高级自适应PID控制器研究及应用

基于智能控制思想设计实现一种高级自适应PID控制器.该控制器由模型辩识单元、控制参数整定单元和PID控制单元等部分组成,具有自动选择控制作用、控制规律、控制参数和修正模型等功能.在系统稳定时施加阶跃信号,测定系统的阶跃响应并确定系统的工艺模型,然后据此确定系统的控制作用、控制规律、择优选择调节参数;当系统设定值改变时,再利用最小二乘法修正系统的工艺模型.实验结果表明,该自适应PID控制器具有较好的控制性能.     

基于PD增益的模型参考自适应同步控制

在保证自适应控制精度的前提下,找出快速收敛的参数,从而使被控系统和参考系统达到同步.针对传统的PI控制器无法获得参数波动的系统的较高的控制性能.基于模型参考自适应控制,利用PD控制器可以预料到系统误差的方向的优点,设计了一种自适应同步控制器.仿真结果表明,该自适应同步控制器能够使被控系统和参考系统达到同步,并能够较精确地控制被控系统的输出,使被控系统满足系统所要求的动态性能.这对研究组合自适应控制策略和开展多模型自适应控制器的研究提供了基础.     

基于强化学习的神经网络在船模速度控制中的应用

讨论了强化学习模型,以及基于进化学习的神经网络模型,在此基础上结合强化学习的自适应能力,通过神经网络的在线学习对船舶模型速度进行跟踪控制,并以强化学习神经网络结构设计出神经网络控制器,最终取得对船舶模型航行速度的自适应控制。经过仿真研究,将开发与训练好的自学习神经网络控制器用于对船舶模型速度跟踪控制,所开发的基于强化学习的神经网络控制器具有良好的速度跟踪性能,控制效果明显。     

一类非线性多变量系统的多模型自适应控制

针对一类不确定的非线性多变量离散时间动态系统,提出了一种基于切换的多模型自适应控制方法.该控制方法的特点在于以下两个方面:首先,引入一个高阶差分算子使得非线性系统的非线性项的限制条件不再要求全局有界;其次,提出的控制方法由线性自适应控制器、神经网络非线性自适应控制器以及切换机构组成:线性控制器用来保证闭环系统的输入输出信号有界,神经网络非线性控制器用来改善闭环系统的性能,基于性能指标的切换机构在每一时刻选择性能指标较好的控制器对系统进行控制.理论分析和仿真实验说明了提出的多模型自适应控制方法的有效性.     

间歇过程操作单元模糊自适应控制器及应用

基于化工反应器间歇式生产过程听复杂特性,提出了一种结合多变量时序逻辑模糊自适应方法的控制器模型,并用于间歇过程操作单元的控制器设计,在实际应用中获得了满意的控制效果。     

进化强化学习及其在机器人路径跟踪中的应用

研究了一种基于自适应启发评价(AHC)强化学习的移动机器人路径跟踪控制方法.AHC的评价单元(ACE)采用多层前向神经网络来实现.将TD(λ)算法和梯度下降法相结合来更新神经网络的权值.AHC的动作选择单元(ASE)由遗传算法优化的模糊推理系统(FIS)构成.ACE网络的输出构成二次强化信号,用于指导ASE的学习.最后将所提出的算法应用于移动机器人的行为学习,较好地解决了机器人的复杂路径跟踪问题.     

间歇过程操作单元模糊自适应控制器及其应用

基于化工反应器间歇式生产过程的复杂特性,提出了一种结合多变量时序逻辑模糊自适应方法的控制器模型,并用于间歇过程操作单元的控制器设计,在实际应用中获得了满意的控制效果     

基于神经网络与多模型的非线性自适应广义预测控制

针对一类不确定非线性离散时间动态系统, 提出了基于神经网络与多模型的非线性广义预测自适应控制方法. 该自适应控制方法由线性鲁棒广义预测自适应控制器, 神经网络非线性广义预测自适应控制器和切换机制三部分构成. 线性鲁棒广义预测自适应控制器保证闭环系统的输入输出信号有界, 神经网络非线性广义预测自适应控制器能够改善系统的性能. 切换策略通过对上述两种控制器的切换, 保证系统稳定的同时, 改善系统性能. 给出了所提自适应方法的稳定性和收敛性分析. 最后通过仿真实例验证了所提方法的有效性.     

