Adaptive Robust Position Control of Electro-Hydraulic Servo Systems with Large Uncertainties and Disturbances

In this paper, a novel adaptive robust control (ARC) scheme is proposed for electro-hydraulic servo systems (EHSSs) with uncertainties and disturbances. All dynamic functions in system dynamics are effectively approximated by multi-layer radial basis function neural network (RBF NN)-based approximators with online adaptive mechanisms. Moreover, neural network-based disturbance observers (NN-DOBs) are established to actively estimate and efficiently compensate for the effects of not only the matched/mismatched but also the imperfections of RBF NN-based approximators on the control system. Based on that, the nonlinear robust control law which integrates RBF NNs and NN-DOBs is synthesized via the sliding mode control (SMC) approach to guarantee the high-accuracy position tracking performance of the overall control system. Furthermore, the problem of the combination between DOBs and RBF NNs is first introduced in this paper to treat both disturbances and uncertainties in the EHSS. The stability of the recommended control mechanism is proven by using Lyapunov theory. Finally, numerical simulations with several distinct frequency levels of reference trajectory are conducted to convincingly demonstrate the effectiveness of the proposed approach.

Discovering Stick-Slip-Resistant Servo Control Algorithm Using Genetic Programming

The stick-slip is one of negative phenomena caused by friction in servo systems. It is a consequence of complicated nonlinear friction characteristics, especially the so-called Stribeck effect. Much research has been done on control algorithms suppressing the stick-slip, but no simple solution has been found. In this work, a new approach is proposed based on genetic programming. The genetic programming is a machine learning technique constructing symbolic representation of programs or expressions by evolutionary process. In this way, the servo control algorithm optimally suppressing the stick-slip is discovered. The GP training is conducted on a simulated servo system, as the experiments would last too long in real-time. The feedback for the control algorithm is based on the sensors of position, velocity and acceleration. Variants with full and reduced sensor sets are considered. Ideal and quantized position measurements are also analyzed. The results reveal that the genetic programming can successfully discover a control algorithm effectively suppressing the stick-slip. However, it is not an easy task and relatively large size of population and a big number of generations are required. Real measurement results in worse control quality. Acceleration feedback has no apparent impact on the algorithms performance, while velocity feedback is important.

Output-Feedback Position Tracking Servo System with Feedback Gain Learning Mechanism via Order-Reduction Speed-Error-Stabilization Approach

This paper presents a novel trajectory-tracking technique for servo systems treating only the position measurement as the output subject to practical concerns: system parameter and load uncertainties. There are two main contributions: (a) the use of observers without system parameter information for estimating the position reference derivative and speed and acceleration errors and (b) an order reduction exponential speed error stabilizer via active damping injection to enable the application of a feedback-gain-learning position-tracking action. A hardware configuration using a QUBE-servo2 and myRIO-1900 experimentally validates the closed-loop improvement under various scenarios.

Finite-Time Output Feedback Control for Electro-Hydraulic Servo Systems with Parameter Adaptation

Measurement noise, parametric uncertainties, and external disturbances broadly exist in electro-hydraulic servo systems, which terribly deteriorate the system control performance. To figure out this problem, a novel finite-time output feedback controller with parameter adaptation is proposed for electro-hydraulic servo systems in this paper. First, to avoid using noise-polluted signals and attain active disturbance compensation, a finite-time state observer is adopted to estimate unknown system states and disturbances, which attenuates the impact of measurement noise and external disturbances on tracking performance. Second, by adopting a parameter adaptive law, the parametric uncertainties in the electro-hydraulic servo system can be much lessened, which is beneficial to averting the high-gain feedback in practice. Then, integrating the backstepping framework and the super-twisting sliding mode technique, a synthesized output feedback controller is constructed to achieve high-accuracy tracking performance for electro-hydraulic servo systems. Lyapunov stability analysis demonstrates that the proposed control scheme can acquire finite-time stability. The excellent tracking performance of the designed control law is verified by comparative simulation results.