The adaptive neural network (NN) output feedback optimal saturation control scheme is investigated for a single-phase photovoltaic (PV) grid-connected power system with partially unavailable states. The unavailable states are estimated by a state observer. The output feedback control scheme combines the adaptive dynamic programming (ADP) approach with the dynamic surface control (DSC) technique based on the backstepping design framework, in which the DSC technique can simplify the computation. By constructing an observer-critic-actor architecture, NNs are utilized via reinforcement learning (RL) to approximate the solution of the Hamilton-Jacobi-Bellman (HJB) equation, such that the difficulty of solving the HJB equation is overcome. By integrating the hyperbolic tangent function with a first-order auxiliary system, the adverse influence of the saturated links is removed. All the variables of the closed-loop PV power system are proved to be semi-globally uniformly ultimately bounded (SGUUB) by the Lyapunov stability theory. The simulation and comparative results show the feasibility and superiority of the presented control scheme.

This paper proposes an adaptive neural network event-triggered and quantized output feedback control scheme for quarter vehicle active suspensions with actuator saturation. The scheme uses neural networks to approximate the unknown parts of the active suspension. When the system states of the suspension are not entirely available, a state observer is designed to estimate the unknown states. The measurable system states, partially estimated observer states, neural network weights, and a filtered virtual control are sequentially event-triggered, quantified, and transmitted to the controller via in-vehicle networks. The problem of non-differentiable virtual control is solved using dynamic surface control technology in the backstepping quantized control design. Integrating a Gaussian error function and a first-order auxiliary subsystem compensates for the nonlinearity caused by asymmetric saturation. Theoretical analysis proves that all error signals of the closed-loop active suspension system are semi-globally uniformly ultimately bounded, and the Zeno phenomenon can be ruled out. Simulation results validate the effectiveness of the proposed control method.

An adaptive neural network (NN) based optimal saturation control scheme is investigated for single-phase grid-connected photovoltaic (PV) systems by incorporating dynamic surface control (DSC) and adaptive dynamic programming (ADP) based on the backstepping control design framework. For each backstepping step, a critic-actor architecture is constructed via reinforcement learning (RL), and the PV system is optimized according to the cost function in the architecture. Due to the nonlinearity, it is difficult to solve the Hamilton-Jacobi-Bellman (HJB) equation. The neural networks (NNs) are employed to approximate the solution of the HJB equation such that the optimal virtual control and the actual controller are obtained. By considering control input symmetric saturation nonlinearity link, constraints on pulse width modulation (PWM) are ensured. On this basis, the combination of backstepping control design and dynamic surface technique is used to overcome the shortcomings of "differential explosion" and simplify calculations. Based on the Lyapunov method, the stability analysis proves that all signals of the closed-loop PV systems are semiglobally uniformly ultimately bounded (SGUUB). Simulation experiments and comparative results are given to verify the efficacy of the studied control strategy. This paper proposes an adaptive neural network-based optimal saturation control scheme for single-phase grid-connected photovoltaic systems. The scheme incorporates dynamic surface control and adaptive dynamic programming based on the backstepping control design framework. The designed controller guarantees that all signals of the closed-loop system are semi-globally uniformly ultimately bounded. image

Based on event-triggering and quantization mechanisms, a fuzzy adaptive quantization saturation control problem has been investigated for a class of nonlinear systems. The unknown components of the controlled system are approximated using fuzzy logic systems. Sensors are able to measure the states of the system. The state signals and some associated variables are sequentially event-triggered, quantized, and transmitted to the controller via network communication. To overcome asymmetric input saturation, an auxiliary subsystem is constructed to compensate for the nonlinearity caused by the saturation. By introducing dynamic surface technology and using the backstepping control design method, a quantization saturation control scheme is proposed, and the problem of virtual controllers being non-differentiable in quantization control is solved. Theoretical proof shows that all error signals of the closed-loop system are semi-globally uniformly ultimately bounded, and the Zeno behavior can be avoided. The control scheme is applied to a quarter-car active suspension system, and the simulation results verify the effectiveness of the proposed control method Note to Practitioners —Data sampling and quantization are critical processes in both computer control systems and network control systems. Event-triggered sampling can reduce network congestion and energy consumption. However, most existing methods rarely consider both event-triggering and quantization of system states and parameters simultaneously. Additionally, both the unknown components of the model and the saturation constraints of the actuator are common in control systems. Taking into account the above issues, this paper proposes a fuzzy adaptive quantization saturation control scheme based on event-triggering and quantization mechanisms. This scheme considers multiple nonlinear links that are more practical in computer control. It has been successfully validated for its applicability in a quarter-car active suspension system.

