An adaptive robust control scheme for robot manipulators with unknown backlash nonlinearity in gears

2018 ◽  
Vol 41 (10) ◽  
pp. 2789-2802 ◽  
Author(s):  
Soheil Ahangarian Abhari ◽  
Farzad Hashemzadeh ◽  
Mahdi Baradarannia ◽  
Hamed Kharrati
Keyword(s):  
Robust Control ◽  
Controller Design ◽  
Robot Manipulators ◽  
Dead Zone ◽  
Main Idea ◽  
Adaptive Robust Control ◽  
Zone Model ◽  
Robot Trajectory ◽  
Zone Parameter ◽  
The Stability

This paper presents an adaptive robust control algorithm for the nonlinear dynamics of robot manipulators with unknown backlash in gears. The basic nonlinear model of a serial manipulator robot is used for the controller design, and this is combined with the nonlinear proposed dead zone model, based on the input and output torque. The main idea of providing this model is to achieve a dynamic model of the system considering the backlash of the robot joint gears, and having less complexity such that the developed controller does not need the inverse backlash model. The adaptive robust controller is developed, without using the inverse backlash model. The proposed controller is designed based on an unknown dead zone parameter and it guarantees the stability and path tracking of the robot trajectory with unknown dead zone parameter in the desired range. Numerical simulations are conducted to show the effectiveness of the proposed controller. Finally, the efficiency and capability of the proposed controller in dealing with the unknown backlash nonlinearities in gears of the manipulator are demonstrated by experimental results with a five-bar manipulator.

A computationally efficient adaptive robust control scheme for a quad-rotor transporting cable-suspended payloads

2021 ◽  
pp. 095441002110136
Author(s):  
Nasim Ullah ◽  
Irfan Sami ◽  
Wang Shaoping ◽  
Hamid Mukhtar ◽  
Xingjian Wang ◽  
...  
Keyword(s):  
Robust Control ◽  
Fractional Order ◽  
Sliding Mode ◽  
Adaptive Robust Control ◽  
Computationally Efficient ◽  
Control Scheme ◽  
Control Schemes ◽  
Adaptive Laws ◽  
Unknown Disturbances ◽  
The Stability

This article proposes a computationally efficient adaptive robust control scheme for a quad-rotor with cable-suspended payloads. Motion of payload introduces unknown disturbances that affect the performance of the quad-rotor controlled with conventional schemes, thus novel adaptive robust controllers with both integer- and fractional-order dynamics are proposed for the trajectory tracking of quad-rotor with cable-suspended payload. The disturbances acting on quad-rotor due to the payload motion are estimated by utilizing adaptive laws derived from integer- and fractional-order Lyapunov functions. The stability of the proposed control systems is guaranteed using integer- and fractional-order Lyapunov theorems. Overall, three variants of the control schemes, namely adaptive fractional-order sliding mode (AFSMC), adaptive sliding mode (ASMC), and classical Sliding mode controllers (SMC)s) are tested using processor in the loop experiments, and based on the two performance indicators, namely robustness and computational resource utilization, the best control scheme is evaluated. From the results presented, it is verified that ASMC scheme exhibits comparable robustness as of SMC and AFSMC, while it utilizes less sources as compared to AFSMC.

Uniform robust exact differentiator based adaptive robust control for a class of nonlinear systems

2017 ◽  
Vol 40 (9) ◽  
pp. 2901-2911 ◽  
Author(s):  
Zhangbao Xu ◽  
Dawei Ma ◽  
Jianyong Yao
Keyword(s):  
Robust Control ◽  
Nonlinear Systems ◽  
Closed Loop ◽  
Lyapunov Method ◽  
Adaptive Robust Control ◽  
Experimental Results ◽  
Closed Loop System ◽  
Robust Controller ◽  
Performance Simulation ◽  
The Stability

In this paper, an adaptive robust controller with uniform robust exact differentiator has been proposed for a class of nonlinear systems with structured and unstructured uncertainties. The adaptive robust controller is integrated with an uniform robust differentiator to handle the problem of the incalculable part of the derivative of virtual controls and the differential explosion happened in backstepping techniques. The stability of the closed loop system is demonstrated via Lyapunov method ensuring a prescribed transient and tracking performance. Simulation and experimental results are carried out to verify the advantages of the proposed method.

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.

scholarly journals Adaptive robust control for free-floating space robot with unknown uncertainty based on neural network

2018 ◽  
Vol 15 (6) ◽  
pp. 172988141881151
Author(s):  
Zhang Wenhui ◽  
Li Hongsheng ◽  
Ye Xiaoping ◽  
Huang Jiacai ◽  
Huo Mingying
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Robust Control ◽  
Control Method ◽  
External Disturbance ◽  
Adaptive Robust Control ◽  
Lyapunov Theory ◽  
Space Robot ◽  
Neural Network Controller ◽  
The Stability

It is difficult to obtain a precise mathematical model of free-floating space robot for the uncertain factors, such as current measurement technology and external disturbance. Hence, a suitable solution would be an adaptive robust control method based on neural network is proposed for free-floating space robot. The dynamic model of free-floating space robot is established; a computed torque controller based on exact model is designed, and the controller can guarantee the stability of the system. However, in practice, the mathematical model of the system cannot be accurately obtained. Therefore, a neural network controller is proposed to approximate the unknown model in the system, so that the controller avoids dependence on mathematical models. The adaptive learning laws of weights are designed to realize online real-time adjustment. The adaptive robust controller is designed to suppress the external disturbance and compensate the approximation error and improve the robustness and control precision of the system. The stability of closed-loop system is proved based on Lyapunov theory. Simulations tests verify the effectiveness of the proposed control method and are of great significance to free-floating space robot.

Rule Reduction and Robust Control of Generalized Takagi-Sugeno Fuzzy Systems

2000 ◽  
Vol 4 (5) ◽  
pp. 373-379 ◽  
Author(s):  
Tadanari Taniguchi ◽  
◽  
Kazuo Tanaka ◽  
Keyword(s):  
Robust Control ◽  
Model Reduction ◽  
Fuzzy System ◽  
Fuzzy Systems ◽  
Controller Design ◽  
Main Idea ◽  
Linear Matrix ◽  
Robust Controller ◽  
Reduction Error ◽  
Takagi Sugeno

This paper presents model reduction and robust control using a generalized form of Takagi-Sugeno fuzzy systems. We first define a generalized form of TakagiSugeno fuzzy systems. The generalized form has a decomposed structure for each element of <I>Ai</I> and <I>Bi</I> matrices in consequent parts. The key feature of this structure is that it is suitable for reducing the number of rules. Conditions to reduce the number of rules are represented in terms of linear matrix inequality (LMIs). The main idea is to find a structure of if-then rules of the reduced model that agrees well with dynamics of the original model. Furthermore, we estimate the lower bound of the norm of model uncertainty of the Takagi-Sugeno fuzzy system that can cover the reduction error. Finally, we present an example of model reduction and robust control for a nonlinear system. In this example, we achieve a robust controller design to compensate for the uncertainly of the Takagi-Sugeno fuzzy system.

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