United Institute of Excavator Key Technology, Nanjing Tech University, Nanjing 211816, China; Institute of Automobile and Construction Machinery, Nanjing Tech University, Nanjing 211816, China.
United Institute of Excavator Key Technology, Nanjing Tech University, Nanjing 211816, China; Institute of Automobile and Construction Machinery, Nanjing Tech University, Nanjing 211816, China.
ISA Trans. 2019 Sep;92:228-240. doi: 10.1016/j.isatra.2019.02.022. Epub 2019 Feb 23.
In order to improve the tracking accuracy of a hydraulic system, an improved ant colony optimization algorithm (IACO) is proposed to optimize the values of proportional-integral-derivative (PID) controller. In addition, this paper presents an experimental study on the parameters identification to deduce accurate numerical values of the hydraulic system, which also determines the relationship between control signal and output displacement. Firstly, the basic principle of the hydraulic system and the mathematical model of the electro-hydraulic proportional control system are analyzed. Based on the theoretical models, the transfer function of the control system is obtained by recursive least square identification method (RLS). Then, the key parameters of the control system model are obtained. Some improvements are proposed to avoid premature convergence and slow convergence rate of ACO: the transition probability is revised based adjacent search mechanism, dynamic pheromone evaporation coefficient adjustment strategy is adopted, pheromone update rule and parameters optimization range are also improved. Then the proposed IACO tuning based PID controller and the identification parameters are modeled and simulated using MATLAB/Simulink and AMESim co-simulation platform. Comparisons of IACO, standard ACO and Ziegler-Nichols (Z-N)PID controllers are carried out with different references as step signal and sinusoidal wave using the co-simulation platform. The simulation results of the bucket system using the proposed controller demonstrates improved settling time, rise time and the convergence speed with a new objective function J. Finally, experiments with leveling operations are performed on a 23 ton robotic excavator. The experimental results show that the proposed controller improves the trajectory accuracy of the leveling operation by 28% in comparison to the standard ACO-PID controller.
为了提高液压系统的跟踪精度，提出了一种改进的蚁群优化算法（IACO）来优化比例积分微分（PID）控制器的值。此外，本文还进行了参数辨识的实验研究，以推导出液压系统的准确数值，从而确定控制信号与输出位移之间的关系。首先，分析了液压系统的基本原理和电液比例控制系统的数学模型。基于理论模型，通过递推最小二乘法识别方法（RLS）获得控制系统的传递函数。然后，获得控制系统模型的关键参数。针对蚁群算法（ACO）易陷入局部最优和收敛速度慢的问题，提出了一些改进措施：基于邻域搜索机制修正转移概率，采用动态信息素挥发系数调整策略，改进信息素更新规则和参数优化范围。然后，使用 MATLAB/Simulink 和 AMESim 联合仿真平台对基于 IACO 整定的 PID 控制器和辨识参数进行建模和仿真。使用联合仿真平台，针对不同参考输入（阶跃信号和正弦波），对 IACO、标准 ACO 和 Ziegler-Nichols（Z-N）PID 控制器进行了对比。利用所提出的控制器对铲斗系统进行了仿真，结果表明，与标准 ACO-PID 控制器相比，新的目标函数 J 可以改善系统的稳定时间、上升时间和收敛速度。最后，在 23 吨机器人挖掘机上进行了调平操作实验。实验结果表明，与标准 ACO-PID 控制器相比，所提出的控制器可以将调平操作的轨迹精度提高 28%。
