摘要
提出一种基于混沌粒子群优化的约束状态反馈预测控制算法,用于解决带有输入约束和状态约束的控制问题。将混沌粒子群优化引入到约束状态反馈预测控制的滚动优化过程中,增强了算法在约束范围内的局部搜索和全局搜索能力。通过对一个实际的带有约束的线性离散系统控制优化问题的解决,验证了基于混沌粒子群优化的状态反馈预测控制算法的可行性和有效性,与传统的二次规划算法的比较结果说明了此算法的优越性,证明了状态反馈预测控制系统良好的鲁棒性。
An algorithm of constrained state feedback model predictive control was proposed based on the chaotic particle-swarm optimization （CPSO） to solve the control problem with simultaneous constraints on inputs and states. CPSO was used for iterative optimization to enhance the capabilities of total search and partial search in the constraint area. A practical constrained optimization problem of the discrete-time linear system is solved by CPSO. The results show the feasibility and effectiveness of constrained state feedback model predictive control based on the chaotic particle-swarm optimization （CPSO）. By comparing the simulation results of QP and PSO, we show the advantages of the PSO-based constrained state feedback model predictive control. The superior robustness of state feedback model predictive was also verified.
出处
《计算机与应用化学》
CAS
CSCD
北大核心
2012年第1期61-66,共6页
Computers and Applied Chemistry
基金
国家自然科学基金资助项目(21006127)
关键词
状态反馈预测控制
约束
粒子群优化算法
混沌局部搜索
state feedback model predictive control
constraint
particle swarm optimization
chaotic local search
