Composite adaptive dynamic surface control using online recorded data

This paper presents an online recorded data‐based design of composite adaptive dynamic surface control for a class of uncertain parameter strict‐feedback nonlinear systems, where both tracking errors and prediction errors are applied to update parametric estimates. Differing from the traditional composite adaptation that utilizes identification models and linear filters to generate filtered modeling errors as prediction errors, the proposed composite adaptation integrates closed‐loop tracking error equations in a moving time window to generate modified modeling errors as prediction errors. The time‐interval integral operation takes full advantage of online recorded data to improve parameter convergence such that the application of both identification models and linear filters is not necessary. Semiglobal practical asymptotic stability of the closed‐loop system is rigorously established by the time‐scales separation and Lyapunov synthesis. The major contribution of this study is that composite adaptation based on online recorded data is achieved at the presence of mismatched uncertainties. Simulation results have been provided to verify the effectiveness and superiority of this approach. Copyright © 2016 John Wiley & Sons, Ltd.     

Formation control for a class of nonlinear multiagent systems using model‐free adaptive iterative learning

In this paper, the problem of formation control is considered for a class of unknown nonaffine nonlinear multiagent systems under a repeatable operation environment. To achieve the formation objective, the unknown nonlinear agent's dynamic is first transformed into a compact form dynamic linearization model along the iteration axis. Then, a distributed model‐free adaptive iterative learning control scheme is designed to ensure that all agents can keep their desired deviations from the reference trajectory over the whole time interval. The main results are given for the multiagent systems with fixed communication topologies and the extension to the switching topologies case is also discussed. The feature of this design is that formation control can be solved only depending on the input/output data of each agent. An example is given to demonstrate the effectiveness of the proposed method.     

Hybrid adaptive control for robot manipulators

A hybrid adaptive control scheme is proposed for robot manipulators. Unmodelled dynamics have been considered in the robot model. The standard RLS algorithm has been modified to take into account these unmodelled dynamics. Global stability of the system is ensured.     

Integrated direct/indirect adaptive robust motion trajectory tracking control of pneumatic cylinders

This paper studies the precision motion trajectory tracking control of a pneumatic cylinder driven by a proportional-directional control valve. An integrated direct/indirect adaptive robust controller is proposed. The controller employs a physical model based indirect-type parameter estimation to obtain reliable estimates of unknown model parameters, and utilises a robust control method with dynamic compensation type fast adaptation to attenuate the effects of parameter estimation errors, unmodelled dynamics and disturbances. Due to the use of projection mapping, the robust control law and the parameter adaption algorithm can be designed separately. Since the system model uncertainties are unmatched, the recursive backstepping technology is adopted to design the robust control law. Extensive comparative experimental results are presented to illustrate the effectiveness of the proposed controller and its performance robustness to parameter variations and sudden disturbances.     

Improving transient performance of adaptive control via a modified reference model and novel adaptation

This paper presents a new model reference adaptive control (MRAC) framework for a class of nonlinear systems to address the improvement of transient performance. The main idea is to introduce a nonlinear compensator to reshape the closed‐loop system transient, and to suggest a new adaptive law with guaranteed convergence. The compensator captures the unknown system dynamics and modifies the given nominal reference model and the control action. This modified controlled system can approach the response of the ideal reference model. The transient is easily tuned by a new design parameter of this compensator. The nominal adaptive law is augmented by new leakage terms containing the parameter estimation errors. This allows for fast, smooth and exponential convergence of both the tracking error and parameter estimation, which again improves overall reference model following. We also show that the required excitation condition for the estimation convergence is equivalent to the classical persistent excitation (PE) condition. In this respect, this paper provides an intuitive and numerically feasible approach to online validate the PE condition. The salient feature of the suggested methodology is that the rapid suppression of uncertainties in the controlled system can be achieved without using a large, high‐gain induced, learning rate in the adaptive laws. Extensive simulations are given to show the effectiveness and the improved response of the proposed schemes. Copyright © 2016 John Wiley & Sons, Ltd.     

On parameter convergence in least squares identification and adaptive control

Least squares estimation is appealing in performance and robustness improvements of adaptive control. A strict condition termed persistent excitation (PE) needs to be satisfied to achieve parameter convergence in least squares estimation. This paper proposes a least squares identification and adaptive control strategy to achieve parameter convergence without the PE condition. A modified modeling error that utilizes online historical data together with instant data is constructed as additional feedback to update parameter estimates, and an integral transformation is introduced to avoid the time derivation of plant states in the modified modeling error. On the basis of these results, a regressor filtering–free least squares estimation law is proposed to guarantee exponential parameter convergence by an interval excitation condition, which is much weaker than the PE condition. And then, an identification‐based indirect adaptive control law is proposed to establish exponential stability of the closed‐loop system under the interval excitation condition. Illustrative results considering both identification and control problems have verified the effectiveness and superiority of the proposed approach.     

