Adaptive Neural Network-Based Control for a Class of Nonlinear Pure-Feedback Systems With Time-Varying Full State Constraints

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.      

Decentralized global robust stabilization of a class of interconnected minimum-phase nonlinear systems

This paper focuses on a class of large-scale interconnected minimum-phase nonlinear systems with parameter uncertainty and nonlinear interconnections. The uncertain parameters are allowed to be time-varying and enter the systems nonlinearly. The interconnections are bounded by nonlinear functions of states. The problem we address is to design a decentralized robust controller such that the closed-loop large-scale interconnected nonlinear system is globally asymptotically stable for all admissible uncertain parameters and interconnections. It is shown that decentralized global robust stabilization of the system can be achieved using a control law obtained by a recursive design method together with an appropriate Lyapunov function.     

Decentralized adaptive neural fixed-time tracking control of constrained interconnected nonlinear systems with partially unmeasurable states

This article devises a new adaptive fixed-time tracking control strategy for interconnected nonlinear systems containing partially unmeasurable states and time-varying output constraints. Radial basis function neural networks, as function approximators, are utilized to model the unknown functions, and the partially unmeasurable states of the systems are estimated by a reduced-order observer. By constructing a transferred function, system outputs are directly constrained in a time-varying constraint bound. Meanwhile, the first-order sliding mode differentiators are utilized to reduce the computational burden caused by the repeated differentiations of virtual controllers. Under the Lyapunov function and the fixed-time theory, the decentralized adaptive fixed-time controllers are constructed. It is proved that the closed-loop systems are fixed-time stable and the output signals are restricted in the bounded compact set. Finally, two simulation examples demonstrate the validity of the proposed control scheme.     

A new decentralised controller design method for a class of strongly interconnected systems

In this paper, two interconnected structures are first discussed, under which some closed-loop subsystems must be unstable to make the whole interconnected system stable, which can be viewed as a kind of strongly interconnected systems. Then, comparisons with small gain theorem are discussed and large gain interconnected characteristics are shown. A new approach for the design of decentralised controllers is presented by determining the Lyapunov function structure previously, which allows the existence of unstable subsystems. By fully utilising the orthogonal space information of input matrix, some new understandings are presented for the construction of Lyapunov matrix. This new method can deal with decentralised state feedback, static output feedback and dynamic output feedback controllers in a unified framework. Furthermore, in order to reduce the design conservativeness and deal with robustness, a new robust decentralised controller design method is given by combining with the parameter-dependent Lyapunov function method. Some basic rules are provided for the choice of initial variables in Lyapunov matrix or new introduced slack matrices. As byproducts, some linear matrix inequality based sufficient conditions are established for centralised static output feedback stabilisation. Effects of unstable subsystems in nonlinear Lur'e systems are further discussed. The corresponding decentralised controller design method is presented for absolute stability. The examples illustrate that the new method is significantly effective.     

Fuzzy adaptive output feedback control for a class of switched non-triangular structure nonlinear systems with time-varying delays

This paper investigates an adaptive fuzzy output feedback control design problem for switched nonlinear system in non-triangular structure form. The discussed system contains unknown nonlinear dynamics, unmeasured states and unknown time-varying delays under a batch of switching signals. Fuzzy logic systems are utilised to learn unknown nonlinear dynamics and construct a fuzzy switched nonlinear observer. By combining the property of fuzzy basis function with Lyapunov–Krasovskii functional and the command filter, a novel observer-based fuzzy adaptive backstepping schematic design algorithm is presented. Furthermore, the stability of the closed-loop control system is proved via Lyapunov stability theory and average dwell time method. The simulation results are presented to verify the validity of the proposed control scheme.     

Sliding control of non-linear systems containing time-varying uncertainties with unknown bounds

The sliding controller is very effective in dealing with system uncertainties defined in compact sets. If the bounds of the uncertainties are not available, the adaptive sliding controller might be designed. One restriction for the adaptive sliding scheme is that the unknown parameter should be constant, which is not always satisfied in practice. For a non-linear system with general uncertainties (i.e. time varying with unknown bounds), both the traditional sliding control and adaptive sliding control do not work properly. This paper proposes a new sliding control scheme for non-linear systems containing time-varying uncertainties with unknown bounds. The uncertainties are assumed to be piecewise continuous functions of time and satisfy the Dirichlet conditions. By representing these uncertainties in finite-term Fourier series, they can be estimated by updating the Fourier coefficients. Since the coefficients are time-invariant, update laws are easily obtained from the Lyapunov approach to guarantee output error convergence. Computer simulations are performed to show efficacy of the proposed schemes.     

