Adaptive Neural Tracking Control Design for a Class of Uncertain Switched Nonlinear Systems with Input Saturation
-
摘要: 针对单输入单输出系统研究一种在任意切换下的跟踪控制问题，系统包含未知扰动和输入饱和特性.首先，利用高斯误差函数描述一个连续可导的非对称饱和模型.其次，利用径向基神经网络（Radial basis function neural network，RBF NN）逼近未知的系统动态.最后，基于公共的Lyapunov函数构造状态反馈控制器.设计的控制器避免过多参数调节从而减轻计算负荷.结果展示本文给出的状态反馈控制器可以保证闭环系统的所有信号是半全局一致有界的，并且跟踪误差可收敛到零值小的领域内.最后的仿真结果进一步验证提出方法的有效性.
-
关键词:
- 切换非线性系统 /
- 公共的Lyapunov函数 /
- 非对称饱和 /
- 自适应Backstepping
Abstract: This paper deals with tracking control for a class of single input and single output (SISO) uncertain strict-feedback switched nonlinear systems with input asymmetric saturation actuator, unknown external disturbance and arbitrary switchings. Firstly, Gaussian error function is employed to represent a novel continuous differentiable asymmetric saturation model. Secondly, by employing radial basis function neural network (RBF NN), unknown functions are approximated. At last, a state-feedback controller is constructed by using common Lyapunov function method. The designed controller decreases the number of learning parameters, thus reduces the computational burden. The designed state-feedback controller is shown to be able to guarantee that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB) and the tracking error converges to a small neighborhood of the origin. Two simulation examples are presented to show the effectiveness of the proposed approach. -
-
图 10 自适应更新率 $\hat{\theta}_1$ , $\hat{\theta}_2$
Fig. 10 Adaptive laws $\hat{\theta}_1$ , $\hat{\theta}_2$
-
[1] 李海波, 柴天佑, 赵大勇.混合选别浓密机底流矿浆浓度和流量区间智能切换控制方法.自动化学报, 2014, 40(9):1967-1975 http://www.aas.net.cn/CN/abstract/abstract18467.shtmlLi Hai-Bo, Chai Tian-You, Zhao Da-Yong. Intelligent switching control of underflow slurry concentration and flowrate intervals in mixed separation thickener. Acta Automatica Sinica, 2014, 40(9):1967-1975 http://www.aas.net.cn/CN/abstract/abstract18467.shtml [2] 柴天佑, 张亚军.基于未建模动态补偿的非线性自适应切换控制方法.自动化学报, 2011, 37(7):773-786 http://www.aas.net.cn/CN/abstract/abstract17475.shtmlChai Tian-You, Zhang Ya-Jun. Nonlinear adaptive switching control method based on unmodeled dynamics compensation. Acta Automatica Sinica, 2011, 37(7):773-786 http://www.aas.net.cn/CN/abstract/abstract17475.shtml [3] Wu J L. Feedback stabilization for multiinput switched nonlinear systems:two subsystems case. IEEE Transactions on Automatic Control, 2008, 53(4):1037-1042 doi: 10.1109/TAC.2008.919518 [4] Wu J L. Stabilizing controllers design for switched nonlinear systems in strict-feedback form. Automatica, 2009, 45(4):1092-1096 doi: 10.1016/j.automatica.2008.12.004 [5] Ma R C, Dimirovski G M, Zhao J. Backstepping robust H∞ control for a class of uncertain switched nonlinear systems under arbitrary switchings. Asian Journal of Control, 2013, 15(1):41-50 doi: 10.1002/asjc.v15.1 [6] 李杰, 齐晓慧, 夏元清, 高志强.线性/非线性自抗扰切换控制方法研究.