This paper presents the simulation of vehicle steering control system using the Imperialist Competitive Algorithm (ICA) for optimizing Proportional Integral Derivative (PID) control parameters to suppress errors on lateral motion and the yaw motion of vehicles. 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 the ICA 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 standard PID controller. And then 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 ICA-PID optimization. The hardware implementation will be investigated in the next future.

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This paper presents a simulation of the automatic steering control system on a vehicle model using particle swarm optimization (PSO) to optimize the parameters of the control system. The control system involves fuzzy logic control (FLC) and proportional-integral-derivative (PID) control working in a cascade; the main control (FLC) is used to control lateral motion, and the secondary control (PID) is an enhancement to control the yaw motion in vehicle models representing 10 degrees of freedom of the vehicle dynamics system. Optimization by PSO is carried out simultaneously on both control systems. On FLC it is done by setting the width and the center point of the membership function (MF) in the input and output FLC so that the optimal composition of the MF parameter is obtained. The optimization process also determines the constants of optimal gain in the PID control. Testing is done through software in the loop simulation. Based on the test results it can be stated that FLC and PID control tuned by PSO can steer the vehicle rate well in accordance with desired trajectory and the vehicle motion can always be maintained at the specified path.

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory