An electric power steering system (EPS) is a new type of steering system developed after a mechanical hydraulic power system (MHPS) and electric-hydraulic power steering system (EHPS). In order to coordinate and solve the portability and sensitivity of the steering system optimally, taking an induction power steering system as the research object, the control algorithm of induction motor control under the EPS is studied in this paper. In order to eliminate the feed-forward performance degradation caused by the change of feed-forward parameters, an on-line identification algorithm of feed-forward parameters is proposed. It can improve the control performance of online identification among three feed-forward parameters in the T-axle motor, it improves on the robustness of feed-forward control performance, at the same time it also gives simulation and test results. This method can improve the control performance of the three feed-forward parameter online identification of the T-axis motor and improve the robustness of feed-forward control performance. At the same time, simulation and test results are given. The simulation results show that the algorithm can significantly improve the response speed and control accuracy of EPS system control.
The proportional-integral-derivative (PID) controller is widely used in various industrial applications such as process control, motor drives, magnetic and optical memory, automotive, flight control and instrumentation. PID tuning refers to the generation of PID parameters (Kp, Ki, Kd) to obtain the optimum fitness value for any system. The determination of the PID parameters is essential for any system that relies on it to function in a stable mode. This paper proposes a method in designing a predictive PID controller system using particle swarm optimization (PSO) algorithm for direct current (DC) motor application. Extensive numerical simulations have been done using the Mathwork’s Matlab simulation environment. In order to gain full benefits from the PSO algorithm, the PSO parameters such as inertia weight, iteration number, acceleration constant and particle number need to be carefully adjusted and determined. Therefore, the first investigation of this study is to present a comparative analysis between two important PSO parameters; inertia weight and number of iteration, to assist the predictive PID controller design. Simulation results show that inertia weight of 0.9 and iteration number 100 provide a good fitness achievement with low overshoot and fast rise and settling time. Next, a comparison between the performance of the DC motor with PID-PSO, with PID of gain 1, and without PID were also discussed. From the analysis, it can be concluded that by tuning the PID parameters using PSO method, the best gain in performance may be found. Finally, when comparing between the PID-PSO and its counterpart, the PI-PSO, the PID-PSO controller gives better performance in terms of robustness, low overshoot (0.005%), low minimum rise time (0.2806 seconds) and low settling time (0.4326 seconds).