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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,...
This research work presents supervised Artificial Intelligence based control technique for an inverted pendulum. The inverted pendulum system is a classic control problem that is used in research. It is a suitable process to test prototype controllers due to its high non-linearities and lack of stability. Most traditional controllers (feedback linearisation, rule based control) are based around an...
In this paper, a new methodology for feed forward-feedback control system design is proposed. Initially, the concept of control equilibrium point is introduced. Using this concept, the steady state control command is determined so as to maintain the desired situation of the system. Non-model-based feed forward control law is conducted on this basis using an artificial neural network. The feedback...
Proper control of the wood-drying kiln is crucial to ensure the satisfactory quality of dried wood and in minimizing drying time and energy. This paper investigates the development and evaluation of a robust control system for a wood drying kiln process incorporating variable structure control (VSC) such that the moisture content of lumber will reach and be stabilized at the desired set point. A description...
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...
This paper is concerned with the development of an online Reinforcement Learning (RL) technique that significantly improves the control systems behavior. The reinforcement learner is based on Q-learning and the final controller is an artificial neural network whose weights are tuned by on line learning. In order to speed up the learning processes and prevent the plant from the instability, initially...
Welding molten pool depth control system based on single neuron self-tuning PID is designed, and several learning algorithms for neuron weights are simulated, the simulation results show that this controller reacts quickly and has good stability. Adopting the improved Hebb learning algorithm can bring best effect. The experiment with various cross-section and seam-gap workpiece is perfect, showing...
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