of Birmingham Neural adaptive control for uncertain nonlinear system with input saturation

Predictor-Based Adaptive Neural Network Control of Unknown Nonlinear Systems With Time-Varying Input Delay.

A predictor-based adaptive neural network control method to mitigate the adverse effects of TVIDs on control performance, an observer-form predictor is constructed, and a corresponding state feedback control strategy is designed.

Output Feedback Higher-Order Sliding Mode Controller for a Class of Nonlinear Systems

Pure Feedback Control for Nonlinear Systems Based on Predefined-Time Performance Function

This paper investigates the predefined-time adaptive neural tracking control problem for a class of nonlinear pure feedback systems with full state constraints. A novel barrier Lyapunov function (BLF) integrated with a predefined-time performance function (PTPF) is constructed to ensure that the tracking error converge...

Adaptive Finite-Time Control for Multi-Input Multi-Output Nonlinear Systems with Input Saturation and External Disturbances

This work presents a finite-time adaptive fuzzy control approach for a category of multi-input multi-output nonlinear systems in the presence of input saturation and external disturbances. A hyperbolic function is adopted to convert the unconstrained control command into a bounded signal so that the actuator constraint...

Observer-based adaptive neural event-triggered control for nonlinear systems with multiple constraints and multiple faults

For nonlinear constrained systems with sensor and actuator faults (SAFs), a neural network-based adaptive event-triggered controller is proposed. Nonlinearities and SAFs commonly occur in actual engineering plants, which can lead to system instability, and severely degrade control performance. The state observer with a...

Robust Adaptive Neural Network-Based Backstepping Tracking for Second-Order Euler-Lagrange Systems with Unknown Parameters

A continuous adaptive control law is developed that eliminates chattering typically caused by discontinuous robust terms and proves that all closed-loop signals are uniformly ultimately bounded, achieving asymptotic trajectory tracking with smooth control inputs.