High-performance robust motion control of machine tools: an adaptive robust control approach and comparative experiments

1997 ◽  
Vol 2 (2) ◽  
pp. 63-76 ◽  
Author(s):  
Bin Yao ◽  
M. Al-Majed ◽  
M. Tomizuka
Keyword(s):  
Robust Control ◽  
Motion Control ◽  
Machine Tools ◽  
High Performance ◽  
Adaptive Robust Control ◽  
Control Approach ◽  
Robust Motion Control

scholarly journals Simplified Adaptive Robust Motion Control with Varying Boundary Discontinuous Projection of Hydraulic Actuator

2014 ◽  
Vol 2014 ◽  
pp. 1-10
Author(s):  
Cungui Yu ◽  
Xianwei Qi
Keyword(s):  
Robust Control ◽  
Motion Control ◽  
High Performance ◽  
Simple Algorithm ◽  
Adaptive Robust Control ◽  
Hydraulic Systems ◽  
Hydraulic Actuator ◽  
Tracking Accuracy ◽  
Rotary Actuator ◽  
Robust Motion Control

This paper deals with the high performance adaptive robust motion control of electrohydraulic servo system driven by dual vane hydraulic rotary actuator. The recently developed adaptive robust control theory is used to handle the nonlinearities and modelling uncertainties in hydraulic systems. Aside from the difficulty of handling parametric variations, the traditional adaptive robust controller (ARC) is also a little complicated in practice. To address these challenging issues, a simplified adaptive robust control with varying boundary discontinuous projection is developed to enhance the robustness of the closed-loop system, based on the features of hydraulic rotary actuator. Compared with previous ARC controller, the resulting controller has a simple algorithm for more suitable implementation and can handle parametric variations via nonlinear robust design. The controller theoretically achieves a guaranteed transient performance and final tracking accuracy in the presence of both parametric uncertainties and uncertain nonlinearities. Extensive simulation results are obtained for a hydraulic rotary actuator to verify the high performance nature of proposed control strategy.

Wavelet Neural Network Based Adaptive Robust Control for a Class of MIMO Nonlinear Systems

2011 ◽  
Vol 383-390 ◽  
pp. 290-296
Author(s):  
Yong Hong Zhu ◽  
Wen Zhong Gao
Keyword(s):  
Neural Network ◽  
Robust Control ◽  
Nonlinear Systems ◽  
Wavelet Neural Network ◽  
Adaptive Robust Control ◽  
Loop Control ◽  
Control Approach ◽  
Mimo Nonlinear Systems ◽  
Output Tracking Control ◽  
Dynamic Inverse

Wavelet neural network based adaptive robust output tracking control approach is proposed for a class of MIMO nonlinear systems with unknown nonlinearities in this paper. A wavelet network is constructed as an alternative to a neural network to approximate unknown nonlinearities of the classes of systems. The proposed WNN adaptive law is used to compensate the dynamic inverse errors of the classes of systems. The robust control law is designed to attenuate the effects of approximate errors and external disturbances. It is proved that the controller proposed can guarantee that all the signals in the closed-loop control system are uniformly ultimately bounded (UUB) in the sense of Lyapunov. In the end, a simulation example is presented to illustrate the effectiveness and the applicability of the suggested method.

Stable and High Performance Energy-Efficient Motion Control of Electric Load Sensing Controlled Hydraulic Manipulators

2013 ◽  
Author(s):  
Janne Koivumäki ◽  
Jouni Mattila
Keyword(s):  
Motion Control ◽  
Energy Efficient ◽  
High Performance ◽  
System Stability ◽  
Electric Load ◽  
Hydraulic Systems ◽  
Control Approach ◽  
Load Sensing ◽  
Hydraulic Manipulators ◽  
Control Scheme

In order to achieve higher energy efficiency for hydraulic systems the Load Sensing (LS) systems, i.e. a Variable Displacement Pump (VDP) with hydro-mechanical control system, can be considered as a state-of-the-art solution. However, as is well known, these traditional hydraulic LS-systems are usually characterized by difficulties in tuning, which can lead to system stability problems. In our previous studies, we have developed a high precision motion control for hydraulic manipulators with separate meter-in meter-out controlled hydraulic actuators. Our control approach was based on the Virtual Decomposition Control (VDC) approach that ensured high motion tracking performance while rigorously guaranteeing the system stability. In this paper, we propose both energy-efficient and high performance nonlinear model based motion control scheme that utilizes the developed servocontrolled Electric Load Sensing (ELS) system for hydraulic robotic manipulators. Experimental results are presented with the proposed ELS-controlled VDP and hydraulic manipulator lifting servoactuator that utilized a separate meter-in meter-out flow control scheme.

scholarly journals Adaptive Robust Control for Networked Strict-Feedback Nonlinear Systems with State and Input Quantization

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2783
Author(s):  
Yanbin Liu ◽  
Jue Wang ◽  
Luis Gomes ◽  
Weichao Sun
Keyword(s):  
Robust Control ◽  
Nonlinear Systems ◽  
Networked Control Systems ◽  
Tracking Error ◽  
Adaptive Robust Control ◽  
Feedback Form ◽  
Control Approach ◽  
Input Quantization ◽  
The Stability ◽  
Strict Feedback

Backstepping method is a successful approach to deal with the systems in strict-feedback form. However, for networked control systems, the discontinuous virtual law caused by state quantization introduces huge challenges for its applicability. In this article, a quantized adaptive robust control approach in backsetpping framework is developed in this article for networked strict-feedback nonlinear systems with both state and input quantization. In order to prove the efficiency of the designed control scheme, a novel form of Lyapunov candidate function was constructed in the process of analyzing the stability, which is applicable for the systems with nondifferentiable virtual control law. In particular, the state and input quantizers can be in any form as long as they meet the sector-bound condition. The theoretic result shows that the tracking error is determined by the pregiven constants and quantization errors, which are also verified by the simulation results.

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