Neural adaptive prescribed performance control for interconnected nonlinear systems with output dead zone

This paper is concerned with the neural‐based decentralized adaptive control for interconnected nonlinear systems with prescribed performance and unknown dead zone outputs. In the controller design procedure, neural networks are employed to identify unknown auxiliary functions, and the control design obstacle caused by the output nonlinearity is resolved via introducing Nussbaum function. Then, a reliable neural decentralized adaptive control is developed through incorporating the backstepping method and the prescribed performance technique. In the light of Lyapunov stability theory, it is verified that the proposed control scheme can ensure that all the closed‐loop signals are bounded, and can also guarantee that the tracking errors remain within a small enough compact set with the prescribed performance bounds. Finally, some simulation results are given to illustrate the feasibility of the devised control strategy.     

带有输出约束的柔性关节机械臂预设性能自适应控制

针对带有输出约束和模型不确定的柔性关节机械臂系统,提出一种基于时变障碍李雅普诺夫函数的预设性能自适应控制方法.通过构造指数衰减的时变约束边界,提出时变正切型障碍李雅普诺夫函数,能够同时适用于约束与非约束情况,进而拓宽传统对数型障碍李雅普诺夫函数的适用范围.此外,通过预先设置时变边界函数的相关参数,使得系统输出在初始阶段...     

Decentralised disturbance-observer-based adaptive tracking in the presence of unmatched nonlinear time-delayed interactions and disturbances

This paper proposes an approximation-based nonlinear disturbance observer (NDO) approach for decentralised adaptive tracking of uncertain interconnected pure-feedback nonlinear systems with unmatched time-delayed nonlinear interactions and external disturbances. Compared with the existing approximation-based NDO approach for uncertain interconnected nonlinear systems where the centralised design framework was proposed, the main contribution of this paper is to develop a decentralised and memoryless NDO-based adaptive control scheme in the presence of unknown time-varying delayed interactions and disturbances unmatched in the control inputs. The recursive design methodology is derived to construct the decentralised NDO and controller where the function approximators used in the decentralised NDO are employed to design the decentralised adaptive controller. From the Lyapunov stability theorem using Lyapunov--Krasovskii functionals, it is shown that all signals of the closed-loop system are semi-globally uniformly ultimately bounded and the tracking errors converge to an adjustable neighbourhood of the origin.     

Mean square leader-following consensus of heterogeneous multi-agent systems with Markovian switching topologies and communication delays

This article considers the mean square leader-following output consensus problem of heterogeneous multi-agent systems under randomly switching topologies and time-varying communication delays. By modeling the switching topologies as a time-homogeneous Markov process and taking the communication delays into consideration, a distributed observer is proposed to estimate the state of the leader. A novel distributed output feedback controller is then designed. By constructing a novel switching Lyapunov functional, an easily-verifiable sufficient condition to achieve the mean square leader-following output consensus is given. Finally, two simulation examples are presented to show the effectiveness of the proposed control scheme.     

时变关联系统的分散自适应输出反馈控制

针对一类具有动静态关联项和未建模动态的时变关联系统,通过引入输入滤波器及一系列坐标变换,给出了一种分散自适应输出反馈控制器的设计方案.当时变参数的变化率属于L1∩L∞,外界干扰属于L2∩L∞,未建模动态的幅值在某砦范围内变化时,证明了闭环系统的稳定性,且每一个子系统的输出收敛于零.仿真例子验证了这一控制方案的有效性.     

Adaptive neural output feedback finite-time command filtered backstepping control for nonlinear systems with full-state constraints

In this paper, an adaptive neural finite-time control method via barrier Lyapunov function, command filtered backstepping, and output feedback is proposed to solve the tracking problem of uncertain high-order nonlinear systems with full-state constraints and input saturation. By utilizing the neural network (NN) to approximate unknown nonlinear functions, the finite-time command filters are used to filtering the virtual control signals and get the intermediate control signals in a finite time in the backstepping process. Because there are errors between the output of finite-time command filters and the virtual control signals, the error compensation signals are added to eliminate the influence of filtering errors. Based on the proposed control scheme, the states of the system can be constrained in the predetermined region, all signals in the system are bounded in finite time, and the tracking error can converge to the desired region in finite time. At last, a simulation example is given to show the effectiveness of the proposed control method.     

Decentralized Output Regulation of a New Class of Interconnected Uncertain Nonlinear Systems

In the current paper the decentralized output regulation problem of a new class of interconnected uncertain nonlinear systems is considered. A novel decentralized high-gain input driven filter is proposed such that the output feedback based control law can be designed. Moreover, a robust multi-input changing supply function technique is presented such that the stability analysis can be performed by the non-quadratic Lyapunov functions. Therefore, the assumptions on the interconnection terms can be removed. Finally the proposed decentralized control laws are applied to the interconnected mass-spring systems immersed in the liquid and the simulation results illustrate the effectiveness of the proposed control scheme.     