The adaptive neural-network (NN) output-feedback control problem is investigated for a quarter-car active suspension system. The sprung mass and the suspension stiffness in the considered suspension system are unknown, and the part states are not measured directly. In the control design, NNs are employed to approximate the unknown nonlinear dynamics, and an NN state observer is given to estimate the immeasurable states. By using the adaptive backstepping control design technique and introducing the command filter method, an observer-based NN output-feedback control algorithm is developed, in which the input saturation constraint is compensated via constructing an auxiliary system. It is proved that all the variables of the controlled system are bounded, and the ride comfort, ride safety condition, and suspension space limit are guaranteed. The computer simulation and compared results further show the effectiveness of the proposed control algorithm.

For a class of fuzzy networked switched time-delay systems, event-triggered control approach is given. The saturated controllers are developed. By using Lyapunov–Krasovskii functional method and linear matrix inequalities (LMIs) theory, the exponential stability of fuzzy networked time-delay systems is proved. To obtain the control gain matrices and observe gain matrices, a separate design principle is proposed. At last, two numerical examples prove the feasibility of the proposed approach and conditions.

针对一类不确定非线性简单互联电力系统,采用T-S模糊模型对该系统进行模糊建模,利用并行分布补偿原则和事件触发机制设计了模糊控制器.基于李雅普诺夫稳定性理论分析了系统的稳定性,并给出了保证不确定非线性简单互联电力系统渐近稳定的充分条件.最后MATLAB仿真验证了提出方法的有效性.

本文针对一类含有执行器饱和的网络化切换模糊系统,在系统状态不可测的情况下,通过设计状态观测器来估计系统中的不可测状态变量,基于事件触发机制设计了观测器的控制器,用凸组合法来处理执行器饱和对系统性能的影响,并利用多Lyapunov函数理论和平均驻留时间方法证明了闭环系统的稳定性.数值仿真实例验证了提出方法的有效性.

针对状态不完全可测的不确定非线性简单互联电力系统,依据T-S模型能够无限逼近特性处理了电力系统的非线性问题,设计了状态观测器来估计系统的状态,并利用并行分布补偿原则和事件触发机制设计了基于观测器状态的模糊控制器.采用李雅普诺夫稳定性理论分析了闭环系统的渐近稳定性,并以线性矩阵不等式的形式给出了使系统稳定的充分条件.最后给出了验证该设计方法有效的仿真例子.

针对含有执行器饱和的网络化切换模糊系统,提出了基于事件触发的系统状态反馈问题.运用事件触发机制和并行分布式补偿算法,设计了状态反馈控制器.基于多 Lyapunov 函数理论和平均驻留时间方法,通过线性矩阵不等式给出了使闭环系统指数稳定的充分条件.最后,用一个数值例子验证了所给出方法的可行性.

Fuzzy adaptive optimal bounded control problems are first investigated for a class of nonlinear continuous-time interconnected systems whose system internal dynamics and unmatched interconnections are completely unknown, when there exist unavailable states in the subsystems of the interconnected systems. The system states and the interconnection terms of the interconnected system are approximated by using a fuzzy state observer. The decentralized optimal controllers and observer-critic structure are designed according to adaptive dynamic programming and enforcement learning technology. The presented control methods can ensure that the system states and parameter estimation errors of the interconnected systems are ultimately uniformly bounded. A simulation example validates the effectiveness of the presented scheme.

For quarter car suspension systems with uncertain mass, first Takagi-Sugeno fuzzy model is constructed by using sector nonlinear. Based on adaptive critic technology and zero-sum game theory, an online adaptive fuzzy optimal control method is studied. A critic fuzzy logic system is given to approximate the solution of the Hamilton-Jacobi-Isaacs equation with H ∞ -cost function in an on-line way, instead of traditional action-critic architecture. A simulation example is employed to illustrate the validity of the scheme of the paper.

SummaryA fuzzy adaptive optimal output‐feedback control problem is investigated for nonlinear continuous‐time interconnected systems with saturation constraints. The system dynamics and the mismatched interconnections of each subsystem are unknown, and each subsystem contains unavailable states. Based on fuzzy logic systems, a fuzzy state observer is designed to approximate the unknown system states and the interconnections of the nonlinear interconnected system. By using adaptive dynamic programming technology, the decentralized optimal controller of each subsystem is given and the saturation control input is updated via the critic fuzzy logic systems. The proposed fuzzy adaptive observer‐based optimal control scheme can guarantee that the closed‐loop interconnected system is ultimately uniformly bounded stable. A simulation example validates the effectiveness of the presented scheme.

