基于单网络ADP的一类未知非线性系统的近似最优控制

针对一类未知的连续非线性系统,提出一个基于单网络近似动态规划(ADP)的近似最优控制方案。该方案通过设计一个新型的递归神经网络(RNN)辨识器放松了系统模型需已知或部分已知的要求,并利用一个神经网络(NN)近似系统的性能指标函数消除了常规ADP方法中的控制网络。通过Lyapunov理论分析严格证明了闭环系统内所有信号一致最终有界,并且所获得的性能指标函数和控制输入分别收敛到最优性能指标函数和最优控制输入的小邻域内。仿真结果验证了所提出控制方案的有效性。     

Neural-network-observer-based optimal control for unknown nonlinear systems using adaptive dynamic programming

In this paper, an observer-based optimal control scheme is developed for unknown nonlinear systems using adaptive dynamic programming (ADP) algorithm. First, a neural-network (NN) observer is designed to estimate system states. Then, based on the observed states, a neuro-controller is constructed via ADP method to obtain the optimal control. In this design, two NN structures are used: a three-layer NN is used to construct the observer which can be applied to systems with higher degrees of nonlinearity and without a priori knowledge of system dynamics, and a critic NN is employed to approximate the value function. The optimal control law is computed using the critic NN and the observer NN. Uniform ultimate boundedness of the closed-loop system is guaranteed. The actor, critic, and observer structures are all implemented in real-time, continuously and simultaneously. Finally, simulation results are presented to demonstrate the effectiveness of the proposed control scheme.     

Online adaptive optimal control for continuous-time nonlinear systems with completely unknown dynamics

An online adaptive optimal control is proposed for continuous-time nonlinear systems with completely unknown dynamics, which is achieved by developing a novel identifier-critic-based approximate dynamic programming algorithm with a dual neural network (NN) approximation structure. First, an adaptive NN identifier is designed to obviate the requirement of complete knowledge of system dynamics, and a critic NN is employed to approximate the optimal value function. Then, the optimal control law is computed based on the information from the identifier NN and the critic NN, so that the actor NN is not needed. In particular, a novel adaptive law design method with the parameter estimation error is proposed to online update the weights of both identifier NN and critic NN simultaneously, which converge to small neighbourhoods around their ideal values. The closed-loop system stability and the convergence to small vicinity around the optimal solution are all proved by means of the Lyapunov theory. The proposed adaptation algorithm is also improved to achieve finite-time convergence of the NN weights. Finally, simulation results are provided to exemplify the efficacy of the proposed methods.     

一类非仿射非线性离散系统的改进无模型自适应控制

针对一类更广泛的非仿射非线性离散系统,提出一种改进的无模型自适应控制算法。该算法基于非参数动态线性化方法,运用观测器的思想,实现带有扰动系统的实时动态线性化,进而将无模型自适应控制方法的应用推广到更广泛的非仿射非线性离散系统。同时,对推广后的改进无模型自适应控制方法进行理论上的证明,并通过仿真实例验证了所提出的改进无模型自适应控制方法的可行性和有效性。     

Global output‐feedback stabilization for nonlinear systems with unknown relative degree

This paper is devoted to the global stabilization via output feedback for a class of nonlinear systems with unknown relative degree, dynamics uncertainties, unknown control direction, and nonparametric uncertain nonlinearities. In particular, the unknown relative degree is without known upper bound, which renders us to research for a filter with varying dimension rather than the ones with over dimensions in the existing literature. In comparison with more popular but a bit stronger input‐to‐state stable or input‐to‐state practically stable requirement, only bounded‐input bounded‐state stable requirement is imposed on the dynamics uncertainties, which affect the systems in a persistent intensity rather than in a decaying one. In this paper, to compensate multiple serious system uncertainties and realize global output‐feedback stabilization, a design scheme via switching logic together with varying dimensional filter is developed. In this scheme, 2 switching sequences, which separately generate the gains of the controller and act as the varying dimensions of the filter, are designed to overcome unknown control direction, dynamics uncertainties and nonparametric uncertain nonlinearities, and unknown relative degree, respectively. A 2‐mass lumped‐parameter structure is provided to show the effectiveness of the proposed method in this paper.     

未知多变量非线性系统自适应模糊预测控制

对一类未知多变量非线性系统提出了直接自适应模糊预测控制方法,此方法首先对被控对象提出了线性时变子模型加非线性子模型的预测模型,然后直接用模糊逻辑系统组成的向量来设计预测控制器,并基于时变死区函数对控制器中的未知向量和广义误差估计值中的未知矩阵进行自适应调整．文中证明了此方法可使广义误差向量估计值收敛到原点的一个邻域内．     

Adaptive asymptotic stabilization of a class of unknown nonlinear systems with specified convergence rate

This paper focuses on the asymptotic stabilization problem for a class of multivariable nonlinear systems with relative degree one, practical examples of which incorporate the liquid level control of water tanks, and the speed control of interconnected carts. The presence of the unknown (other than uncertain) additive and multiplicative nonlinearities renders asymptotic stability difficult to be achieved by the existing robust control methods. To conquer this obstacle, a novel adaptive control strategy is proposed. In the control design, the state constraint technique is newly introduced to the adaptive design to generate a proper control gain to compensate for unknown nonlinearities, instead of the use of extra approximating structures. By this means, both the prescribed transient performance regarding the convergence rate and the expected asymptotic stability are preserved. Finally, simulation results are given to illustrate the established theoretical findings.     