基于模型参考自适应方法的对偶控制方法研究

针对未知参数随机系统，基于模型参考自适应方法和双准则函数，设计了具有控制和学习作用的对偶控制器．通过控制器的学习作用提高了控制精度，有效地减少了启动时的超调．仿真结果表明，设计的控制器能够具有较好的跟踪性能．     

基于ANFIS的火电单元机组协调控制设计方法

基于自适应网络模糊推理系统,将模糊推理、变结构控制和神经网络结合起来,提出了一种针对某火电厂300 MW单元机组协调控制的方法.给出了控制系统的传递函数模型、结构图和自适应训练方法.仿真结果表明,该算法鲁棒性好,抗干扰能力强,自适应控制效果良好.     

Adaptive critic autopilot design of bank-to-turn missiles using fuzzy basis function networks.

A new adaptive critic autopilot design for bank-to-turn missiles is presented. In this paper, the architecture of adaptive critic learning scheme contains a fuzzy-basis-function-network based associative search element (ASE), which is employed to approximate nonlinear and complex functions of bank-to-turn missiles, and an adaptive critic element (ACE) generating the reinforcement signal to tune the associative search element. In the design of the adaptive critic autopilot, the control law receives signals from a fixed gain controller, an ASE and an adaptive robust element, which can eliminate approximation errors and disturbances. Traditional adaptive critic reinforcement learning is the problem faced by an agent that must learn behavior through trial-and-error interactions with a dynamic environment, however, the proposed tuning algorithm can significantly shorten the learning time by online tuning all parameters of fuzzy basis functions and weights of ASE and ACE. Moreover, the weight updating law derived from the Lyapunov stability theory is capable of guaranteeing both tracking performance and stability. Computer simulation results confirm the effectiveness of the proposed adaptive critic autopilot.     

Robust adaptive critic control of nonlinear systems using fuzzy basis function networks: An LMI approach

This paper proposes an adaptive critic tracking control design for a class of nonlinear systems using fuzzy basis function networks (FBFNs). The key component of the adaptive critic controller is the FBFN, which implements an associative learning network (ALN) to approximate unknown nonlinear system functions, and an adaptive critic network (ACN) to generate the internal reinforcement learning signal to tune the ALN. Another important component, the reinforcement learning signal generator, requires the solution of a linear matrix inequality (LMI), which should also be satisfied to ensure stability. Furthermore, the robust control technique can easily reject the effects of the approximation errors of the FBFN and external disturbances. Unlike traditional adaptive critic controllers that learn from trial-and-error interactions, the proposed on-line tuning algorithm for ALN and ACN is derived from Lyapunov theory, thereby significantly shortening the learning time. Simulation results of a cart-pole system demonstrate the effectiveness of the proposed FBFN-based adaptive critic controller.     

Reinforcement learning for an ART-based fuzzy adaptive learningcontrol network

This paper proposes a reinforcement fuzzy adaptive learning control network (RFALCON), constructed by integrating two fuzzy adaptive learning control networks (FALCON), each of which has a feedforward multilayer network and is developed for the realization of a fuzzy controller. One FALCON performs as a critic network (fuzzy predictor), the other as an action network (fuzzy controller). Using temporal difference prediction, the critic network can predict the external reinforcement signal and provide a more informative internal reinforcement signal to the action network. The action network performs a stochastic exploratory algorithm to adapt itself according to the internal reinforcement signal. An ART-based reinforcement structure/parameter-learning algorithm is developed for constructing the RFALCON dynamically. During the learning process, structure and parameter learning are performed simultaneously. RFALCON can construct a fuzzy control system through a reward/penalty signal. It has two important features; it reduces the combinatorial demands of system adaptive linearization, and it is highly autonomous.     

A self-learning fuzzy logic controller using genetic algorithmswith reinforcements

This paper presents a new method for learning a fuzzy logic controller automatically. A reinforcement learning technique is applied to a multilayer neural network model of a fuzzy logic controller. The proposed self-learning fuzzy logic control that uses the genetic algorithm through reinforcement learning architecture, called a genetic reinforcement fuzzy logic controller, can also learn fuzzy logic control rules even when only weak information such as a binary target of “success” or “failure” signal is available. In this paper, the adaptive heuristic critic algorithm of Barto et al. (1987) is extended to include a priori control knowledge of human operators. It is shown that the system can solve more concretely a fairly difficult control learning problem. Also demonstrated is the feasibility of the method when applied to a cart-pole balancing problem via digital simulations     

Probabilistic dual heuristic programming-based adaptive critic

Adaptive critic (AC) methods have common roots as generalisations of dynamic programming for neural reinforcement learning approaches. Since they approximate the dynamic programming solutions, they are potentially suitable for learning in noisy, non-linear and non-stationary environments. In this study, a novel probabilistic dual heuristic programming (DHP)-based AC controller is proposed. Distinct to current approaches, the proposed probabilistic (DHP) AC method takes uncertainties of forward model and inverse controller into consideration. Therefore, it is suitable for deterministic and stochastic control problems characterised by functional uncertainty. Theoretical development of the proposed method is validated by analytically evaluating the correct value of the cost function which satisfies the Bellman equation in a linear quadratic control problem. The target value of the probabilistic critic network is then calculated and shown to be equal to the analytically derived correct value. Full derivation of the Riccati solution for this non-standard stochastic linear quadratic control problem is also provided. Moreover, the performance of the proposed probabilistic controller is demonstrated on linear and non-linear control examples.     

