基于动态神经网络自适应控制的主动悬架系统

主动悬挂系统中的液压执行器伺服系统是典型的仿射非线性系统，其参数也是时变的，要建立其准确的数学模型是很困难的。在该文中将主动悬架系统看作是一未知系统，利用动态神经网络对未知系统进行辨识，根据建模误差和参数的不确定性来调整网络的参数和结构并用动态神经网络的优化能力得出系统的控制率。在仿真试验中用动态神经网络所得到的控制算法对模型的输出进行跟踪控制，从仿真结果图可以看出这种控制器能获得较好的控制效果。     

基于自适应动态规划的一类带有时滞的离散时间非线性系统的最优控制策略

针对一类状态和控制变量均带有时滞的非线性系统的带有二次性能指标函数最优控制问题, 本文提出了一种基于新的迭代自适应动态规划算法的最优控制方案. 通过引进时滞矩阵函数, 应用动态规划理论, 本文获得了最优控制的显式表达式, 然后通过自适应评判技术获得最优控制量. 本文给出了收敛性证明以保证性能指标函数收敛到最优. 为了实现所提出的算法, 本文采用神经网络近似性能指标函数、计算最优控制策略、求解时滞矩阵函数、以及给非线性系统建模. 最后本文给出了两个仿真例子说明所提出的最优策略的有效性.     

汽车电子控制主动悬架系统设计的探讨

随着我国高新技术的发展,汽车日益走进人们的生活。汽车的出现改善了人们的生活,给人们出行带来了便利,但我国工程师并不满足于仅有的成就,他们仍在研究汽车的各方面性能,致力于研究如何设计汽车的悬架系统才会更好。前些年,人们就对汽车行驶的平稳性和安全性各执一词,要求使用不同软度的弹簧。通过汽车某一部位的加速度来估量车子行驶的平稳性,通过车轮的动载来估量汽车行驶的稳定性,而悬架系统设计的优化能够保证乘坐车辆时舒适和安全,代表着一种创新性的改革。对此,本文首先对悬架系统进行了系统性的阐述,并将被动悬架系统与主动悬架系统进行了对比分析,简要介绍了主动悬架系统目前的现状与趋势,指出了主动悬架系统研究的意义。     

基于MAS的电子政务系统自适应主动控制策略及其实现

在分布式系统和协同工作系统基础上，讨论了多主体控制结构及其交互模型。同时，面向政务系统平台的网络服务、资源服务和应用服务协作求解过程，提出了政务系统平台基于MAS的自适应主动控制结构，并以自主通信控制和协商控制策略的过程定义为例，介绍了电子政务平台环境自主协商控制的通信协议实现过程。     

动态规划最优控制在非线性系统中的应用

应用一种新的自适应动态最优化方法(ADP),在线实现对非线性连续系统的最优控制。首先应用汉密尔顿函数（Hamilton-Jacobi-Bellman, HJB）求解系统的最优控制,并应用神经网络BP算法对汉密尔顿函数中的性能指标进行估计,进而得到非线性连续系统的最优控制。同时引进一种新的自适应算法,基于参数误差,在线实现对系统进行动态最优求解,而且通过李亚普诺夫方法对参数收敛情况也进行详细的分析。最后,用仿真结果来验证所提出的方法的可行性。     

汽车主动悬架系统的控制方法综述

汽车主动悬架系统是车辆结构的重要组成部分之一,良好的汽车主动悬架系统可以大幅度提高汽车整体的稳定性和安全性。因此,针对几类典型汽车主动悬架系统的控制方法进行综述。首先,介绍汽车主动悬架系统模型和路面输入模型,包括四分之一主动悬架系统模型、二分之一主动悬架系统模型和随机路面模型。然后,阐述现有的典型控制方法对提升几类汽车主动悬架系统性能的影响。最后,总结并指出主动悬架系统的研究发展方向和趋势。     

基于ADP算法的带时滞及饱和的非线性系统最优控制

针对控制时滞及带饱和的一类离散时间非线性系统的最优控制问题,通过重构性能指标函数和对应的系统变换,处理了性能指标函数中的控制耦合项;继而引入一个合适的泛函,解决了控制带饱和问题.给出了一个新的性能指标函数,利用迭代自适应动态规划(ADP)算法获得最优控制.为实现该算法,采用神经网络逼近函数来求解最优控制问题.仿真结果验证了方法的有效性.     

