基于和声搜索的机械臂控制优化算法研究

发布时间：2019-02-15 20:30

【摘要】：机械臂控制系统是一个多变量、参数不确定、结构复杂、强耦合的非线性系统。传统控制器难以实现对复杂机械臂的实时性和鲁棒性控制,遗传算法具有很好的收敛性、实时性和鲁棒性,被广泛用于具有全局寻优问题的复杂控制系统优化,但系统稳定性和超调量很难达到人们的要求。和声搜索算法是一种启发式全局搜索算法,在有关问题上较遗传算法有更好的性能。基于以上原因,本文研究了和声搜索遗传算法,并对该算法进行了优化,将改进的和声搜索遗传算法用于六自由度机械臂的鲁棒控制器中,对控制器的参数进行优化。本文的主要研究内容如下。首先对机械臂系统进行了建模,基于矩阵分析的数学理论,求解出机械臂的正运动学方程和逆运动学方程,根据运动学方程和机械臂的物理学特性求解出机械臂的动力学方程。针对已经求得的机械臂动力学方程,设计匹配的鲁棒控制器实现对机械臂更精准、稳定控制。其次,为提高控制器的性能指标,引入改进的遗传算法对其参数进行优化,给出了整体的优化步骤。采用自适应交叉概率和变异概率的遗传算法能够更好地提升系统的灵活性。通过MATLAB进行仿真验证,证明了本方案能较好的提升控制系统的控制速率和稳态精度。最后,引入了和声搜索遗传算法,并对该算法进行了优化,将和声搜索算法的运算思维运用到遗传算法的初始群体产生过程中。给出了优化后的和声搜索遗传算法整体运算流程和运算思维,并通过测试函数对算法性能进行了测试。应用改进和声搜索遗传算法对机械臂控制器参数进一步优化。通过MATLAB仿真验证了该优化算法能有效提升机械臂控制器的控制效率、稳态精度和鲁棒性,并且能保证系统的超调量值保持在合适的范围内。
[Abstract]:The manipulator control system is a nonlinear system with multivariable, uncertain parameters, complex structure and strong coupling. Traditional controller is difficult to realize real-time and robust control of complex manipulator. Genetic algorithm has good convergence, real-time and robustness. It is widely used to optimize complex control system with global optimization problem. However, the system stability and overshoot are difficult to meet people's requirements. Harmony search algorithm is a heuristic global search algorithm, which has better performance than genetic algorithm in related problems. Based on the above reasons, this paper studies the genetic algorithm of harmonic search, and optimizes the algorithm. The improved genetic algorithm of harmony search is applied to the robust controller of a six-degree-of-freedom manipulator, and the parameters of the controller are optimized. The main contents of this paper are as follows. Based on the mathematical theory of matrix analysis, the forward and inverse kinematics equations of the manipulator are solved, and the dynamic equations of the manipulator are obtained according to the kinematics equation and the physical characteristics of the manipulator. According to the dynamic equations of manipulator, a matching robust controller is designed to control the manipulator more accurately and stably. Secondly, in order to improve the performance of the controller, an improved genetic algorithm is introduced to optimize its parameters, and the overall optimization steps are given. The genetic algorithm with adaptive crossover probability and mutation probability can improve the flexibility of the system. The simulation results of MATLAB show that the proposed scheme can improve the control rate and steady-state precision of the control system. Finally, the genetic algorithm of harmony search is introduced, and the algorithm is optimized. The operational thinking of harmony search algorithm is applied to the initial population generation process of genetic algorithm. The overall operation flow and thinking of the optimized harmony search genetic algorithm are given, and the performance of the algorithm is tested by testing function. The improved harmony search genetic algorithm is used to optimize the parameters of manipulator controller. The MATLAB simulation shows that the optimization algorithm can effectively improve the control efficiency, steady-state accuracy and robustness of the manipulator controller, and ensure that the overshoot value of the system is kept in a suitable range.
【学位授予单位】：哈尔滨理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP241;TP18

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