自动化学报, 2016, 42(2):202-212 http://www.aas.net.cn/CN/abstract/abstract18810.shtmlLi Jie, Qi Xiao-Hui, Xia Yuan-Qing, Gao Zhi-Qiang. On linear/nonlinear active disturbance rejection switching control. Acta Automatica Sinica, 2016, 42(2):202-212 http://www.aas.net.cn/CN/abstract/abstract18810.shtml [7] Long F, Fei S M, Fu Z M, Zheng S Y. Adaptive neural network control for switched system with unknown nonlinear part by using backstepping approach:SISO case. Advances in Neural Networks-ISNN 2006. Berlin Heidelberg:Springer-Verlag, 2006. 842-848 [8] Han T T, Ge S S, Lee T H. Adaptive neural control for a class of switched nonlinear systems. Systems & Control Letters, 2009, 58(2):109-118 [9] Li Y M, Tong S C, Li T S. Adaptive fuzzy backstepping control design for a class of pure-feedback switched nonlinear systems. Nonlinear Analysis:Hybrid Systems, 2015, 16(1):72-80 [10] Jiang B, Shen Q K, Shi P. Neural-networked adaptive tracking control for switched nonlinear pure-feedback systems under arbitrary switching. Automatica, 2015, 61:119-125 doi: 10.1016/j.automatica.2015.08.001 [11] Ge S S, Wang C. Direct adaptive NN control of a class of nonlinear systems. IEEE Transactions on Neural Networks, 2002, 13(1):214-221 doi: 10.1109/72.977306 [12] Ma J J, Ge S S, Zheng Z Q, Hu D. Adaptive NN control of a class of nonlinear systems with asymmetric saturation actuators. IEEE Transactions on Neural Networks and Learning Systems, 2015, 26(7):1532-1538 doi: 10.1109/TNNLS.2014.2344019 [13] Wang M, Wang C. Learning from adaptive neural dynamic surface control of strict-feedback systems. IEEE Transactions on Neural Networks and Learning Systems, 2015, 26(6):1247-1259 doi: 10.1109/TNNLS.2014.2335749 [14] He W, Chen Y H, Yin Z. Adaptive neural network control of an uncertain robot with full-state constraints. IEEE Transactions on Cybernetics, 2016, 46(3):620-629 doi: 10.1109/TCYB.2015.2411285 [15] Li D J. Neural network control for a class of continuous stirred tank reactor process with dead-zone input. Neurocomputing, 2014, 131:453-459 doi: 10.1016/j.neucom.2013.11.006 [16] Al Janaideh M, Rakheja S, Su C Y. A generalized Prandtl-Ishlinskii model for characterizing the hysteresis and saturation nonlinearities of smart actuators. Smart Materials and Structures, 2009, 18(4):045001 doi: 10.1088/0964-1726/18/4/045001 [17] Zhao W, Ren X M, Gao X H. Synchronization and tracking control for multi-motor driving servo systems with backlash and friction. International Journal of Robust and Nonlinear Control, 2016, 26(13):2745-2766 doi: 10.1002/rnc.v26.13 [18] Liu Z, Chen C, Zhang Y. Decentralized robust fuzzy adaptive control of humanoid robot manipulation with unknown actuator backlash. IEEE Transactions on Fuzzy Systems, 2015, 23(3):605-616 doi: 10.1109/TFUZZ.2014.2321591 [19] 胡洲, 王志胜, 甄子洋.带输入饱和的欠驱动吊车非线性信息融合控制.