System Identification And Control Of Automatic Car Pedal Pressing System For Low-Speed Driving In A Road Traffic Delay - Lai, Chong Jin (2022)
Congestion in major cities has been a typical occurrence among communities. Being stuck in traffic for hours in a sitting position necessitates recurrent chores of manually pressing the accelerator and stop pedals excessively, which, if performed without the proper sitting posture, can result in quick weariness, especially for the driver's leg and back. Therefore, this research is to automate the pressing mechanism by modelling an actuator for pedals pressing concentrating for low-speed driving in a road traffic delay. This project presents system identification and control of automatic car pedal pressing system for low-speed driving in a road traffic delay. Two parameters such as force and low speed will be observed for both controllers and the ability to attenuate disturbance will be simulated. Output of the controller will determine the force of the car brake. The parallel linkage is connected to the actuator such that it provides an opposite reaction to the car pedal. The system identification is taken from the dynamic behavior of the system and modelled using input-output data acquired directly from the experimental rig. The work utilized a neural network to model the system as the system exhibits highly nonlinear behavior. This paper compares the low-speed control with both controllers: conventional PID and fuzzy logic controller with respect to overshoot and steady state error. A PID controller and a Fuzzy controller were designed, simulated, and compared in their ability to control the speed of the car. Both controllers were then implemented and tested in automatic car pedal pressing system. The controller gains were tuned using metaheuristic algorithm which is Particle Swarm Algorithm (PSO) for optimal values of fuzzy controller parameters. The controller parameters are optimized based on Integral Squared Error (ISE), Integral Absolute Error (IAE), Integral Time Absolute Error (ITAE) and Mean Squared Error) MSE. The comparative assessment of both controllers was reported and discussed. It shown that Fuzzy logic controller performs better than PID controller with 1.2724% reduction in steady state error and 3.64% in overshoot respectively.