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In this work is presented a design and implementation of an intelligent controller applied to dynamic systems. The main objective is to design a management intelligent system that acts in the controllers of Direct current machine (DC) and thermo-eletric control systems drives. The purpose is to evaluate the performance of algorithms for intelligent control that integrate DC and thermal actuators to...
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...
Aiming at the problem that it is difficult to confirm the parameters of the PID controller and the parameters can not be changed once identified, an intelligent PID control method is proposed. According to the size of the system error, this algorithm controls the system with different subsections of different parameters, by using the particle swarm optimization (PSO) to optimize the parameters of...
This paper presents a self-adjusting fuzzy proportion integral differential (PID) controller for high non-linear, time-varying automatic gauge control (AGC) System of rolling mills. For the adjusting of the PID parameters is difficulty. The three parameters of PID controller can be adjusted adaptively on line based on the fuzzy rules. The simulation shows that the fuzzy self-adjusted PID controller...
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...
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