Abstract
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.
Highlights
Hydraulic servo valve systems (HSS), which produce high torque and large forces with high speeds, are the critical components of the industrial field
An intelligent hysteresis model was proposed based on the learning capability of least-squares support-vector machine (LS-support-vector machines (SVMs)) for open-loop feed-forward controlling of servo-hydraulic actuators
A feed-forward-trained LS-SVM model is used in place of a feedback controller (PID controller)
Summary
Hydraulic servo valve systems (HSS), which produce high torque and large forces with high speeds, are the critical components of the industrial field. The control scheme consists of feedback control and feed-forward control In this control strategy, employing optimal control theory, PID parameters were tuned; the least-squares support-vector machine was utilized as a function approximator to model the inverse dynamics of the inverter. Sharghi et al [14] predicted the highly nonlinear hysteresis behavior of a magnetorheological damper (MR Damper) using the LS-SVM model They aimed to overcome the hybrid simulation constraints in the field of experimental studies, so they employed the least-squares support-vector machine model as an alternative to the rate-dependent physical substructure. The current study aims to evaluate the performance of the LS-SVM hysteresis model in an open-loop feed-forward hysteresis control For this purpose, first, a numerical model of a PID-controlled actuator was created, and sets of numerical data of inputs and outputs of the plant were generated.
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