自动化学报, 2014, 40(7):1522-1527 http://www.aas.net.cn/CN/abstract/abstract18422.shtmlHu Zhou, Wang Zhi-Sheng, Zhen Zi-Yang. Nonlinear information fusion control for underactuated cranes with input saturation. Acta Automatica Sinica, 2014, 40(7):1522-1527 http://www.aas.net.cn/CN/abstract/abstract18422.shtml [20] Li Y M, Tong S C, Li T S. Adaptive fuzzy output-feedback control for output constrained nonlinear systems in the presence of input saturation. Fuzzy Sets and Systems, 2014, 248:138-155 doi: 10.1016/j.fss.2013.11.006 [21] Wang H Q, Chen B, Liu X P, Liu K F, Liu C. Adaptive neural tracking control for stochastic nonlinear strict-feedback systems with unknown input saturation. Information Sciences, 2014, 269:300-315 doi: 10.1016/j.ins.2013.09.043 [22] Sui S, Tong S C, Li Y M. Adaptive fuzzy backstepping output feedback tracking control of MIMO stochastic pure-feedback nonlinear systems with input saturation. Fuzzy Sets and Systems, 2014, 254:26-46 doi: 10.1016/j.fss.2014.03.013 [23] Zhang L L, Yang G H. Dynamic surface error constrained adaptive fuzzy output feedback control for switched nonlinear systems with unknown dead zone. Neurocomputing, 2016, 199:128-136 doi: 10.1016/j.neucom.2016.03.028 [24] Zhao X D, Shi P, Zheng X L, Zhang L X. Adaptive tracking control for switched stochastic nonlinear systems with unknown actuator dead-zone. Automatica, 2015, 60:193-200 doi: 10.1016/j.automatica.2015.07.022 [25] Niu B, Li L. Adaptive neural network tracking control for a class of switched strict-feedback nonlinear systems with input delay. Neurocomputing, 2016, 173:2121-2128 doi: 10.1016/j.neucom.2015.10.059 [26] Tong S C, Sui S, Li Y M. Observed-based adaptive fuzzy tracking control for switched nonlinear systems with dead-zone. IEEE Transactions on Cybernetics, 2015, 45(12):2816-2826 doi: 10.1109/TCYB.2014.2386912 [27] Yin S, Yu H, Shahnazi R, Haghani A. Fuzzy adaptive tracking control of constrained nonlinear switched stochastic pure-feedback systems. IEEE Transactions on Cybernetics, 2017, 47(3):579-588, DOI: 10.1109/TCYB.2016.2521179 [28] Wu J, Su B Y, Li J, Zhang X, Ai L F. Global adaptive neural tracking control of nonlinear MIMO systems. Neural Computing and Applications, 2016, DOI: 10.1007/s00521-016-2268-x [29] Sanner R M, Slotine J J E. Gaussian networks for direct adaptive control. IEEE Transactions on Neural Networks, 1992, 3(6):837-863 doi: 10.1109/72.165588 [30] Wang H Q, Chen B, Liu K F, et al. Adaptive neural tracking control for a class of nonstrict-feedback stochastic nonlinear systems with unknown backlash-like hysteresis. IEEE Transactions on Neural Networks and Learning Systems, 2014, 25(5):947-958 doi: 10.1109/TNNLS.2013.2283879 期刊类型引用(9)
1. 陈锦攀，赵兵，孙闻，罗滇生，廖峰，王伟，梁晓兵，王玲，李德波. 一种最速控制器的机理研究与应用. 化工自动化及仪表. 2023(06): 745-753+874 . 
百度学术2. 张慧凤，董乐伟，魏新江. 一类带有输入饱和的随机系统抗干扰控制. 自动化学报. 2021(06): 1453-1459 . 本站查看3. 王元慧，李子宜，张潇月，张晓云. 考虑输入饱和的锚泊辅助动力定位滑模控制. 哈尔滨工程大学学报. 2021(07): 1048-1055 . 百度学术4. 汝浩男，赵前进，吴健. 一类带有未知时延的随机系统自适应控制. 安徽理工大学学报(自然科学版). 2021(04): 65-72 . 百度学术5. 陈龙胜，王琦，何国毅. 非仿射纯反馈非线性切换系统自适应控制. 上海交通大学学报. 2020(09): 981-986 . 百度学术6. 赵颖，付俊，赵军. 切换系统的无扰切换控制及其在航空发动机中的应用. 自动化学报. 2020(10): 2165-2176 . 本站查看7. 李军，刘哲，周永言. 一种新型高性能控制器的研究与应用. 自动化学报. 2020(12): 2558-2571 . 本站查看8. 付明玉，王莎莎，王元慧，庹玉龙. 带输入饱和的欠驱动水面船参数自适应滑模控制. 哈尔滨工程大学学报. 2019(01): 202-209 . 百度学术9. 田艳兵，魏巍，代明星. 一类SNS的模糊自适应输出反馈控制. 计算机仿真. 2019(10): 316-321 . 百度学术其他类型引用(15)
-
下载:
图(12)
计量
- 文章访问数: 2418
- HTML全文浏览量: 322
- PDF下载量: 855
- 被引次数: 24
