基于改进鲨鱼优化算法的自抗扰控制参数整定
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摘要
非线性自抗扰控制器耦合参数多,常规经验整定法难以获得最优参数,以至于影响控制器的控制精度.单一机制的优化算法整定出的自抗扰参数均可能是局部最优解,不能有效提高自抗扰控制器的控制精度.针对此问题,提出一种基于改进鲨鱼优化算法的自抗扰控制器参数优化设计方法.为解决基本鲨鱼优化算法易陷入局部最优解、算法后期收敛速度慢的问题,提出混合交叉变异策略与双种群协同机制,以ITAE指标为自抗扰控制器参数选择的优化目标,并以二自由度机械臂为例进行仿真验证.结果表明,优化后的自抗扰控制器具有更小的超调量和更高的控制精度,在加入外界干扰后,控制器可以很快抑制干扰,具有很好的抗干扰能力,改进后的鲨鱼优化算法可以用于复杂非线性系统自抗扰控制器的参数优化.
There are many coupling parameters in a nonlinear active disturbances rejection controller(ADRC), but the optimal parameters are di?cult to be obtained by the method of conventional empirical turning, which a?ects the control accuracy of the controller. A single-mechanism optimization algorithm is used to set the ADRC parameters that may be the local optimal solution, which can not e?ectively improve the control accuracy of the ADRC. For this problem,a parameter optimization design method based on the improved shark optimization algorithm is proposed. In order to solve the problem that the basic optimization algorithm is easy to fall into local optimum and converges slow, a hybrid cross mutation strategy and a double population co-evolution mechanism are proposed, which take the ITAE index as the optimization target of ADRC parameters selection and simulation with a two degrees of freedom manipulator as an example. The result shows that the optimized ADRC has less overshoot and higher control accuracy. After adding external interference, the controller can quickly suppress interference, so it has good anti-interference ability which can be used to optimize the parameters of the ADRC in complex nonlinear systems.
There are many coupling parameters in a nonlinear active disturbances rejection controller(ADRC), but the optimal parameters are di?cult to be obtained by the method of conventional empirical turning, which a?ects the control accuracy of the controller. A single-mechanism optimization algorithm is used to set the ADRC parameters that may be the local optimal solution, which can not e?ectively improve the control accuracy of the ADRC. For this problem,a parameter optimization design method based on the improved shark optimization algorithm is proposed. In order to solve the problem that the basic optimization algorithm is easy to fall into local optimum and converges slow, a hybrid cross mutation strategy and a double population co-evolution mechanism are proposed, which take the ITAE index as the optimization target of ADRC parameters selection and simulation with a two degrees of freedom manipulator as an example. The result shows that the optimized ADRC has less overshoot and higher control accuracy. After adding external interference, the controller can quickly suppress interference, so it has good anti-interference ability which can be used to optimize the parameters of the ADRC in complex nonlinear systems.
引文
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[2]Huang Y,Xue W.Active disturbance rejection control:Methodology and theoretical analysis[J].Isa Transactions,2014,53(4):963-976.
[3]Huang D,Zhang J,Liu Y,et al.Improved active disturbance rejection controller on suspension system and its performance analysis[J].Trans of the Chinese Society of Agricultural Engineering,2017,33(2):61-72.
[4]Chen Q,Li L,Wang M,et al.The precise modeling and active disturbance rejection control of voice coil motor in high precision motion control system[J].Applied Mathem-atical Modelling,2015,39(19):5936-5948.
[5]Shen Y,Shao K,Ren W,et al.Diving control of autonomous underwater vehicle based on improved active disturbance rejection control approach[J]Neurocomputing,2016,173:1377-1385.
[6]Yang X,Cui J,Lao D,et al.Input shaping enhanced active disturbance rejection control for a twin rotor multi-input multi-output system(TRMS)[J].Isa Transactions,201662:287-298.
[7]Mo R,Geng Q,Lu X.An active disturbance rejection controller design and parameter tuning for helicopter with slung-load[C].The 12th IEEE Int Conf on on Control and Automation.Kathmandu:IEEE,2016:242-247.
[8]Qing G,Shi-jie Z,Juan C.Parameter tuning of linear active disturbance rejection controller based on chaotic quantum behaved particle swarm optimization[C].The29th Chinese Control and Decision Conf.Chongqing:IEEE,2017:7484-7489.
[9]Ge Li-Ming,Li Zong-Gang,Wang S W,et al Parameter-tuning of active disturbance rejectioncontrol based on settling/observing time[J].Control and Decision,2017,32(7):1333-1337.
[10]Yin Z,Du C,Liu J,et al.Research on autodisturbance-rejection control of induction motors based on ant colony optimization algorithm[J].IEEE Trans on Industrial Electronics,2018,65(4):3077-3094.
[11]Yu Y,Wang H,Li N,et al.Automatic carrier landing system based on active disturbance rejection control with a novel parameters optimizer[J].Aerospace Science&Technology,2017,69:149-160.
[12]Wang P,Wang H,Bai G,et al.Parameter optimization of ADRC for spacecraft attitude maneuver based on particle swarm optimization algorithm[C].The 6th Int Conf on Intelligent Human-Machine Systems&Cybernetics Hangzhou:IEEE,2014:194-197.
[13]Liu Z H,Zhang Y J,Zhang J,et al.Active disturbance rejection control of a chaotic system based on immune binary-state particle swarm optimization algorithm[J]Acta Physica Sinica,2011,60(1):789-797.
[14]Abedinia O,Amjady N,Ghasemi A.A new metaheuristic algorithm based on shark smell optimization[J]Complexity,2016,21(5):97-116.
[15]Cheng J,Rong-Jun Li.Particle swarm optimization based on immune evasion[J].Mathematics in Practice and Theory,2014,43(4):509-513.
[16]Qin Q,Niu B,Li Li,et al.A Double-population particle swarm optimization algorithm based on predator-prey behavior[J].Information&Control,2011,40(6):733-739.
[17]Liu F C,Jia Y F,Ren L N.Anti-synchronizing different chaotic systems using active disturbance rejection controller based on the chaos particle swarm optimization algorithm[J].Acta Physica Sinica,2013,62(12):98-105
[18]Heck D,Saccon A,Wouw N V D,et al.Guaranteeing stable tracking of hybrid position-force trajectories for a robot manipulator interacting with a stiff environment[J]Automatica,2016,63:235-247.
[19]Mathew R,Hiremath S S.Trajectory tracking and control of differential drive robot for predefined regular geometrical path[J].Procedia Technology,2016,25:1273-1280.