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The paper deals with new directions in research, development and applications of advanced control methods and structures based on the principles of optimality, robustness and intelligence. Present trends in the complex process control design demand an increasing degree of integration of numerical mathematics, control engineering methods, new control structures based of distribution, embedded network...
Brushless DC (BLDC) motors are widely used for many industrial applications because of their high efficiency, high torque and low volume. The aim of this research is to develop a complete model of the BLDC motor and to design an optimal controller for its position control. Generally PID controller is used for many control problems because of its simple structure and easy implementation. However in...
There are many improved methods of optimizing PID control parameters. However, it is still difficult for most of them to reach the performance of fast search and optimize the parameters with high quality. Hence, an optimal predicting PID controller based on elite-based hybrid genetic algorithm is presented which improves the simple GA in a large degree with some new strategies and is suitable for...
The target to solve multiobjective optimization problems (MOOP) is to find as many Pareto-optimal solutions as possible. A new algorithm aimed to solve MOOP was proposed in this paper - multi-agent quantum evolutionary algorithm (MAQEA) on the basis of quantum mechanics theory, the study and competition ability of multi-agent system and organic evolutionary strategy. In the multi-agent system, the...
Ant colony algorithm is a brand-new type of simulate evolution algorithm, which focus on its solution to assembled optimized question. The author utilizes this algorithm to optimize PID control parameter, but in basic ant colony algorithm, there are some defects of slow convergence speed, easy to get stagnate, and low ability of full search. This paper presents a method of optimized PID control of...
In this paper, the PID type single-input fuzzy logic controller (SFLC) is proposed by modified from PD type SFLC. By proposed structure, the steady error from PD type SFLC can be improved. Furthermore, the optimal control performance can be achieved by tuning membership function and gain parameter with genetic algorithm (GA). The simulation results show the proposed structure has better performance...
Based on the conception of evolution, the particle swarm optimization (PSO) algorithm and genetic algorithm (GA) are applied to parameters optimization of PID controller. Simulations are carried out in the typical industrial models. Comparing PSO with GA, it is shown that the performance of PSO is better than that of GA, it is provided that a preferable method to optimization parameters of PID controller
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