基于动态神经网络的鲁棒自适应跟踪

研究了一类基于两层动态神经网的仿射型鲁棒适应跟踪问题，对于未知的仿射非线性系统，提出了新的鲁棒学习算法，该算法不需要知道 理想权值的界。     

非线性系统基于HM模型的自适应鲁棒跟踪控制

研究了非线性系统的跟踪控制问题，基于HM模型对非线性系统进行描述，并将全局模糊模型表示成不确定系统形式。在满足匹配条件下，针对未知不确定界，采用自适应鲁棒控制器，利用自适应变量信息来补偿系统的不确定性信息，实现了非线性系统的渐近跟踪控制。一级倒立摆仿真实验，验证了方案的有效性。控制器结构简单，规则少，具有应用价值。     

不确定性系统的自适应鲁棒跟踪控制

针对存在未知干扰和未建模动态等不确定性的系统的自适应鲁棒跟踪控制问题进行了探讨.首选将l1优化控制器的有限拍设计方法结合给出了最优鲁棒稳态跟踪控制器的设计方法．然后利用集员辨识的思想,将名义模型的参数和未建模动态及干扰的大小作为未知参数,提出了一种递推参数估计方法.最后将上述研究结果结合起来提出了一种自适应鲁棒跟踪控制策略,证明了自适应算法的全局收敛性并给出了鲁棒跟踪性能指标的一下较紧的上界.与现有的结果相比,本文提出的自适应控制具有非保守的鲁棒稳定性,具有渐近最优的鲁棒跟踪性能.     

执行器故障不确定非线性系统最优自适应输出跟踪控制

本文针对一类具有执行器故障的多输入多输出（Multi-input multi-output,MIMO）不确定连续仿射非线性系统,提出了一种最优自适应输出跟踪控制方案.设计了保证系统稳定性的不确定项估计神经网络权值调整算法,仅采用评价网络即可同时获得无限时域代价函数和满足哈密顿-雅可比-贝尔曼（Hamilton-Jacobi-Bellman,HJB）方程的最优控制输入.考虑执行器卡死和部分失效故障,设计最优自适应补偿控制律,所设计的控制律可以实现对参考输出的一致最终有界跟踪.飞行器控制仿真和对比验证表明了本文方法的有效性和优越性.     

基于迭代神经动态规划的数据驱动非线性近似最优调节

利用数据驱动控制思想,建立一种设计离散时间非线性系统近似最优调节器的迭代神经动态规划方法.提出针对离散时间一般非线性系统的迭代自适应动态规划算法并且证明其收敛性与最优性.通过构建三种神经网络,给出全局二次启发式动态规划技术及其详细的实现过程,其中执行网络是在神经动态规划的框架下进行训练.这种新颖的结构可以近似代价函数及其导函数,同时在不依赖系统动态的情况下自适应地学习近似最优控制律.值得注意的是,这在降低对于控制矩阵或者其神经网络表示的要求方面,明显地改进了迭代自适应动态规划算法的现有结果,能够促进复杂非线性系统基于数据的优化与控制设计的发展.通过两个仿真实验,验证本文提出的数据驱动最优调节方法的有效性.     

非线性系统的神经网络鲁棒自适应跟踪控制

针对一类具有未知非线性函数和未知虚拟系数非线性函数的二阶非线性系统,提出了一种神经网络鲁棒自适应输出跟踪控制方法.用李雅普诺夫稳定性分析方法证明了本文的神经网络自适应控制器能够使受控系统内的所有信号均为有界.选择的神经网络权值调整规律可以防止自适应控制中的参数漂移.     

一类非线性激励器系统的鲁棒自适应跟踪

考虑一类具有非线性激励器不确定系统的鲁棒跟踪问题，其不确定性是部分已知的。所构造的鲁棒自适应控制方案能确保系统的跟踪误差终极一致有界．与已有文献结果相比．未知参数估计的自适应律和控制器是连续的，从而使得所提出的设计方案在实际控制问题中易实现。且与具有线性激励器的系统一样具有较强的鲁棒性．最后通过数值算例进一步说明了该设计方案是有效的。     