未知非线性系统自适应改进RBF滑模控制

针对一类未知的非线性系统,利用输入/输出线性化将其变换为部分线性可控系统,通过RBF神经网络对未知非线性函数进行逼近,提出了一种基于RBF神经网络的自适应滑模控制,并设计了自适应滑模控制器;提出了一种连续函数,很好地减少了抖振现象,使得闭环系统状态一致稳定最终有界。实验结果验证了方法的有效性。     

一类非线性离散系统自适应准滑模控制

针对一般非线性离散时间系统的不确定性和扰动抑制问题, 提出一种新的自适应准滑模控制算法. 算法包括两部分, 其一是基于紧格式动态线性化模型的自适应准滑模控制器设计, 其中动态线性化方法中“伪偏导数”的估计算法仅依赖于系统I/O 实时量测值. 其二是采用径向基神经网络估计器来估计系统的综合不确定性. 理论分析证明了系统的BIBO稳定性. 仿真结果验证了所提算法的有效性.     

Observer-critic structure-based adaptive dynamic programming for decentralised tracking control of unknown large-scale nonlinear systems

In this paper, a decentralised tracking control (DTC) scheme is developed for unknown large-scale nonlinear systems by using observer-critic structure-based adaptive dynamic programming. The control consists of local desired control, local tracking error control and a compensator. By introducing the local neural network observer, the subsystem dynamics can be identified. The identified subsystems can be used for the local desired control and the control input matrix, which is used in local tracking error control. Meanwhile, Hamiltonian-Jacobi-Bellman equation can be solved by constructing a critic neural network. Thus, the local tracking error control can be derived directly. To compensate the overall error caused by substitution, observation and approximation of the local tracking error control, an adaptive robustifying term is employed. Simulation examples are provided to demonstrate the effectiveness of the proposed DTC scheme.     

Adaptive fuzzy leader-follower consensus control using sliding mode mechanism for a class of high-order unknown nonlinear dynamic multi-agent systems

In this article, a distributed leader-follower consensus approach is developed for a class of high-order unknown nonlinear dynamic multi-agent systems (MASs). Because every agent of the MAS contains multiple state variables, the existing consensus methods are not completely applicable for it. In order to find the qualified consensus protocol for this high-order MAS, sliding mode mechanism can be naturally considered for designing the consensus control because it can manage multiple state variables with the help of a constructed hyperplane. To this consensus control design, the sliding mode term is composed of all tracking error variables. Since the method does not require the switching control term around sliding surface, it can avoid the chattering phenomenon, which exits in most of the published sliding mode controls (SMCs). Furthermore, to handle the unknown nonlinear dynamic problem, the adaptive approximation strategy is implemented by employing fuzzy logic system (FLS). In the light of Lyapunov stability analysis, it is demonstrated that the proposed control approach can accomplish the consensus tasks. Finally, a numerical example is implemented to further show the desired results.     

无未知参数先验信息的非线性自适应观测器设计

研究了一类具有未知参数的非线性系统自适应观测器设计问题.不同于现有结果,本文所研究的非线性系统更为一般,已知的系统信息更少:1)系统未知参数的范数的上界未知;2)具有关于可测输出非Lipschitz连续的非线性动态:3)系统输出显式地依赖于控制输入.通过设计自适应调节器来估计未知参数范数,从而给出了不基于未知参数先验信息的非线性自适应观测器设计的新方法.所设计的观测器为全局渐近收敛的,即实现了系统状态的渐近重构,确保了未知参数估计的一致有界性.此外,在系统输出不显式地依赖于控制输入的条件下,研究了降维观测器的设计问题.仿真例子验证了本文理论结果的正确性.     

无未知参数先验信息的非线性自适应观测器设计

研究了一类具有未知参数的非线性系统自适应观测器设计问题．不同于现有结果,本文所研究的非线性系统更为。一般,已知的系统信息更少：1）系统未知参数的范数的上界未知;2）具有关于可测输出非Lipschitz连续的非线性动态;3）系统输出显式地依赖于控制输入．通过设计自适应调节器来估计未知参数范数,从而给出了不基于未知参数先验信息的非线性自适应观测器设计的新方法．所设计的观测器为令局渐近收敛的,即实现了系统状态的渐近重构,确保了未知参数估计的一致有界性．此外,在系统输出不显式地依赖于控制输入的条件下,研究了降维观测器的设计问题．仿真例子验证了本文理论结果的正确性．     

Adaptive consensus control of nonlinear systems with unknown control directions

In this paper, we investigate the adaptive consensus control for a class of nonlinear systems with different unknown control directions where communications among the agents are represented by a directed graph. Based on the backstepping technique, a fully distributed adaptive control approach is proposed without using global information of the topology. Meanwhile, a novel Nussbaum-type function is proposed to address the consensus control with unknown control directions. It is proved that boundedness of all closed-loop signals and asymptotic consensus tracking for all the agents' outputs are ensured. In simulation studies, a numerical example is illustrated to show the effectiveness of the control scheme.     

