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标题：基于改进全阶状态观测器的异步电机速度估算系统 |
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作者：沈凤龙，满永奎，王建辉 |
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| 2019年第6期 访问次数：290次 |
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摘要：为解决全阶状态观测器反馈自适应率参数寻找最优解困难的问题，提出一种部分种群给定的粒子群优化算法。该算法将利用频域方法设计好的几组参数值编码后混入随机初始种群，使得初始种群中优良品质个体的数量大大增加，提高了收敛速度和搜索效率。为解决不同转速下全阶状态观测器的离散精度和计算量相矛盾问题，提出在低速时采用欧拉法而在高速时采用简化的梯形法对全阶状态观测器进行离散化，既保证了系统估算精度，又使计算量大大减小。仿真和实验结果表明，基于全阶状态观测器转速估算系统具有良好的动态响应速度和稳态精度。
Abstract: For the optimal solution of feedback adaptive rate parameters of full order state observer was difficult to find, an improved particle swarm offline optimization algorithm was proposed based on a given part of the population parameters was given. The feedback adaptive rate parameter design criterion was given by the frequency domain design method. Combined with the optimal advantages of particle swarm algorithm, some designed and encoded parameter values were mixed to the random initial population to increase the number of the fine individuals. The method can improve the convergence speed and search efficiency and achieve good optimization effect. Aiming at the problem of error and the amount of calculation, an improved method of discretization was proposed. The euler method was adopted in low speed and the trapezoidal method was adopted to discrete the full order state observer. At the same time, the trapezoidal method were reasonable approximate simplified. The discretization precision of improved method was greatly increased in the high speed and the amount of calculation was greatly reduced to realize the full order state observer of accurate digital control with the premise of guarantee accuracy advantages. Simulation and experimental results show that the speed estimation system has good dynamic response speed and steady-state accuracy based on the given algorithm.
Key words: full order observer; feedback adaptive rate; particle swarm optimization algorithm; discretization |
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