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In this paper, we apply an adaptive control algorithm to a nonlinear multivariable process. Such controller is based on the multiple models approach. As the design of the control law requires the knowledge of the dynamical model of the system, we deal firstly with the identification of the system parameters using the recursive least squares and the retro propagation of the gradient algorithms. Then,...
A double neuron model-free control method for pH processes is presented. In this control system, the principal neuron controller is designed to control pH processes, and the subordinate neuron controller is used to compensate for the nonlinear characteristic. The purpose of improving response speed and reducing errors is reached. Simulation results show the efficiency, good disturbance resistibility...
Generally the application of traditional adaptive control algorithm relies on the mathematic model of system. But mathematic models of some dynamic systems are difficult to establish. According to this actual problem and the existing structure of algorithm, an improved Model Free Adaptive control algorithm based on neural network is put forward in this paper. Within corresponding controller, equivalent...
In this paper, an adaptive neural controller design procedure for a class of nonlinear systems with incompletely known and time varying nonlinearities is presented. The unknown process dynamics is on-line identified using feedforward neural networks based estimators. Both the form of the controller and the adaptation laws of neural networks weights are derived from a Lyapunov stability property of...
Aiming at the practical plants with strong nonlinear characteristics, a new multi-model internal model control (MIMC) strategy based on Gaussian potential function networks (GPFN) is proposed in this paper. The internal model is represented by GPFN and the corresponding controller can be got directly, which simplifies the control law design and analyses greatly. Meanwhile, the way of model switch...
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