DoS攻击下多智能体系统无模型自适应迭代学习跟踪控制EI北大核心CSCD

针对周期性拒绝服务(DoS)攻击下多智能体系统有限时间趋同跟踪控制问题,本文提出了一种无模型自适应迭代学习控制(MFAILC)算法.假设多智能体系统具有固定拓扑结构,并且仅有部分智能体可获取到期望轨迹信息.在多智能体系统数据传输过程中,需要经由对数量化器进行量化处理.首先,使用伪偏导数将智能体系统动态线性化,处理过程中考虑符合伯努利分布的周期性DoS攻击现象,在此基础上设计了MFAILC控制算法,其次,采用压缩映射方法给出了一个在期望意义下保证跟踪误差收敛的充分条件,并在理论上证明了所提算法的收敛性.所提算法只需利用系统的输入输出数据就可完成趋同跟踪任务.最后,仿真结果验证了所提算法的有效性.     

Distributed adaptive fuzzy iterative learning control of coordination problems for higher order multi-agent systems

In this paper, the adaptive fuzzy iterative learning control scheme is proposed for coordination problems of Mth order (M ≥ 2) distributed multi-agent systems. Every follower agent has a higher order integrator with unknown nonlinear dynamics and input disturbance. The dynamics of the leader are a higher order nonlinear systems and only available to a portion of the follower agents. With distributed initial state learning, the unified distributed protocols combined time-domain and iteration-domain adaptive laws guarantee that the follower agents track the leader uniformly on [0, T]. Then, the proposed algorithm extends to achieve the formation control. A numerical example and a multiple robotic system are provided to demonstrate the performance of the proposed approach.     

Model-free adaptive control design for nonlinear discrete-time processes with reinforcement learning techniques

This paper deals with the model-free adaptive control (MFAC) based on the reinforcement learning (RL) strategy for a family of discrete-time nonlinear processes. The controller is constructed based on the approximation ability of neural network architecture, a new actor-critic algorithm for neural network control problem is developed to estimate the strategic utility function and the performance index function. More specifically, the novel RL-based MFAC scheme is reasonable to design the controller without need to estimate y(k+1) information. Furthermore, based on Lyapunov stability analysis method, the closed-loop systems can be ensured uniformly ultimately bounded. Simulations are shown to validate the theoretical results.     

高阶无模型自适应迭代学习控制

针对一类非线性非仿射离散时间系统,提出了高阶无模型自适应迭代学习控制方案.控制器的设计和分析仅依赖于系统的输入/输出(I/O)数据,不需要已知任何其他知识.该方法采用了高阶学习律,可利用更多以前重复过程中的控制信息提高系统收敛性,且学习增益可通过"拟伪偏导数"更新律迭代调节.仿真结果验证了所提出算法的有效性.     

非线性系统的改进型自适应积分控制算法

针对传统非线性系统的积分控制,为减小稳态跟踪误差和防止积分饱和,设计了一种改进型的自适应积分控制。采用反馈线性化消去系统的非线性,在初始误差较大的条件下,引入惩罚函数根据误差动态调节比例系数和积分系数可抑制较大的超调量。应用Lyapunov稳定性理论,证明了在外部扰动有界的条件下,闭环控制系统一致最终有界。闭环系统稳定性的理论分析和仿真结果都验证了上述策略的可行性。     

同参数估计对偶的自适应控制算法

本文把线性和非线性系统统一处理。从自适应控制算法与参数估计算法的对偶性出发,提出了自适应控制算法的一种统一格式。这种格式算法简单,并在一定的条件下,能使控制误差一致的足够小。     

基于迭代扩张状态观测器的数据驱动最优迭代学习控制

针对一类带扰动有限时间内重复运行的离散时间非线性非仿射不确定系统,本文提出了一种基于迭代扩张状态观测器的数据驱动最优迭代学习控制方法.首先,提出了改进的迭代动态线性化方法,将被控系统线性化为与控制输入有关的仿射形式,并将不确定性合并到一个非线性项中;然后,设计了迭代扩张状态观测器对非线性不确定项进行估计,作为对扰动的补偿;最后,设计了性能指标函数,通过最优技术,提出了参数迭代更新律和最优学习控制律.本文通过数学分析,证明了跟踪误差的有界收敛性.仿真结果验证了方法的有效性.所提出的新型迭代动态线性化方法可很大程度上降低线性化后的控制增益的动态复杂性,使其易于估计.所提出的迭代扩张状态观测器可以在重复中学习,对非重复扰动可进行有效的估计.此外,本文控制器的设计与分析是数据驱动的控制方法,除了被控系统的输入输出数据以外,不需要任何其他模型信息.     

