Neural-Network-Based Decentralized Adaptive Control for a Class of Large-Scale Nonlinear Systems With Unknown Time-Varying Delays

A decentralized adaptive methodology is presented for large-scale nonlinear systems with model uncertainties and time-delayed interconnections unmatched in control inputs. The interaction terms with unknown time-varying delays are bounded by unknown nonlinear bounding functions related to all states and are compensated by choosing appropriate Lyapunov–Krasovskii functionals and using the function approximation technique based on neural networks. The proposed memoryless local controller for each subsystem can simply be designed by extending the dynamic surface design technique to nonlinear systems with time-varying delayed interconnections. In addition, we prove that all the signals in the closed-loop system are semiglobally uniformly bounded, and the control errors converge to an adjustable neighborhood of the origin. Finally, an example is provided to illustrate the effectiveness of the proposed control system.      

Neural-Network-Based Control for Discrete-Time Nonlinear Systems with Input Saturation Under Stochastic Communication Protocol

In this paper,an adaptive dynamic programming(ADP)strategy is investigated for discrete-time nonlinear systems with unknown nonlinear dynamics subject to input saturation.To save the communication resources between the controller and the actuators,stochastic communication protocols(SCPs)are adopted to schedule the control signal,and therefore the closed-loop system is essentially a protocol-induced switching system.A neural network(NN)-based identifier with a robust term is exploited for approximating the unknown nonlinear system,and a set of switch-based updating rules with an additional tunable parameter of NN weights are developed with the help of the gradient descent.By virtue of a novel Lyapunov function,a sufficient condition is proposed to achieve the stability of both system identification errors and the update dynamics of NN weights.Then,a value iterative ADP algorithm in an offline way is proposed to solve the optimal control of protocol-induced switching systems with saturation constraints,and the convergence is profoundly discussed in light of mathematical induction.Furthermore,an actor-critic NN scheme is developed to approximate the control law and the proposed performance index function in the framework of ADP,and the stability of the closed-loop system is analyzed in view of the Lyapunov theory.Finally,the numerical simulation results are presented to demonstrate the effectiveness of the proposed control scheme.     

一类仿射非线性网络控制系统的稳定性分析

利用采样数字控制系统的方法分析了一类混杂动态系统模型描述的仿射非线性网络控制系统的稳定性问题.针对一类仿射非线性对象和线性数字控制器组成的网络控制系统,考虑了网络诱导延时对系统稳定性的影响,得到了仿射非线性网络控制系统一致渐近稳定的条件．仿真实例验证了理论分析的正确性．     

On Improving Transient Performance in Tracking Control for a Class of Nonlinear Discrete-Time Systems With Input Saturation

Quick response and small overshoot are two desired transient performances of target tracking control. While most of the design schemes compromise between these two performances, we try to achieve both simultaneously for the tracking control of a class of nonlinear discrete-time systems with input saturation by using a composite nonlinear feedback (CNF) control technique. The closed-loop system with improved transient performance preserves the stability of the nonlinear part of the partially linear composite system.     

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.      

Neural-Network-Based State Feedback Control of a Nonlinear Discrete-Time System in Nonstrict Feedback Form

In this paper, a suite of adaptive neural network (NN) controllers is designed to deliver a desired tracking performance for the control of an unknown, second-order, nonlinear discrete-time system expressed in nonstrict feedback form. In the first approach, two feedforward NNs are employed in the controller with tracking error as the feedback variable whereas in the adaptive critic NN architecture, three feedforward NNs are used. In the adaptive critic architecture, two action NNs produce virtual and actual control inputs, respectively, whereas the third critic NN approximates certain strategic utility function and its output is employed for tuning action NN weights in order to attain the near-optimal control action. Both the NN control methods present a well-defined controller design and the noncausal problem in discrete-time backstepping design is avoided via NN approximation. A comparison between the controller methodologies is highlighted. The stability analysis of the closed-loop control schemes is demonstrated. The NN controller schemes do not require an offline learning phase and the NN weights can be initialized at zero or random. Results show that the performance of the proposed controller schemes is highly satisfactory while meeting the closed-loop stability.      

