Control of Neural Network Feedback Linearization Based on Chaotic Particle Swarm Optimization  

作　　者：S.X. Wang H. Li Z.X. Li 

机构地区：[1]School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China

出　　处：《Journal of Energy and Power Engineering》2010年第4期37-44,共8页能源与动力工程（美国大卫英文）

基　　金：This work is supported by National Natural Science Foundation of China （50776005）.

摘　　要：A new chaotic particle swarm algorithm is proposed in order to avoid the premature convergence of the particle swarm optimization and the shortcomings of the chaotic optimization, such as slow searching speed and low accuracy when used in the multivariable systems or in large search space. The new algorithm combines the particle swarm algorithm and the chaotic optimization, using randomness and ergodicity of chaos to overcome the premature convergence of the particle swarm optimization. At the same time, a new neural network feedback linearization control system is built to control the single-machine infinite-bus system. The network parameters are trained by the chaos particle swarm algorithm, which makes the control achieve optimization and the control law of prime mover output torque obtained. Finally, numerical simulation and practical application validate the effectiveness of the method.

关 键 词：Chaos particle swarm algorithm OPTIMIZATION neural network single-machine infinite-bus system feedback linearization. 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP301.6[自动化与计算机技术—控制科学与工程]