In this paper, an adaptive neural network(NN)control approach is proposed for nonlinear pure-feedback systems with time-varying full state constraints. The pure-feedback systems of this paper are assumed to possess no...In this paper, an adaptive neural network(NN)control approach is proposed for nonlinear pure-feedback systems with time-varying full state constraints. The pure-feedback systems of this paper are assumed to possess nonlinear function uncertainties. By using the mean value theorem, pure-feedback systems can be transformed into strict feedback forms. For the newly generated systems, NNs are employed to approximate unknown items. Based on the adaptive control scheme and backstepping algorithm, an intelligent controller is designed. At the same time, time-varying Barrier Lyapunov functions(BLFs)with error variables are adopted to avoid violating full state constraints in every step of the backstepping design. All closedloop signals are uniformly ultimately bounded and the output tracking error converges to the neighborhood of zero, which can be verified by using the Lyapunov stability theorem. Two simulation examples reveal the performance of the adaptive NN control approach.展开更多
A new design scheme of direct adaptive fuzzy controller for a class of perturbed pure-feedback nonlinear systems is proposed. The design is based on backstepping and the approximation capability of the first type fuzz...A new design scheme of direct adaptive fuzzy controller for a class of perturbed pure-feedback nonlinear systems is proposed. The design is based on backstepping and the approximation capability of the first type fuzzy systems. A continuous robust term is adopted to minify the influence of modeling errors or disturbances. By introducing the modified integral-type Lyapunov function, the approach is able to avoid the requirement of the upper bound of the first time derivation of the high frequency control gain. Through theoretical analysis, the closed-loop control system is proven to be semi-globally uniformly ultimately bounded, with tracking error converging to a residual set.展开更多
In this paper, an adaptive neural networks(NNs)tracking controller is proposed for a class of single-input/singleoutput(SISO) non-affine pure-feedback non-linear systems with input saturation. In the proposed approach...In this paper, an adaptive neural networks(NNs)tracking controller is proposed for a class of single-input/singleoutput(SISO) non-affine pure-feedback non-linear systems with input saturation. In the proposed approach, the original input saturated nonlinear system is augmented by a low pass filter.Then, new system states are introduced to implement states transformation of the augmented model. The resulting new model in affine Brunovsky form permits direct and simpler controller design by avoiding back-stepping technique and its complexity growing as done in existing methods in the literature.In controller design of the proposed approach, a state observer,based on the strictly positive real(SPR) theory, is introduced and designed to estimate the new system states, and only two neural networks are used to approximate the uncertain nonlinearities and compensate for the saturation nonlinearity of actuator. The proposed approach can not only provide a simple and effective way for construction of the controller in adaptive neural networks control of non-affine systems with input saturation, but also guarantee the tracking performance and the boundedness of all the signals in the closed-loop system. The stability of the control system is investigated by using the Lyapunov theory. Simulation examples are presented to show the effectiveness of the proposed controller.展开更多
Adaptive neural network （NN） dynamic surface control （DSC） is developed for a class of non-affine pure-feedback systems with unknown time-delay. The problems of ＂explosion of complexity＂ and circular constructio...Adaptive neural network （NN） dynamic surface control （DSC） is developed for a class of non-affine pure-feedback systems with unknown time-delay. The problems of ＂explosion of complexity＂ and circular construction of the practical controller in the traditional backstepping algorithm are avoided by using this controller design method. For removing the requirements on the sign of the derivative of function f~, Nussbaum control gain technique is used in control design procedure. The effects of unknown time-delays are eliminated by using appropriate Lyapunov-Krasovskii functionals. Proposed control scheme guarantees that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded. Two simulation examples are presented to demonstrate the method.展开更多
针对以熔化极气体保护焊(gas metal arc welding,GMAW)为代表的一类非匹配不确定纯反馈非线性系统的输出问题,提出一种基于变幂次趋近律的滑模控制方法。首先,采用滑模微分器得到含系统非匹配不确定性干扰的输出一阶导数。得益于终端滑...针对以熔化极气体保护焊(gas metal arc welding,GMAW)为代表的一类非匹配不确定纯反馈非线性系统的输出问题,提出一种基于变幂次趋近律的滑模控制方法。首先,采用滑模微分器得到含系统非匹配不确定性干扰的输出一阶导数。得益于终端滑模有限时间稳定的性能,该方法具有估计精度高、估计误差收敛速度快的优点。然后,提出一种新型的变幂次趋近律,并证明在相同增益下,其趋近速度均快于现有各种趋近律,且具有自适应调节趋近速度的能力,既保证了在全局范围内系统轨迹有限时间趋近滑模面,又避免了在滑模面附近出现抖振。最后,采用变幂次趋近律滑模变结构控制方法和传统趋近律滑模变结构控制方法分别对带有非匹配干扰的GMAW中的弧长进行控制仿真,并对比弧长跟踪效果,分析稳态误差。结果表明,变幂次趋近律滑模变结构方法能够有效的提高系统收敛的快速性,滑模控制方法对于非匹配不确定非线性系具有强鲁棒性。展开更多
基金supported in part by the National Natural Science Foundation of China(61622303,61603164,61773188)the Program for Liaoning Innovative Research Team in University(LT2016006)+1 种基金the Fundamental Research Funds for the Universities of Liaoning Province(JZL201715402)the Program for Distinguished Professor of Liaoning Province
文摘In this paper, an adaptive neural network(NN)control approach is proposed for nonlinear pure-feedback systems with time-varying full state constraints. The pure-feedback systems of this paper are assumed to possess nonlinear function uncertainties. By using the mean value theorem, pure-feedback systems can be transformed into strict feedback forms. For the newly generated systems, NNs are employed to approximate unknown items. Based on the adaptive control scheme and backstepping algorithm, an intelligent controller is designed. At the same time, time-varying Barrier Lyapunov functions(BLFs)with error variables are adopted to avoid violating full state constraints in every step of the backstepping design. All closedloop signals are uniformly ultimately bounded and the output tracking error converges to the neighborhood of zero, which can be verified by using the Lyapunov stability theorem. Two simulation examples reveal the performance of the adaptive NN control approach.