一类不确定离散时间系统的鲁棒控制

本文针对一类离散时间不确定多输入系统,采用李雅普诺夫第二方法设计了一种鲁棒的线性状态反馈控制器.分析了控制系统的稳定性,并得出系统稳定的充分条件.最后给出仿真例子.     

一类离散时间非线性系统不依赖受控系统数学模型的学习自适应控制

给出了一类离散时间非线性系统的不依赖受控系统数学模型的学习自适应控制方案，它不需要受控系统的结构信息、数学模型、外部试验信号和训练过程，仅用受控系统的Ｉ／Ｏ数据来设计，传统的未建模动态不存在，所给出的计算机仿真结果说明了所给出的方案的正确性和有效性。     

一类离散时间非线性系统不依赖受控系统数学模型的学习自适应控制

给出了一类离散时间非线性系统的不依赖受控系统数学模型的学习自适应控制方案，它不需要受控系统的结构信息、数学模型、外部试验信号和训练过程，仅用受控系统的I／O数据来设计，传统的未建模动态不存在，所给出的计算机仿真结果说明了所给出的方案的正确性和有效性。     

Receding Horizon Control for a Class of Discrete-Event Systems With Real-Time Constraints

We consider discrete-event systems (DES) involving the control of tasks with real-time constraints. When future event time information is limited, we propose a receding horizon (RH) controller in which only some future information is available within a time window. Analyzing sample paths obtained under this scheme and comparing them to optimal sample paths (obtained when all event times are known), we derive a number of attractive properties of the RH controller, including: the fact that it still guarantees all real-time constraints; there are segments of its sample path over which all controls are still optimal; the error relative to the optimal task departure times is decreasing under certain conditions. Simulation results are included to verify the properties of the controller and show that its performance can be near-optimal even if the RH window size is relatively small     

带有控制约束的离散时间系统的可控性可达性强可连性

本文研究带有控制约束的离散时间线性系统的可控性、可达性及强可连性。给出判别这些性质的充分必要条件,并指出它们之间的关系。     

Fault Detection for a Class of Nonlinear Networked Control Systems with Communication Constraints

International Journal of Control, Automation and Systems - This paper is concerned with fault detection for a class of nonlinear networked control systems with stochastic transfer delays and data...     

一类不确定离散时间系统的最优鲁棒控制

本文针对一类含有不确定因素的离散单输入系统,采用李雅普诺夫第二种方法设计了一种鲁棒控制器.这个控制器是系统状态的线性函数,并且被证明当矩阵Q给定时,它是最优的.文末给出了仿真例子.     

Adaptive Consensus Quantized Control for a Class of High-Order Nonlinear Multi-Agent Systems With Input Hysteresis and Full State Constraints

For a class of high-order nonlinear multi-agent systems with input hysteresis, an adaptive consensus output-feedback quantized control scheme with full state constraints is investigated. The major properties of the proposed control scheme are: 1) According to the different hysteresis input characteristics of each agent in the multi-agent system, a hysteresis quantization inverse compensator is designed to eliminate the influence of hysteresis characteristics on the system while ensuring that the quantized signal maintains the desired value. 2) A barrier Lyapunov function is introduced for the first time in the hysteretic multi-agent system. By constructing state constraint control strategy for the hysteretic multi-agent system, it ensures that all the states of the system are always maintained within a predetermined range. 3) The designed adaptive consensus output-feedback quantization control scheme allows the hysteretic system to have unknown parameters and unknown disturbance, and ensures that the input signal transmitted between agents is the quantization value, and the introduced quantizer is implemented under the condition that only its sector bound property is required. The stability analysis has proved that all signals of the closed-loop are semi-globally uniformly bounded. The StarSim hardware-in-the-loop simulation certificates the effectiveness of the proposed adaptive quantized control scheme.      

