Trajectory control of a hydraulic system using intelligent control approach based on adaptive prediction model

Electro-hydraulic actuators (EHAs) have become a preferred alternative to traditional hydraulic actuators with valve control systems due to their numerous advantages, making them an ideal choice for applications requiring high-precision force or position control. However, the highly complex nonlinear nature of EHAs makes modelling and controlling them a challenging task. To address this challenge, a new position control approach has been proposed for an EHA system using a combination of a feedforward online-tuning PID (FOPID) controller and an adaptive grey predictor (AGP), known as the feedforward online-tuning adaptive grey predictor (FOAGP). The FOPID controller is constructed based on PID controller and fuzzy logic algorithm to control the EHA system towards referred trajectory, while an updating rule that consists of robust checking terms optimizes its parameters online to minimize control error. The AGP predictor is an important aspect of the proposed approach. It consists of a self-tuning step size mechanism, which estimates the performance of the plant to tune the parameters of the controller and create an additive control signal that is used to counteract environment noises and perturbations. This approach significantly improves control performance by reducing the effect of disturbances and sensor noises on the system. The FOAGP approach was tested in simulation to investigate its effectiveness. The results showed that the proposed approach outperformed other existing control methods, with a higher accuracy and better control performance. One of the significant advantages of the FOAGP approach is its ability to learn and adapt to changing system dynamics. The learning mechanism used in the FOPID controller allows the system to optimize its parameters online, which is especially useful in systems with varying operating conditions. The AGP predictor also continuously adjusts its parameters to accurately estimate the system output, making it an effective tool for controlling EHAs. The proposed approach offers a significant improvement in control performance, making it a better alternative to traditional control methods. This approach can be applied to various EHA systems, including those used in aerospace, automobile, and robotic applications, among others.