scholarly journals Dynamic learning from adaptive neural control for flexible joint robot with tracking error constraints using high-gain observer

2018 ◽  
Vol 6 (3) ◽  
pp. 177-190
Author(s):  
Zhiguang Chen ◽  
Min Wang ◽  
Yongtao Zou
Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Min Wang ◽  
Yanwen Zhang ◽  
Huiping Ye

A dynamic learning method is developed for an uncertain n-link robot with unknown system dynamics, achieving predefined performance attributes on the link angular position and velocity tracking errors. For a known nonsingular initial robotic condition, performance functions and unconstrained transformation errors are employed to prevent the violation of the full-state tracking error constraints. By combining two independent Lyapunov functions and radial basis function (RBF) neural network (NN) approximator, a novel and simple adaptive neural control scheme is proposed for the dynamics of the unconstrained transformation errors, which guarantees uniformly ultimate boundedness of all the signals in the closed-loop system. In the steady-state control process, RBF NNs are verified to satisfy the partial persistent excitation (PE) condition. Subsequently, an appropriate state transformation is adopted to achieve the accurate convergence of neural weight estimates. The corresponding experienced knowledge on unknown robotic dynamics is stored in NNs with constant neural weight values. Using the stored knowledge, a static neural learning controller is developed to improve the full-state tracking performance. A comparative simulation study on a 2-link robot illustrates the effectiveness of the proposed scheme.


2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Chenyang Xu ◽  
Humin Lei ◽  
Jiong Li ◽  
Jikun Ye ◽  
Dongyang Zhang

For nonaffine pure-feedback systems, an adaptive neural control method based on extreme learning machine (ELM) is proposed in this paper. Different from the existing methods, this scheme firstly converts the original system into a nonaffine system containing only one unknown term by equivalent transformation, thus avoiding the cumbersome and complex indirect design process of traditional backstepping methods. Secondly, a high-performance finite-time-convergence-differentiator (FD) is designed, through which the system state variables and their derivatives are accurately estimated to ensure the control effect. Thirdly, based on the implicit function theorem, the ELM neural network is introduced to approximate the uncertain items of the system, which simplifies the repeated adjustment process of the network training parameters. Meanwhile, the minimum learning parameter algorithm (MLP) is adopted to design the adaptive law for the norm of the network weight vector, which significantly reduces calculations. And it is theoretically proved that the closed-loop control system is stable and the tracking error is bounded. Finally, the effectiveness of the designed controller is verified by simulation.


2017 ◽  
Vol 14 (1) ◽  
pp. 172988141668270 ◽  
Author(s):  
Zhonghua Wu ◽  
Jingchao Lu ◽  
Jingping Shi ◽  
Qing Zhou ◽  
Xiaobo Qu

A robust adaptive neural control scheme based on a back-stepping technique is developed for the longitudinal dynamics of a flexible hypersonic flight vehicle, which is able to ensure the state tracking error being confined in the prescribed bounds, in spite of the existing model uncertainties and actuator constraints. Minimal learning parameter technique–based neural networks are used to estimate the model uncertainties; thus, the amount of online updated parameters is largely lessened, and the prior information of the aerodynamic parameters is dispensable. With the utilization of an assistant compensation system, the problem of actuator constraint is overcome. By combining the prescribed performance function and sliding mode differentiator into the neural back-stepping control design procedure, a composite state tracking error constrained adaptive neural control approach is presented, and a new type of adaptive law is constructed. As compared with other adaptive neural control designs for hypersonic flight vehicle, the proposed composite control scheme exhibits not only low-computation property but also strong robustness. Finally, two comparative simulations are performed to demonstrate the robustness of this neural prescribed performance controller.


Author(s):  
Ziquan Yu ◽  
Youmin Zhang ◽  
Yaohong Qu ◽  
Zhewen Xing

This paper is concerned with the fractional-order fault-tolerant tracking control design for unmanned aerial vehicle (UAV) in the presence of external disturbance and actuator fault. Based on the functional decomposition, the dynamics of UAV is divided into velocity subsystem and altitude subsystem. Altitude, flight path angle, pitch angle and pitch rate are involved in the altitude subsystem. By using an adaptive mechanism, the fractional derivative of uncertainty including external disturbance and actuator fault is estimated. Moreover, in order to eliminate the problem of explosion of complexity in back-stepping approach, the high-gain observer is utilized to estimate the derivatives of virtual control signal. Furthermore, by using a fractional-order sliding surface involved with pitch dynamics, an adaptive fractional-order fault-tolerant control scheme is proposed for UAV. It is proved that all signals of the closed-loop system are bounded and the tracking error can converge to a small region containing zero via the Lyapunov analysis. Simulation results show that the proposed controller could achieve good tracking performance in the presence of actuator fault and external disturbance.


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