Wavelet reduced order observer based adaptive tracking control for a class of uncertain nonlinear systems using reinforcement learning

This Paper investigates the mean to design the reduced order observer and observer based controllers for a class of uncertain nonlinear system using reinforcement learning. A new design approach of wavelet based adaptive reduced order observer is proposed. The proposed wavelet adaptive reduced order observer performs the task of identification of unknown system dynamics in addition to the reconstruction of states of the system. Reinforcement learning is used via two wavelet neural networks (WNN), critic WNN and action WNN, which are combined to form an adaptive WNN controller. The “strategic” utility function is approximated by the critic WNN and is minimized by the action WNN. Owing to their superior learning capabilities, wavelet networks are employed in this work for the purpose of identification of unknown system dynamics. Using the feedback control, based on reconstructed states, the behavior of closed loop system is investigated. By Lyapunov approach, the uniformly ultimate boundedness of the closed-loop tracking error is verified. A numerical example is provided to verify the effectiveness of theoretical development.     

Adaptive fuzzy control of satellite attitude by reinforcementlearning

The attitude control of a satellite is often characterized by a limit cycle, caused by measurement inaccuracies and noise in the sensor output. In order to reduce the limit cycle, a nonlinear fuzzy controller was applied. The controller was tuned by means of reinforcement learning without using any model of the sensors or the satellite. The reinforcement signal is computed as a fuzzy performance measure using a noncompensatory aggregation of two control subgoals. Convergence of the reinforcement learning scheme is improved by computing the temporal difference error over several time steps and adapting the critic and the controller at a lower sampling rate. The results show that an adaptive fuzzy controller can better cope with the sensor noise and nonlinearities than a standard linear controller     

利用模糊系统的直升机的自适应控制

针对直升机动力学为非线性,且存在不确定因素和状态变化,设计利用模糊系统的自适应控制器.设计的控制器是系统的输出跟踪参考模型输出的直接调整模糊控制器参数的自适应控制器.又利用Lyapunov函数保证了闭环控制系统的稳定性并推导最优的自适应规律.实验结果表明,有外部扰动的情况下所设计的自适应控制器比模糊控制器对直升机控制具有良好的动态响应和稳定性,是一种非常有效的控制方法.     

一种基于小脑模型关节控制器评论-策略家的机器人跟踪控制算法

本文提出了一种基于小脑模型关节控制器(CMAC)的评论–策略家算法,设计不依赖模型的跟踪控制器,来解决机器人的跟踪问题.该跟踪控制器包含位置控制器和角度控制器,其输出分别为线速度和角速度.位置控制器由评价单元和策略单元组成,每个单元都采用CMAC算法,按改进δ学习规则在线调整权值.策略单元产生控制量;评判单元在线调整策略单元学习速率.以双轮驱动自主移动机器人为例,与固定学习速率CMAC做比较,仿真数据表明,基于CMAC的评论–策略家算法的跟踪控制器具有跟踪速度快,自适应能力强,配置参数范围宽,不依赖数学模型等特点.     

An adaptive critic approach to event‐triggered robust control of nonlinear systems with unmatched uncertainties

In this paper, we develop a novel event‐triggered robust control strategy for continuous‐time nonlinear systems with unmatched uncertainties. First, we build a relationship to show that the event‐triggered robust control can be obtained by solving an event‐triggered nonlinear optimal control problem of the auxiliary system. Then, within the framework of reinforcement learning, we propose an adaptive critic approach to solve the event‐triggered nonlinear optimal control problem. Unlike typical actor‐critic dual approximators used in reinforcement learning, we employ a unique critic approximator to derive the solution of the event‐triggered Hamilton‐Jacobi‐Bellman equation arising in the nonlinear optimal control problem. The critic approximator is updated via the gradient descent method, and the persistence of excitation condition is necessary. Meanwhile, under a newly proposed event‐triggering condition, we prove that the developed critic approximator update rule guarantees all signals in the auxiliary closed‐loop system to be uniformly ultimately bounded. Moreover, we demonstrate that the obtained event‐triggered optimal control can ensure the original system to be stable in the sense of uniform ultimate boundedness. Finally, a F‐16 aircraft plant and a nonlinear system are provided to validate the present event‐triggered robust control scheme.