基于神经动态优化的非线性系统近似最优跟踪控制

提出一种输入约束下一类连续时间非线性系统最优跟踪控制问题的近似求解方法.针对有限时间跟踪性能指标下一类单输入单输出非线性系统,利用所提出的最优跟踪控制方法实现目标系统所对应性能指标近似最优.首先将系统的性能指标沿时间泰勒展开,得到一个近似的性能指标;其次,在系统状态可观测条件下,将该问题进一步转化为以控制输入为决策变量的非线性规划问题;再次,利用神经动态优化方法,求解含不等式约束下的近似最优控制问题并给出相应的递归神经网络模块原理图;进而,针对整个闭环系统进行理论分析,证明在一定条件下闭环系统的稳定性;最后,通过两个实例仿真验证所提出方法的有效性.     

基于自适应动态规划的矿渣微粉生产过程跟踪控制

矿渣微粉是一种新型绿色环保型建材,可以大大提高水泥混凝土的力学性能.本文以矿渣微粉生产过程为研究对象,针对该过程难以通过机理建模进行辨识和控制的特点,利用数据驱动的思想,建立矿渣微粉生产过程的递归神经网络模型.在此基础上,利用自适应动态规划,设计具有控制约束的跟踪控制器,并将其应用到矿渣微粉生产过程中.仿真分析表明,建立的数据驱动模型能够有效地辨识矿渣微粉生产过程,同时,本文提出的控制方法能够实现输入受限的微粉比表面积及磨内压差的最优跟踪控制.     

基于自组织模糊CMAC网络的非线性系统鲁棒自适应跟踪控制

基于自组织模糊CMAC(SOFCMAC)神经网络,提出了一种非线性模型参考神经网络增广逆系统鲁棒自适应跟踪控制方法.该方法的特点是通过S0FCMAC神经网络在线修正由于建模误差、不确定因素等引起的非线性系统逆误差,使得系统输出准确跟踪参考模型输出.SOFCMAC的权值调整规律由Lyapunov稳定性理论导出.文中证明了非线性闭环系统的稳定性.仿真例子表明了本文方法的有效性.     

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 dynamic programming and optimal control of nonlinear nonaffine systems

In this paper, a novel optimal control design scheme is proposed for continuous-time nonaffine nonlinear dynamic systems with unknown dynamics by adaptive dynamic programming (ADP). The proposed methodology iteratively updates the control policy online by using the state and input information without identifying the system dynamics. An ADP algorithm is developed, and can be applied to a general class of nonlinear control design problems. The convergence analysis for the designed control scheme is presented, along with rigorous stability analysis for the closed-loop system. The effectiveness of this new algorithm is illustrated by two simulation examples.     

Adaptive differential dynamic programming for multiobjective optimal control

An efficient numerical solution scheme entitled adaptive differential dynamic programming is developed in this paper for multiobjective optimal control problems with a general separable structure. For a multiobjective control problem with a general separable structure, the “optimal” weighting coefficients for various performance indices are time-varying as the system evolves along any noninferior trajectory. Recognizing this prominent feature in multiobjective control, the proposed adaptive differential dynamic programming methodology combines a search process to identify an optimal time-varying weighting sequence with the solution concept in the conventional differential dynamic programming. Convergence of the proposed adaptive differential dynamic programming methodology is addressed.     

Optimal prescribed performance tracking control of nonlinear motor driven systems via adaptive dynamic programming

Although optimal regulation problem has been well studied, resolving optimal tracking control via adaptive dynamic programming (ADP) has not been completely resolved, particularly for nonlinear uncertain systems. In this paper, an online adaptive learning method is developed to realize the optimal tracking control design for nonlinear motor driven systems (NMDSs), which adopts the concept of ADP, unknown system dynamic estimator (USDE), and prescribed performance function (PPF). To this end, the USDE in a simple form is first proposed to address the NMDSs with bounded disturbances. Then, based on the estimated unknown dynamics, we define an optimal cost function and derive the optimal tracking control. The derived optimal tracking control is divided into two parts, that is, steady-state control and optimal feedback control. The steady-state control can be obtained with the tracking commands directly. The optimal feedback control can be obtained via the concept of ADP based on the PPF; this contributes to improving the convergence of critic neural network (CNN) weights and tracking accuracy of NMDSs. Simulations are provided to display the feasibility of the designed control method.     

