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.
Brushless DC motors are often used as the power sources for modern ship electric propulsion systems. Due to the electromagnetic torque ripple of the motor, the traditional control method reduces the drive performance of the motor under load changes. Aiming at the problem of the torque ripple of the DC brushless motor during a non- commutation period, this paper analysis the reasons for the torque ripple caused by pulse- width modulation (PWM), and proposes a PWM_ON_PWM method to suppress the torque ripple of the DC brushless motor. Based on the mathematical model of a DC brushless motor, this method adopts a double closed-loop control method based on fuzzy control to suppress the torque ripple of the DC brushless motor. The fuzzy control technology is integrated into the parameter tuning process of the proportional–integral–derivative (PID) controller to effectively improve the stability of the motor control system. Under the Matlab/Simulink platform, the response performance of different PID control methods and the torque characteristics of different PWM modulation methods are simulated and compared. The results show that the fuzzy adaptive PID control method has good dynamic response performance. It is verified that the PWM_ON_PWM modulation method can effectively suppress the torque ripple of the motor during non-commutation period, improve the stability of the double closed-loop control system and meet the driving performance of the motor under different load conditions.
This paper presents a development of a model of a set of multistage centrifugal electro pumps including two 4 stage stainless steel centrifugal pumps, each coupled to a 4 kW three-phase induction motor, connected to a hydraulic application running under two control strategies including constant speed and variable speed methods. Each pump provides 16 m3/hr flow rate and 58mwaterhead at BEP (Best Efficiency Point). Dynamicity of the model causes variations in all operational parameters of pumping system in any variation on consuming flow rate. Each electro pump has been driven with a variable frequency drive utilizing frequency control method for adjusting the rotational speed under a PID control regarding to match of pumping system operational point with the consumption point to save the energy. 83% energy saving is achieved by model in variable speed control strategy comparing to constant speed control strategy. MATLAB/SIMULINK software using ode45 solver and variable step size simulates this model.
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).