Robust tracking control for a class of flexible-joint time-delay robots using only position measurements

In this paper, robust tracking control is investigated for a class of uncertain flexible-joint robots with time delays and time-varying perturbations. By employing the Lyapunov--Krasovskii functional technique and backstepping design technique, a novel robust tracking control scheme using only position measurements is developed such that all the states and signals of the closed-loop flexible-joint time-delay robot system remain bounded and the tracking error can asymptotically converge to a small neighbourhood around the origin. By appropriately choosing the weighting gains in the Lyapunov–Krasovskii functionals, the circular phenomenon in the controller design is overcome. Due to suitably designing the velocity observer and the virtual control input, the link-side dynamics does not need to be incorporated into the actuator-side tracking error dynamics, and so the complexity in the backstepping design is avoided. Consequently, we can easily construct the Lyapunov–Krasovskii functionals, and, in turn, the robust tracking control scheme developed here is a linear time-varying controller and can be simply implemented. Simulation examples are provided to verify the effectiveness of the proposed control algorithm.     

时变时滞非仿射大系统的分散自适应控制

针对一类具有未知时变时滞的非仿射互联大系统基于神经网络的逼近能力, 提出了一种分散自适应神经网络控制方案。该方案利用中值定理对未知非仿射函数进行分离; 利用分离技术和Young's不等式放宽了对未知时滞及时滞互联不确定项的限制, 同时大大减少了在线调节参数的数量。此外, 利用Lyapunov Krasovskii 泛函补偿了未知时滞带来的不确定性。通过理论分析, 证明了闭环系统所有信号是有界的, 输出跟踪误差收敛到原点的一个小邻域内。最后, 仿真结果验证了所提控制方案的有效性。     

Nussbaum gain adaptive fuzzy control for switched nonlinear systems with predefined output and full time-varying states constraints

In this paper, the problem of adaptive fuzzy tracking control for a class of uncertain switched nonlinear systems with unknown control direction is studied. Aiming at the problem, an adaptive control scheme with Nussbaum gain technology is constructed by using the average dwell time (ADT) method and the backstepping method to overcome the unknown control direction, and time-varying asymmetric barrier Lyapunov functions (ABLFs) are adopted to ensure the full-state constraints satisfaction. The proposed control scheme guarantees that all closed-loop signals remain bounded under a class of switching signals with ADT, while the output tracking error converges to a small neighborhood of the zero. An important innovation of this design method is that the unknown control direction, asymmetric time-varying full state constraints, and predefined time-varying output requirements are simultaneously considered in uncertain switched nonlinear systems for the first time. We set a moment in advance, and make the systems comply with the constraint conditions before running the moment by the shift function nested in the first time-varying ABLF. Finally, a simulation example verifies the effectiveness of the proposed scheme.     

含仿射不确定参数的多项式非线性系统控制器设计

针对含仿射时变不确定参数的多项式非线性系统,提出了基于多项式分解的控制方法. 多项式分解方法主要思想是将多项式系统转化成带自由变量的系数矩阵,从而偶次多项式的非负性验证问题可转化成线性矩阵不等式或双线性矩阵不等式求解问题. 文中多项式系统控制器综合基于 Lyapunov 稳定定理. 构造 Lyapunov 函数以及寻找反馈控制器可由所给的算法通过计算机程序自动完成. 对于多维系统相对高阶的控制器,由多项式全基构造的控制器将有很多项单项式. 为克服这一问题,文中算法给出含最少单项式的简约型控制器设计方法,并提出针对最小代价性能目标优化的增益受约次优控制. 数值仿真例子表明,文中所给的控制方法取得良好性能.     

含仿射不确定参数的多项式非线性系统控制器设计

针对含仿射时变不确定参数的多项式非线性系统，提出了基于多项式分解的控制方法. 多项式分解方法主要思想是将多项式系统转化成带自由变量的系数矩阵，从而偶次多项式的非负性验证问题可转化成线性矩阵不等式或双线性矩阵不等式求解问题. 文中多项式系统控制器综合基于 Lyapunov 稳定定理. 构造 Lyapunov 函数以及寻找反馈控制器可由所给的算法通过计算机程序自动完成. 对于多维系统相对高阶的控制器，由多项式全基构造的控制器将有很多项单项式. 为克服这一问题，文中算法给出含最少单项式的简约型控制器设计方法，并提出针对最小代价性能目标优化的增益受约次优控制. 数值仿真例子表明，文中所给的控制方法取得良好性能.     

Decentralized state observer scheme for uncertain time-delay T-S fuzzy interconnected systems

This paper focuses on a class of T-S fuzzy interconnected systems with time delays and time-varying parameter uncertainties. Observer-based output feedback decentralized controller is designed such that the closed-loop interconnected system is asymptotically stable in the Lyapunov sense in probability for all admissible uncertainties and time delays. Sufficient conditions for robustly asymptotically stability of the systems are given in terms of a set of linear matrix inequalities(LMIs).     

Decentralized state observer scheme for uncertain time-delay T-S fuzzy interconnected systems

This paper focuses on a class of T-S fuzzy interconnected systems with time delays and time-varying parameter uncertainties. Observer-based output feedback decentralized controller is designed such that the closed-loop interconnected system is asymptotically stable in the Lyapunov sense in probability for all admissible uncertainties and time delays. Sufficient conditions for robustly asymptotically stability of the systems are given in terms of a set of linear matrix inequalities （LMIs）.