Robust Discrete-Time Iterative Learning Control for Nonlinear Systems With Varying Initial State Shifts

This note is concerned with the robust discrete-time iterative learning control (ILC) design for nonlinear systems with varying initial state shifts. A two-gain ILC law is considered using a 2-D analysis approach. Sufficient conditions are derived to guarantee both convergence of the learning process for fixed initial condition and boundedness of the tracking error for variable initial condition. It is shown that the error data with anticipation in time can well handle the varying initial state shifts in discrete-time ILC.      

Fourier-Neural-Network-Based Learning Control for a Class of Nonlinear Systems With Flexible Components

This paper considers an output feedback learning control for a class of uncertain nonlinear systems with flexible components. The distinct time delay caused by system flexibility leads to the phase lag phenomenon and low system bandwidth. Therefore, the tracking problem of such systems is very difficult and challenging. To improve the tracking performance of such systems, an iterative learning control scheme using the Fourier neural network (FNN) is presented in this paper. This scheme uses only local output information for feedback. FNN employs orthogonal complex Fourier exponentials as its activation functions and the physical meaning of its hidden-layer neurons is clear. The FNN-based learning controller introduced here relies on the frequency-domain method, which converts the tracking problem in the time domain into a number of regulation problems in the frequency domain. A novel phase compensation method is introduced to deal with the phase lag phenomenon, so that the bandwidth of the closed-loop system is increased. Experiments on a belt-driven positioning table are conducted to show the effectiveness of the proposed controller.     

一类具有未知幂次的高阶不确定非线性系统的自适应控制

研究了一类具有未知幂次的高阶不确定非线性系统的自适应跟踪控制问题. 在无需系统函数先验知识的条件下, 采用积分反推技术和障碍李雅普诺夫函数, 提出了一种新颖的自适应跟踪控制算法. 该控制算法的显著特点是所设计的自适应控制器均与系统幂次无关, 并且能够保证闭环系统的所有信号皆有界. 仿真算例验证了该控制算法的有效性.     

具有间歇性执行器故障的非线性系统自适应CFB控制

控制系统的执行器经常发生各种未知的间歇性故障. 如何有效地处理这些故障对系统的影响是一个难题. 针对一类不确定严格反馈非线性系统, 提出一种自适应CFB (Command filtered backstepping) 控制方案解决了间歇性执行器故障的补偿问题. 利用神经网络逼近控制器中的未知函数, 并采用投影算子实时在线更新控制器中的估计参数使得参数估计值随着故障次数的累积而不断增加的问题被消除. 提出改进的Lyapunov函数证明了所提出的方案能够保证所有闭环信号的有界性, 同时建立了跟踪误差与Lyapunov函数跳变幅度, 最小故障时间间隔, 设计参数之间的关系. 如果Lyapunov函数的跳变幅度越小以及两个连续故障之间的时间间隔越长, 系统的稳态跟踪指标越好. 通过迭代计算建立了暂态跟踪误差指标的均方根型界. 该界表明了通过选择恰当的设计参数, 可改善系统的暂态指标. 仿真结果表明了所提方案的有效性.     

Control of a Speech Robot via an Optimum Neural-Network-Based Internal Model With Constraints

An optimum internal model with constraints is proposed and discussed for the control of a speech robot, which is based on the human-like behavior. The main idea of the study is that the robot movements are carried out in such a way that the length of the path traveled in the internal space, under external acoustical and mechanical constraints, is minimized. This optimum strategy defines the designed internal model, which is responsible for the robot task planning. First, an exact analytical way to deal with the problem is proposed. Next, by using some empirical findings, an approximate solution for the designed internal model is developed. Finally, the implementation of this solution, which is applied to the control of a speech robot, yields interesting results in the field of task-planning strategies, task anticipation (namely, speech coarticulation), and the influence of force on the accuracy of executed tasks.