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。     

应用轴距预描的主动悬架最优控制器设计

设计主动悬架的关键任务之一是寻找一个好的控制律，随机线性最优控制理论能为车辆提供良好的性能，得到了广泛的应用。轴距预描控制利用前轮处的路面信息作为预描变量对后轮进行控制，能充分发挥主动悬架的潜力。该文以悬架理论为基础，应用最优控制理论进行了结合轴距预瞄信息的主动悬架最优控制器的设计，并用一个卡尔曼滤波器估计系统状态。最后在MATLAB环境中建模和仿真，将有、无预描主动悬架系统的车身加速度、悬架动行程及车轮动位移3项指标进行了对比。仿真结果表明，采用轴距预描控制在低速情况下能较大改善后悬架系统的性能．验证了轴距预描控制的可行性。     

一类非线性奇异摄动系统的近似最优控制

针对一类非线性奇异摄动系统,基于自适应动态规划算法提出了一种新型的近似最优控制设计方法.该方法基于奇异摄动系统的快、慢Hamilton-Jacobi-Bellman(HJB)方程,从初始性能指标开始,通过神经网络的近似和控制律与性能指标的逐步更新迭代,最终收敛到最优的性能指标,而不用直接求解复杂的HJB方程.同时给出了...     

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 neural optimal control via command filter for nonlinear multi-agent systems including time-varying output constraints

In this article, an optimal command-filtered backstepping control approach is proposed for uncertain strict-feedback nonlinear multi-agent systems (MASs) including output constraints and unmodeled dynamics. One-to-one nonlinear mapping (NM) is utilized to recast constrained systems as corresponding unrestricted systems. A dynamical signal is applied to cope with unmodeled dynamics. Based on dynamic surface control (DSC), the feedforward controller is designed by introducing error compensating signals. The optimal feedback controller is produced applying adaptive dynamic programming (ADP) and integral reinforcement learning (IRL) techniques in which neural networks are utilized to approximate the relevant cost functions online with established weight updating laws. Therefore, the entire controller, including feedforward and feedback controllers, not only ensures that all signals in the closed-loop systems are cooperative semi-globally uniformly ultimately bounded (SGUUB) and the outputs maintain in the provided time-varying constraints, but also makes sure that the cost functions achieve minimization. A simulation example is presented to illustrate the feasibility of the proposed control algorithm.     

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.     

植保机钟摆式喷杆主动悬架自适应模糊控制

针对具有不确定性与参数时变性的钟摆式喷杆主动悬架系统的运动控制问题,设计出一种基于间接型自适应模糊控制方法的喷杆位姿主动控制器。为一种结构简单、成本低廉且应用广泛的钟摆式喷杆主动悬架系统建立了数学模型,并在此基础上,为了不失一般性,对系统模型进行了输入输出线性化变换。应用间接自适应模糊控制方法克服由于系统不确定性以及外部干扰带来的一些负面影响。此外,应用Lyapunov综合法设计控制器中调整参数的自适应律。仿真结果表明,所提出的控制方法具有快速响应性以及较强的自适应性和鲁棒性。所设计的控制器有利于提高植保机的喷雾均匀性及喷杆的稳定性。     

Adaptive optimal dynamic surface control of strict‐feedback nonlinear systems with output constraints

In this paper, an adaptive optimal control strategy is proposed for a class of strict‐feedback nonlinear systems with output constraints by using dynamic surface control. The controller design procedure is divided into two parts. One is the design of feedforward controller and the other is the design of optimal controller. To guarantee the satisfaction of output constraints in feedforward controller, nonlinear mapping is utilized to transform the constrained system into an unconstrained system. Neural‐network based adaptive dynamic programming algorithm is employed to approximate the optimal cost function and the optimal control law. By theoretical analysis, all the signals in the closed‐loop system are proved to be semi‐globally uniformly ultimately bounded and the output constraints are not violated. A numerical example illustrates the effectiveness of the proposed scheme.     