In this paper, the state feedback fuzzy optimal event-triggered control scheme is investigated for simple interconnected power system when system states are measurable. By using the Takagi-Sugeno fuzzy model, the fuzzy model of the power system is established. In terms of and parallel distributed compensation (PDC) technology, the local fuzzy optimization controller is designed. The sufficient conditions which ensure the stability of the power system are given in the form of linear matrix inequality (LMI). According to Lyapunov stability theory, the system stability is proved. Simulation results demonstrate the proposed control scheme can realize the optimal control of the power system.

This paper investigates the output-feedback control design problem for a class of switched Takagi–Sugeno fuzzy large-scale systems with non-measurable premise variables. The considered fuzzy large-scale systems consist of several interconnected subsystems with different switching modes and there exists the asynchronous switching between the system switching modes and the controller switching modes. A decentralised state observer is proposed to estimate the unmeasured states, and a new output-feedback decentralised control scheme is developed by using the state observers and the switching functions. Based on the theory of Lyapunov stability and average dwell-time methods, the sufficient conditions of ensuring the switched control system stability are proposed and proved, which are formulated in the form of linear matrix inequalities. An applicable example is provided to show the effectiveness of the obtained theoretical results.

In this paper, an observer-based online optimal control scheme is presented for nonlinear continuous-time systems with actuator saturation. Three neural networks, which include an observer neural network, an critic neural network and an actuator neural network, comprise observer-based critic-actuator architecture. The critic neural network is employed to approximate the cost performance. The control input depends on the the actuator neural network and the estimated system states. By using Lyapunov stability theory, the system state estimate error, the neural network weight estimate errors are ensured to be uniformly ultimately bounded. A simulation example is given to demonstrate the validness of the proposed control method.

集散控制系统(Distributed Control System,简称DCS)是石油、电力、冶金、化工等行业实现自动控制的主流产品,集散控制系统课程是自动化、测控技术与仪器等专业的主要专业课程,该课程的目标是使学生掌握DCS的设计方法、并具备DCS的硬件和软件开发,DCS综合应用等工程实践能力.该课程具有与工程实践联系密切、实用性较强等特点,以培养学生具有扎实的理论基础,同时具有较好的工程实践能力为目的,针对该课程本身的特点,进行了教学内容的改革及优化,实现了教学内容的模块化和系统化.所提出的改革方法在实际教学过程进行了实践,取得了良好教学效果.

This paper researches the event-triggered control of networked continuous-time switched fuzzy systems. The controllers are designed by event-trigger mechanism and parallel distributed compensation method. Through Lyapunov theory and average dwell time approach, we give sufficient conditions that can ensure the closed-loop system's exponential stability, which are depicted by linear matrix inequalities (LMIs). At last, a numerical example proves the feasibility of the method.

This paper investigates an output-feedback control design problem for a class of switched continuous-time Takagi-Sugeno (T-S) fuzzy systems. The considered fuzzy systems consist of several switching modes and each switching mode is described by T-S fuzzy models. In addition, there exists the asynchronous switching between the system switching modes and the controller switching modes. By using parallel distributed compensation design method, the output-feedback control schemes are developed based on state observers for the measurable and immeasurable premise variables cases. The sufficient conditions of ensuring the switched control system stabilization are proposed based on the theory of Lyapunov stability and average-dwell time methods. The controller and observer gains are obtained via two-step method. An illustrated numerical example is provided to show the effectiveness of the proposed control approaches.

This paper investigates the adaptive neural network optimal output feedback control design problem for nonlinear continuous-time systems with actuator saturation. The system dynamics and states of the controlled system are unknown. A neural network state observer is constructed to estimate the system states. This paper uses two neural networks, one is used to construct the neural network state observer, the other (critic neural network) is used to approximate the cost functions, which comprise an observer-critic architecture. In this architecture, the critic neural network weights are tuned based on both the current data and the previous data, thus the conditions of the persistent excitation in the previous literatures are relaxed. By utilizing adaptive dynamic programming approach, a new observer-based optimal control scheme is developed. It is proved that the proposed adaptive neural network output feedback optimal control scheme can ensure that the whole closed-loop system is stable. Moreover, the estimate errors of the critic neural network weights are asymptotically stable. A simulation example is given to validate the effectiveness of the proposed method.