Feed-forward control of hysteretic systems is a challenging task due to the hysteresis nonlinearity. Hysteresis models are utilized not only for identification, but also for hysteresis control. The feed-forward control, which is not an error-based (feedback-based) algorithm, plays a significant role in hysteresis control problems. Instead, it works based on knowledge about the process in the form of a mathematical model of the process. In feed-forward control problems, it is important to identify the inverse relationship of the output and input of the system, i.e., determining the mapping of the output and input of the system plays a key role in feed-forward controlling. This paper presents a new feed-forward controller model to control an actuator in a laboratory to tackle the restrictions of feedback control systems. For this purpose, first, a numerical model of a Proportional-Integral-Derivative (PID)-controlled actuator was created, and sets of numerical data of inputs and outputs of the plant were generated. Then, a least-squares support-vector machine (LS-SVM) hysteresis model was trained inversely on the generated data sets of the numerical modeling. Afterwards, to examine the efficacy of the proposed method for real-world hydraulic actuators in the presence of experimental errors and noise, sets of experimental data were obtained from physical modeling at KNTU’s Structural and Earthquake Engineering Laboratory (KSEEL). The results indicate the high performance of the proposed model.
Research on Speed Sensorless Vector Control System of Asynchronous Motor Based on MRAS