Event-triggered optimal tracking control of multiplayer unknown nonlinear systems via adaptive critic designs

In this article, the event-triggered optimal tracking control problem for multiplayer unknown nonlinear systems is investigated by using adaptive critic designs. By constructing a neural network (NN)-based observer with input–output data, the system dynamics of multiplayer unknown nonlinear systems is obtained. Subsequently, the optimal tracking control problem is converted to an optimal regulation problem by establishing a tracking error system. Then, the optimal tracking control policy for each player is derived by solving coupled event-triggered Hamilton-Jacobi (HJ) equation via a critic NN. Meanwhile, a novel weight updating rule is designed by adopting concurrent learning method to relax the persistence of excitation (PE) condition. Moreover, an event-triggering condition is designed by using Lyapunov's direct method to guarantee the uniform ultimate boundedness (UUB) of the closed-loop multiplayer systems. Finally, the effectiveness of the developed method is verified by two different multiplayer nonlinear systems.     

离散非线性零和博弈的事件驱动最优控制方案

在求解离散非线性零和博弈问题时,为了在有效降低网络通讯和控制器执行次数的同时保证良好的控制效果,本文提出了一种基于事件驱动机制的最优控制方案.首先,设计了一个采用新型事件驱动阈值的事件驱动条件,并根据贝尔曼最优性原理获得了最优控制对的表达式.为了求解该表达式中的最优值函数,提出了一种单网络值迭代算法.利用一个神经网络构建评价网.设计了新的评价网权值更新规则.通过在评价网、控制策略及扰动策略之间不断迭代,最终获得零和博弈问题的最优值函数和最优控制对.然后,利用Lyapunov稳定性理论证明了闭环系统的稳定性.最后,将该事件驱动最优控制方案应用到了两个仿真例子中,验证了所提方法的有效性.     

未知饱和控制系统有穷域最优控制

针对带有饱和执行器且局部未知的非线性连续系统的有穷域最优控制问题,设计了一种基于自适应动态规划(ADP)的在线积分增强学习算法,并给出算法的收敛性证明.首先,引入非二次型函数处理控制饱和问题.其次,设计一种由常量权重和时变激活函数构成的单一网络,来逼近未知连续的值函数,与传统双网络相比减少了计算量.同时,综合考虑神经网络产生的残差和终端误差,应用最小二乘法更新神经网络权重,并且给出基于神经网络的迭代值函数收敛到最优值的收敛性证明.最后,通过两个仿真例子验证了算法的有效性.     

Adaptive dynamic surface control for uncertain nonaffine nonlinear systems

This paper focuses on the problem of adaptive control for uncertain nonaffine nonlinear systems. The original nonaffine systems are transformed into the augmented affine systems via adding an auxiliary integrator, which makes the explicit control design possible. By introducing a modified sliding mode filter in each step, a novel adaptive dynamic surface controller is proposed, where the ‘explosion of complexity’ problem inherent in the backstepping design is avoided. It is proven rigorously that for any initial control condition, the proposed adaptive scheme is able to ensure the semiglobal uniformly ultimately boundedness of all signals in the closed loop. An illustrative example is carried out to verify the effectiveness of the proposed approach. Copyright © 2016 John Wiley & Sons, Ltd.     

具有输入时滞的主动悬挂系统的减振控制

研宄具有输入时滞的汽车主动悬挂系统在路面扰动下的减振控制器设计问题.首先根据汽车悬挂系统的特点,从实用性的角度出发化简了悬挂系统的数学模型.然后提出一种变量代换方法,将具有输入时滞的主动悬挂系统转换为形式上不含时滞的系统.针对转换后的无时滞系统,设计出具有输入时滞的主动悬挂系统减振控制器:又从控制器的成本和易实现性出发添加了一个状态观测器.在这种系统结构下,设计出一种具有记忆和积分特性的主动悬挂系统减振控制策略.仿真结果验证了这种设计控制器的方法是有效的.