基于实数极值优化的预测控制改进方法及其应用研究

发布时间：2018-05-14 21:30

本文选题：实数编码群体极值优化 + 预测控制　；参考：《温州大学》2017年硕士论文

【摘要】：预测控制在复杂工业过程控制系统中得到了广泛的应用,但现有算法严重依赖设计经验,因此如何采用进化算法对传统预测控制方法中的滚动优化策略进行改进,从而进一步提升预测控制方法高效求解约束优化控制难题的能力,具有重要的理论意义和工程应用价值,已成为学术界和工业界的研究热点之一。另外,预测控制方法在电力电子功率变换器和多区域互联电力系统中的应用研究探索近年来越来越受到电气工程领域的关注,但还处于起步阶段。因此,本文从基于实数编码群体极值优化的新视角研究预测控制的改进方法及其在典型过程控制系统与多区域互联电力系统中的应用。本文的主要研究工作和创新点如下:(1)提出了一种基于多点非均匀变异的实数编码群体极值优化算法(RCEO),通过对多个典型连续优化测试函数和电压自动控制系统的分数阶PID控制器参数优化问题的仿真研究,验证了 RCEO相比基于实数编码的遗传算法(GA)、粒子群算法(PSO)和基于其它变异操作的极值优化等进化算法具有更少的可调参数和更佳的优化性能;(2)在研究工作(1)的基础上,提出了基于RCEO的约束广义预测控制算法(CGPC-RCEO )和约束动态矩阵控制算法(CDMC-RCEO),分别通过对热交换器循环水温度控制和连续搅拌釜式反应器温度控制的仿真研究从而验证了本文提出的CGPC-RCEO和CDMC-RCEO算法相比经典预测控制算法、基于GA和PSO的预测控制方法具有更佳的系统输出响应和控制增量响应等控制性能;(3)基于研究工作(2),将CDMC-RCEO的基本思想推广应用到多区域互联电力系统中,提出了一种基于CDMC-RCEO的多区域互联电力系统分布式负荷频率预测控制方法,对两区域和三区域互联电力系统的仿真实验结果表明了:本文提出方法相比传统积分控制算法、比例积分算法、CGPC-RCEO、基于GA和PSO的CDMC算法具有更佳的动态和稳态控制性能,且具有更强的鲁棒性。
[Abstract]:Predictive control has been widely used in complex industrial process control systems, but the existing algorithms rely heavily on design experience, so how to improve the rolling optimization strategy in traditional predictive control methods by evolutionary algorithm. Therefore, it is of great theoretical significance and engineering application value to improve the ability of predictive control method to solve the problem of constrained optimal control efficiently. It has become one of the research hotspots in academia and industry. In addition, the application of predictive control method in power electronic power converters and multi-area interconnected power systems has attracted more and more attention in the field of electrical engineering in recent years, but it is still in its infancy. Therefore, this paper studies the improved predictive control method and its application in typical process control systems and multi-area interconnected power systems from a new perspective of real-coded population extremum optimization. The main research work and innovation of this paper are as follows: (1) A real coded population extremum optimization algorithm based on multipoint nonuniform mutation is proposed. By dividing several typical continuous optimization test functions and voltage automatic control systems, a real coded population extremum optimization algorithm is proposed. Simulation study on Parameter Optimization of Multi-order PID Controller, It is verified that RCEO has less adjustable parameters and better optimization performance than genetic algorithm based on real number coding, particle swarm optimization (PSO) and extreme value optimization based on other mutation operations. A constrained generalized predictive control algorithm (CGPC-RCEO) based on RCEO and a constrained dynamic matrix control algorithm (CDMC-RCEOO) are proposed. The simulation results of heat exchanger circulating water temperature control and continuous stirred tank reactor temperature control are verified. Compared with the classical predictive control algorithm, the CGPC-RCEO and CDMC-RCEO algorithms are proposed in this paper. The predictive control method based on GA and PSO has better control performance such as system output response and control increment response. Based on the research work, the basic idea of CDMC-RCEO is extended to multi-area interconnected power system. A distributed load frequency predictive control method for multi-area interconnected power systems based on CDMC-RCEO is proposed. The simulation results for two and three regions of interconnected power systems show that: compared with the traditional integral control algorithm, the proposed method in this paper is better than the traditional integrated control algorithm. The proportional integration algorithm CGPC-RCEO. CDMC algorithm based on GA and PSO has better dynamic and steady-state control performance and stronger robustness.
【学位授予单位】：温州大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP13;TP18

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