严格反馈互联系统分散式backstepping自适应迭代学习控制

基于Lyapunov分析方法,针对具有严格反馈形式的非线性互联系统,本文设计了一种分散式backstepping自适应迭代学习控制器.子系统之间的互联项为所有子系统输出项线性有界,为每个子系统设计的控制器仅采用该子系统的信息,不需要子系统之间相互传递信息.在控制器中,引入在时间轴和迭代轴上同时更新的自适应参数,以补偿子系统之间的互联项影响.通过采用本文给出的控制器,可使得每个子系统的输出跟踪相应的参考模型输出,仿真结果验证了本文算法的有效性.     

基于复合自适应律的直线电机自适应鲁棒控制

自适应鲁棒控制(ARC)能克服参数不确定性与扰动对系统的影响, 具有良好的输出跟踪性能. 然而常规ARC的参数估计值难以逼近真值. 为实现高性能的控制与准确的参数估计, 本文提出了基于复合自适应律的自适应鲁棒控制(CAARC). 该方法同时采用了输出误差与参数估计误差的相关信息构造自适应律, 具有比常规ARC更好的参数估计效果. 本文在理论上证明了CAARC的闭环稳定性与参数估计误差的收敛性, 并通过分析表明CAARC具有比常规ARC更好的输出跟踪性能, 最后通过仿真验证了该方法的有效性.     

A new method of parameter estimation and adaptive control of nonlinear systems with filterless least-squares

This paper is concerned with the parameter estimation and adaptive control problem for a sort of strict-feedback nonlinear systems with unknown parameters. In order to deal with the nonlinear functions, we develop a kind of least-squares estimator in the parameter estimation part. With such an estimator, we design the state-feedback control law and analyze the properties of the closed-loop system. To be concrete, the parameter estimator can guarantee the boundedness of parameter estimate error. At the same time, the adaptive control law is proposed to make the system states converge to zero and the equilibrium stable globally under some common assumptions. Finally, simulations are given to demonstrate the theoretical results.     

Identification based adaptive iterative learning controller

In recent years, more research in the control field has been in the area of self‐learning and adaptable systems, such as a robot that can teach itself to improve its performance. One of the more promising algorithms for self‐learning control systems is Iterative Learning Control (ILC), which is an algorithm capable of tracking a desired trajectory within a specified error limit. Conventional ILC algorithms have the problem of relatively slow convergence rate and adaptability. This paper suggests a novel approach by combining system identification techniques with the proposed ILC approach to overcome the aforementioned problems. The ensuing design procedure is explained and results are accrued from a number of simulation examples. A key point in the proposed scheme is the computation of gain matrices using the steepest descent approach. It has been found that the learning rule can be guaranteed to converge if certain conditions are satisfied. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

路径规划; 态势评估; 模糊逻辑; 贝叶斯网络

针对非线性动态系统辨识和控制的特点，对4层模糊神经网络进行了优化和改进，形成了动态模糊神经网络，提高了网络的稳定性和对动态系统的辨识能力，同时给出了基于Lyapunov函数稳定收敛定理的各权向量以及权矩阵学习速率的自适应调整算法．应用于非线性动态系统的辨识和控制仿真试验表明，改进后的动态模糊神经网络与模糊神经网络相比，可取得更好的辨识精度和跟踪控制效果。     

非参数模型控制在液位控制系统中的应用研究

针对工业控制过程中液位系统的时变和明显的滞后特征,研究了非参数模型控制方法在液位控制系统中的设计方案,讨论了控制算法中引入的伪偏导数的在线估计问题,实现了通过液位系统的输入输出信息并利用递归最小二乘法对伪偏导数进行在线估计的过程,仿真实验验证了非参数模型算法对液位控制的鲁棒性、快速性及抗干扰性,通过仿真比较,展示了该算法性能优于PID算法和模糊控制的结果.