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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。
A constrained state feedback predictive control algorithm based on chaos particle swarm optimization was proposed to solve the control problem with input constraints and state constraints. The chaos particle swarm optimization is introduced into the rolling optimization process of constrained state feedback predictive control, which enhances the local search and global search ability of the algorithm in the constrained range. The feasibility and effectiveness of a state feedback predictive control algorithm based on chaotic particle swarm optimization are verified by solving an actual constrained linear discrete-time system control optimization problem. Comparing with the traditional quadratic programming algorithm, The superiority of this algorithm proves the good robustness of the state feedback predictive control system.