基于粒子群算法的直升机控制器设计

发布时间：2018-07-06 14:29

本文选题：粒子群算法 + ADS-33　；参考：《南昌航空大学》2015年硕士论文

【摘要】：直升机存在振动大、操纵难、稳定性差等问题,为安全有效地控制直升机,必须设计性能优良的控制系统。传统的控制方法在控制参数的选取中,过于依赖于设计人员的经验,文中采用粒子群算法对控制参数进行智能化的寻优设计。为了提高粒子群算法的搜索效率,文中提出一种改进的粒子群算法,采用该改进的粒子群算法设计直升机显模型跟踪控制器和线性二次型控制器,开展了如下研究工作:1、分析直升机的受力和力矩,依据牛顿运动定律得出了直升机的六自由度运动方程,利用在平衡点附近增加小扰动的方法对六自由度方程进行线性化处理,得到直升机9阶线性化模型;2、考虑基本粒子群算法易陷入局部极小值的不足,研究基本粒子群算法中的惯性权重和学习因子的选取后,提出一种自适应变换惯性权重和学习因子的改进方法,运用Schwefel等函数验证,该方法具有良好的收敛速度和搜索精度;3、在直升机显模型跟踪控制系统设计中,首先根据ADS-33品质中悬停和低速飞行状态下对直升机性能指标的要求选择直升机4个通道的显模型,然后以跟踪误差最小为目标,采用粒子群算法对显模型跟踪控制系统中的积分矩阵和前向增益矩阵进行优化。仿真结果表明,系统的跟踪性、解耦性和鲁棒性均达到了理想的效果;4、在直升机线性二次型控制中,先针对二次型控制要求对直升机进行能控性分析,设计出LQR控制器,然后以二次型最优指标为目标,采用粒子群算法对Q和R矩阵进行优化,仿真结果表明,系统的误差与鲁棒性均达到了理想的效果。通过粒子群算法对直升机控制器进行优化设计,相比传统的控制器设计方法,该方法提高了设计的效率,同时又能保证系统的性能要求,为控制器的设计开辟了一条新的途径。
[Abstract]:In order to control the helicopter safely and effectively, it is necessary to design a control system with good performance. The traditional control method is too dependent on the designer's experience in the selection of control parameters. In this paper, the particle swarm optimization algorithm is used to design the control parameters intelligently. In order to improve the searching efficiency of particle swarm optimization algorithm, an improved particle swarm optimization algorithm is proposed in this paper. The improved particle swarm optimization algorithm is used to design helicopter explicit model tracking controller and linear quadratic controller. The following research work is carried out: 1, the force and torque of the helicopter are analyzed, according to Newton's law of motion, the motion equation of the helicopter with six degrees of freedom is obtained, and the equation of six degrees of freedom is linearized by adding a small disturbance near the equilibrium point. The 9 th order linearization model of helicopter is obtained. Considering the deficiency of elementary particle swarm optimization algorithm which is easy to fall into local minimum, the selection of inertia weight and learning factor in basic particle swarm optimization algorithm is studied. An improved method for adaptive transformation of inertia weight and learning factor is proposed. The method is verified by Schwefel and other functions. The method has good convergence speed and searching precision. It is used in the design of helicopter model tracking control system. Firstly, according to the requirement of helicopter performance index in hover and low speed flight state of ADS-33, the explicit model of four channels of helicopter is selected, and then the minimum tracking error is taken as the target. Particle swarm optimization (PSO) is used to optimize the integral matrix and the forward gain matrix in the explicit model tracking control system. The simulation results show that the tracking, decoupling and robustness of the system are satisfactory. In the linear quadratic control of the helicopter, the controllability of the helicopter is analyzed according to the requirements of the quadratic control, and the LQR controller is designed. Then the quadratic optimal index is used to optimize the Q and R matrices. The simulation results show that the error and robustness of the system are satisfactory. The particle swarm optimization algorithm is used to optimize the design of helicopter controller. Compared with the traditional controller design method, this method can improve the efficiency of the design and at the same time ensure the performance of the system. It opens a new way for the design of the controller.
【学位授予单位】：南昌航空大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：V249.12;TP18

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