The comparison of optimization for active steering control on vehicle using PID controller based on artificial intelligence techniques
The comparison of optimization for active steering control on vehicle using PID controller based on artificial intelligence techniques
复制标题
基于人工智能技术的PID控制器对车辆主动转向控制的优化比较
DOI：
10.1109/isemantic.2016.7873803
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
N. Sutantra
中科院分区：
文献类型：
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作者：
Dwi Hendra Kusuma;Machrus Ali;N. Sutantra
比较了几种应用人工智能(AI)优化车辆转向控制系统仿真的方法，以优化比例积分导数(PID)控制参数，以抑制车辆横向运动和偏航运动的误差。比较了萤火虫算法(FA)、粒子群算法(PSO)、蚁群算法(ACO)、蝙蝠算法(BA)和帝国主义竞争算法(ICA)等五种参数整定方法。将车辆表示为具有10个自由度的车辆动力学系统的模型。仿真结果表明，在车辆转向控制系统中，人工智能整定的PID控制可以将被控对象的输出调节到期望的轨迹，从而保持车辆的稳定性。利用独立分量分析来整定PID参数，可以减小车辆的横摇误差和横向误差。所提出的优化方法的主要优点是速度更快、精度更高。从而减小了控制器的误差。得到的结果是，车辆的运动能够以较小的误差保持在期望的轨迹上，并且能够达到比采用无参数优化的控制系统更高的速度。本文仅通过软件仿真来验证人工智能优化的效果。硬件实现将在未来进行研究。
This paper presents a comparison of optimization for vehicle steering controls system simulation using several Artificial Intelligence (AI) for optimizing Proportional Integral Derivative (PID) control parameters to suppress errors on lateral motion and the yaw motion of vehicles. This paper compares five kinds of tuning methods of parameter for PID controller, among other are Firefly Algorithm (FA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Bat Algorithm (BA) and Imperialist Competitive Algorithm (ICA). The vehicles are represented in the model vehicle with 10 degrees of Freedom of vehicle dynamics system. The simulation results show that the PID control tuned by AI in the vehicle steering control system can adjust the plant output to the desired trajectory so that the stability of the vehicle is maintained. Vehicle yaw error and lateral error can be reduced by using ICA to determine PID parameter. The main advantage of proposed optimization is faster and more accurate compared with PID controller. So the error of the controller is reduced too. The results obtained are of vehicle motion can be maintained in accordance with the desired trajectory with smaller error and was able to achieve higher speeds than with the control system using optimized without parameters. This paper only deals with software simulation to proof the effect of AI optimization. The hardware implementation will be investigated in the next future.