Global adaptive exponential stabilisation for nonlinear systems with multiple unknown control directions

In this paper, we address the global generalised exponential stabilisation problem for a class of lower-triangular systems with multiple unknown directions. Instead of the well-known Nussbaum-gain adaptive rule, a Lyapunov-based adaptive logic switching rule is proposed to seek the correct control directions for such systems. The main advantage of the proposed controller is that it can guarantee the global generalised exponential stability of closed-loop systems. Simulation examples are given to verify the effectiveness of the developed control approach.     

Observer‐based adaptive robust control of a class of nonlinear systems with dynamic uncertainties

In this paper, a discontinuous projection‐based adaptive robust control (ARC) scheme is constructed for a class of nonlinear systems in an extended semi‐strict feedback form by incorporating a nonlinear observer and a dynamic normalization signal. The form allows for parametric uncertainties, uncertain nonlinearities, and dynamic uncertainties. The unmeasured states associated with the dynamic uncertainties are assumed to enter the system equations in an affine fashion. A novel nonlinear observer is first constructed to estimate the unmeasured states for a less conservative design. Estimation errors of dynamic uncertainties, as well as other model uncertainties, are dealt with effectively via certain robust feedback control terms for a guaranteed robust performance. In contrast with existing conservative robust adaptive control schemes, the proposed ARC method makes full use of the available structural information on the unmeasured state dynamics and the prior knowledge on the bounds of parameter variations for high performance. The resulting ARC controller achieves a prescribed output tracking transient performance and final tracking accuracy in the sense that the upper bound on the absolute value of the output tracking error over entire time‐history is given and related to certain controller design parameters in a known form. Furthermore, in the absence of uncertain nonlinearities, asymptotic output tracking is also achieved. Copyright © 2001 John Wiley & Sons, Ltd.     

Adaptive nonlinear observer for state and unknown parameter estimation in noisy systems

This paper proposes a novel adaptive observer for Lipschitz nonlinear systems and dissipative nonlinear systems in the presence of disturbances and sensor noise. The observer is based on an H_∞ observer that can estimate both the system states and unknown parameters by minimising a cost function consisting of the sum of the square integrals of the estimation errors in the states and unknown parameters. The paper presents necessary and sufficient conditions for the existence of the observer, and the equations for determining observer gains are formulated as linear matrix inequalities (LMIs) that can be solved offline using commercially available LMI solvers. The observer design has also been extended to the case of time-varying unknown parameters. The use of the observer is demonstrated through illustrative examples and the performance is compared with extended Kalman filtering. Compared to previous results on nonlinear observers, the proposed observer is more computationally efficient, and guarantees state and parameter estimation for two very broad classes of nonlinear systems (Lipschitz and dissipative nonlinear systems) in the presence of input disturbances and sensor noise. In addition, the proposed observer does not require online computation of the observer gain.     

Adaptive dynamic surface asymptotic tracking for a class of uncertain nonlinear systems

This paper contributes to dynamic surface asymptotic tracking for a class of uncertain nonlinear systems in strict‐feedback form. By utilizing the nonlinear filters with a positive time‐varying integral function, an adaptive state feedback controller is explicitly designed via a dynamic surface approach, where the compensating term with the estimate of an unknown bound is introduced to eliminate the effect raised by the boundary layer error at each step. Compared with the existing results in the literature, the proposed control scheme not only avoids the issue of “explosion of complexity” inherent in the backstepping procedure but also holds the asymptotic output tracking. Finally, simulation results are presented to verify the effectiveness of the proposed methodology.     

基于动态递归神经网络的一类非线性不确定系统的自适应观测器

针对一类非线性不确定系统，当其状态不可测时，在基于动态递归神经网络的观测器中，对用来抑制不确定性、保证观测器鲁棒观测的控制项进行恰当的设计。仿真结果证实了该设计的有效性。     

Global adaptive output feedback control for a class of stochastic nonlinear systems with unknown homogeneous growth rates

This article focuses on the adaptive output feedback stabilization for a class of stochastic nonlinear systems whose drift and diffusion terms satisfy homogeneous growth conditions. Since the homogeneous growth rates are unknown, two dynamic gains are coupled into the full-order homogeneous observer. By virtue of adding a power integrator technique and the homogeneity theory, two adaptive laws and a homogeneous output feedback controller are designed. Based on the celebrated nonnegative semimartingale convergence theorem and the general stochastic Barbˇlat's lemma, it is indicated that all the signals of the closed-loop system are bounded almost surely, and all the system states of the closed-loop system converge to origin almost surely. Finally, the effectiveness of the proposed control scheme is verified by means of both numerical and practical examples.