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Multiple controllers based on multiple radial based function neural network(RBFNN) models are used to control a nonlinear system to trace a set-point.
Abstract. Multiple controllers based on multiple radial based function neural network(RBFNN) models are used to control a nonlinear system.
Jun 3, 2007 · A switching controller which switches among different controller based on different RBFNN is used to adapt the varing set-point value and improve the output ...
An adaptive output feedback control scheme is pre sented for output tracking of a class of continuous- time nonlinear plants. An FU3F neural network is used.
An RBF neural network is used to adaptively compensate for the plant nonlinearities. The network weights are adapted using a Lyapunov-based design. The method ...
Missing: Switching | Show results with:Switching
The simulation results show that RBF neural network and PD parallel control system can control the nonlinear system. Moreover, the control system with Newton ...
This paper presents an off-line identification-based modelling method for a class of smooth SISO nonlinear systems for the purposes of process control, ...
Oct 22, 2024 · This paper presents a nonlinear optimal control technique based on approximating the solution to the Hamilton-Jacobi-Bellman (HJB) equation.
Abstract. The paper considers intelligent control system architectures for task-level control. The problem is to compute feedforward control for a sequence ...
May 19, 2023 · This paper investigates the adaptive neural network prescribed performance control problem for a class of dual switching nonlinear systems with time-delay.