基金This work was supported by the National Natural Science Foundation of China (No. 60074013 & 10371106)the Foundation of the Education bureau of Jiangsu Province (No. KK0310067)the Foundation of Information Science Subject Group of Yangzhou University
文摘A new design scheme of direct adaptive fuzzy controller for a class of perturbed pure-feedback nonlinear systems is proposed. The design is based on backstepping and the approximation capability of the first type fuzzy systems. A continuous robust term is adopted to minify the influence of modeling errors or disturbances. By introducing the modified integral-type Lyapunov function, the approach is able to avoid the requirement of the upper bound of the first time derivation of the high frequency control gain. Through theoretical analysis, the closed-loop control system is proven to be semi-globally uniformly ultimately bounded, with tracking error converging to a residual set.
文摘In this paper, an adaptive neural networks(NNs)tracking controller is proposed for a class of single-input/singleoutput(SISO) non-affine pure-feedback non-linear systems with input saturation. In the proposed approach, the original input saturated nonlinear system is augmented by a low pass filter.Then, new system states are introduced to implement states transformation of the augmented model. The resulting new model in affine Brunovsky form permits direct and simpler controller design by avoiding back-stepping technique and its complexity growing as done in existing methods in the literature.In controller design of the proposed approach, a state observer,based on the strictly positive real(SPR) theory, is introduced and designed to estimate the new system states, and only two neural networks are used to approximate the uncertain nonlinearities and compensate for the saturation nonlinearity of actuator. The proposed approach can not only provide a simple and effective way for construction of the controller in adaptive neural networks control of non-affine systems with input saturation, but also guarantee the tracking performance and the boundedness of all the signals in the closed-loop system. The stability of the control system is investigated by using the Lyapunov theory. Simulation examples are presented to show the effectiveness of the proposed controller.
基金partially supported by the Key Program of Henan Provincial Department of Education(No.13A470254)National Natural Science Foundation of China(Nos.61273137 and 51375145)+1 种基金the Science and Technology Innovative Foundation for Distinguished Young Scholar of Henan Province(No.144100510004)the Science and Technology Programme Foundation for the Innovative Talents of Henan Province University(No.13HASTIT038)
文摘Adaptive neural network （NN） dynamic surface control （DSC） is developed for a class of non-affine pure-feedback systems with unknown time-delay. The problems of ＂explosion of complexity＂ and circular construction of the practical controller in the traditional backstepping algorithm are avoided by using this controller design method. For removing the requirements on the sign of the derivative of function f~, Nussbaum control gain technique is used in control design procedure. The effects of unknown time-delays are eliminated by using appropriate Lyapunov-Krasovskii functionals. Proposed control scheme guarantees that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded. Two simulation examples are presented to demonstrate the method.
文摘针对以熔化极气体保护焊(gas metal arc welding,GMAW)为代表的一类非匹配不确定纯反馈非线性系统的输出问题,提出一种基于变幂次趋近律的滑模控制方法。首先,采用滑模微分器得到含系统非匹配不确定性干扰的输出一阶导数。得益于终端滑模有限时间稳定的性能,该方法具有估计精度高、估计误差收敛速度快的优点。然后,提出一种新型的变幂次趋近律,并证明在相同增益下,其趋近速度均快于现有各种趋近律,且具有自适应调节趋近速度的能力,既保证了在全局范围内系统轨迹有限时间趋近滑模面,又避免了在滑模面附近出现抖振。最后,采用变幂次趋近律滑模变结构控制方法和传统趋近律滑模变结构控制方法分别对带有非匹配干扰的GMAW中的弧长进行控制仿真,并对比弧长跟踪效果,分析稳态误差。结果表明,变幂次趋近律滑模变结构方法能够有效的提高系统收敛的快速性,滑模控制方法对于非匹配不确定非线性系具有强鲁棒性。
