem of adaptive dynamic surface control for pure‐feedback time‐varying state constrained nonaffine nonlinear system. A continuous and semibounded condition is proposed function to ensure that system can be controlled, invariant set introduced this mild condition. By employing technique, “complexity explosion” caused by backstepping technique averted in developed method. Robust compensators are devised weaken poor effect disturbances uncertainties. The...

The increasing utilization of wastewater necessitates dedicated attentions to the potential security threats, and formulate strategies for defense, response, future protection. nonideal actuator subject faults constraints may underload driving force reduce sewage purification efficiency. This article proposes an adaptive performance self-recovery control strategy treatment process (WWTP) with actuator. Therein, a Gaussian error function is reconstructed imitate asymmetrical constraints. A...

To solve the anti-disturbance control problem of dissolved oxygen concentration in wastewater treatment plant (WWTP), an scheme based on reinforcement learning (RL) is proposed. An extended state observer (ESO) Takagi–Sugeno (T-S) fuzzy model first designed to estimate system and total disturbance. The controller compensates for disturbance output real time, online searches optimal policy using a neural-network-based adaptive dynamic programming (ADP) controller. For reducing computational...

In this paper, the observer-based synergetic adaptive neural network control method is designed for a class of discrete-time systems with dead-zone. A macro-variable introduced by approach to theory and networks are utilised estimate unmeasured states unknown functions in system. Furthermore, employing an design procedure Lyapunov stability theory, closed-loop system guaranteed, desired performance achieved simultaneously. Finally, some simulation results given prove validity developed method.

Two methods to predict the performance characteristics of switched reluctance motor (SRM) drive systems under normal and fault operating conditions are presented. The first method is based on use an iterative approach which indirectly couples a two-dimensional nonlinear finite element model state space describing SRM system. second uses artificial neural networks applied for its interpolation capabilities highly in order obtain fast accurate prediction