基于自适应动态规划的多设定值跟踪控制方法

针对一类典型的带有控制约束的非线性离散时间系统,提出了一种基于自适应动态规划(adaptive dynamic programmmg,ADP)算法的多设定值跟踪控制方法,并对其收敛性和稳定性做了严格分析.在ADP迭代跟踪控制的基础上,根据多模型控制的思想,设置阶梯状的参考轨迹,使得系统状态逐渐地跟踪到最终设定值,保证了系统的稳定性,极大地减小超调量,加快了响应时间,改善控制品质;同时由于控制器约束的存在,引入非二次型的性能指标函数,使得控制量始终在有界的范围内变化.最后对仿真结果进行了分析,结果表明了此方法的可行性和有效性.     

Finite-horizon neuro-optimal tracking control for a class of discrete-time nonlinear systems using adaptive dynamic programming approach

In this paper, a finite-horizon neuro-optimal tracking control strategy for a class of discrete-time nonlinear systems is proposed. Through system transformation, the optimal tracking problem is converted into designing a finite-horizon optimal regulator for the tracking error dynamics. Then, with convergence analysis in terms of cost function and control law, the iterative adaptive dynamic programming (ADP) algorithm via heuristic dynamic programming (HDP) technique is introduced to obtain the finite-horizon optimal tracking controller which makes the cost function close to its optimal value within an ?-error bound. Three neural networks are used as parametric structures to implement the algorithm, which aims at approximating the cost function, the control law, and the error dynamics, respectively. Two simulation examples are included to complement the theoretical discussions.     

Online optimal tracking control of continuous-time linear systems with unknown dynamics by using adaptive dynamic programming

In this paper, a novel theoretic formulation based on adaptive dynamic programming (ADP) is developed to solve online the optimal tracking problem of the continuous-time linear system with unknown dynamics. First, the original system dynamics and the reference trajectory dynamics are transformed into an augmented system. Then, under the same performance index with the original system dynamics, an augmented algebraic Riccati equation is derived. Furthermore, the solutions for the optimal control problem of the augmented system are proven to be equal to the standard solutions for the optimal tracking problem of the original system dynamics. Moreover, a new online algorithm based on the ADP technique is presented to solve the optimal tracking problem of the linear system with unknown system dynamics. Finally, simulation results are given to verify the effectiveness of the theoretic results.     

Infinite horizon optimal control of affine nonlinear discrete switched systems using two-stage approximate dynamic programming

In this article, a novel iteration algorithm named two-stage approximate dynamic programming (TSADP) is proposed to seek the solution of nonlinear switched optimal control problem. At each iteration of TSADP, a multivariate optimal control problem is transformed to be a certain number of univariate optimal control problems. It is shown that the value function at each iteration can be characterised pointwisely by a set of smooth functions recursively obtained from TSADP, and the associated control policy, continuous control and switching control law included, is explicitly provided in a state-feedback form. Moreover, the convergence and optimality of TSADP is strictly proven. To implement this algorithm efficiently, neural networks, critic and action networks, are utilised to approximate the value function and continuous control law, respectively. Thus, the value function is expressed by the weights of critic networks pointwise. Besides, redundant weights are ruled out at each iteration to simplify the exponentially increasing computation burden. Finally, a simulation example is provided to demonstrate its effectiveness.     

A novel optimal tracking control scheme for a class of discrete-time nonlinear systems using generalised policy iteration adaptive dynamic programming algorithm

In this paper, a novel iterative adaptive dynamic programming (ADP) algorithm, called generalised policy iteration ADP algorithm, is developed to solve optimal tracking control problems for discrete-time nonlinear systems. The idea is to use two iteration procedures, including an i-iteration and a j-iteration, to obtain the iterative tracking control laws and the iterative value functions. By system transformation, we first convert the optimal tracking control problem into an optimal regulation problem. Then the generalised policy iteration ADP algorithm, which is a general idea of interacting policy and value iteration algorithms, is introduced to deal with the optimal regulation problem. The convergence and optimality properties of the generalised policy iteration algorithm are analysed. Three neural networks are used to implement the developed algorithm. Finally, simulation examples are given to illustrate the performance of the present algorithm.     

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.