Efficient decentralized iterative learning tracker for unknown sampled-data interconnected large-scale state-delay system with closed-loop decoupling property

In this paper, an efficient decentralized iterative learning tracker is proposed to improve the dynamic performance of the unknown controllable and observable sampled-data interconnected large-scale state-delay system, which consists of N multi-input multi-output (MIMO) subsystems, with the closed-loop decoupling property. The off-line observer/Kalman filter identification (OKID) method is used to obtain the decentralized linear models for subsystems in the interconnected large-scale system. In order to get over the effect of modeling error on the identified linear model of each subsystem, an improved observer with the high-gain property based on the digital redesign approach is developed to replace the observer identified by OKID. Then, the iterative learning control (ILC) scheme is integrated with the high-gain tracker design for the decentralized models. To significantly reduce the iterative learning epochs, a digital-redesign linear quadratic digital tracker with the high-gain property is proposed as the initial control input of ILC. The high-gain property controllers can suppress uncertain errors such as modeling errors, nonlinear perturbations, and external disturbances (Guo et al., 2000) [18]. Thus, the system output can quickly and accurately track the desired reference in one short time interval after all drastically-changing points of the specified reference input with the closed-loop decoupling property.     

Design and experimental realization of a robust decentralized PI controller for a coupled tank system

This paper presents design and realization of a robust decentralized PI controller for regulating the level of a coupled tank system. The proposed controller is designed based on a predefined reference transfer function model in which we adopt a frequency matching of actual and reference models. Realization of control algorithms for a multivariable system is often complicated owing to uncertainties in the process dynamics. In this paper, initially a frequency response fitting model reduction technique is adopted to obtain a First Order Plus Dead Time (FOPDT) model of each higher order decoupled subsystem. Further, using the obtained reduced order model, the proposed robust decentralized PI controller is designed. The stability and performance of the proposed controller are verified by considering multiplicative input and output uncertainties. The performance of the proposed robust decentralized controller has been compared with that of a decentralized PI controller. To validate the performance of the proposed control approach, real-time experimentation is pursed on a Feedback Instrument manufactured coupled tank system.     

离散系统单调收敛高阶迭代学习控制

研究了一类离散线性时不变系统高阶迭代学习控制在相应范数意义下的单调收敛条件,给出了对给定目标函数迭代学习控制参数的最优解,并讨论了其收敛速度。常见的离散P型、D型及PD型ILC算法均可看作是所讨论算法的特例。仿真结果表明采用给出的最优设计具有更好的迭代学习单调收敛性能。     

B-spline network-based iterative learning control for trajectory tracking of a piezoelectric actuator

This paper presents the trajectory tracking approach of a piezoelectric actuator using an iterative learning control (ILC) scheme based on B-spline network (BSN) filtering. The ILC scheme adopts a state-compensated iterative learning formula, which compensates for the state difference between two consecutive iterations in order that the iterative learning can learn from the tracking errors of the previous iteration effectively. The BSN is used to attenuate the noises and retrieve the signals of the tracking errors for the ILC. The BSN serves as a unique filter which generally does not have zero-phase responses. Design details on the ILC scheme using BSN filtering are discussed in the paper. Extensive experiments of tracking two desired trajectories for a piezoelectric actuator are presented. The experimental results show that the state-compensated ILC scheme using BSN filtering can achieve fast error convergence and keep small steady-state tracking errors close to the system noise level. This research thus relaxes the restriction of the zero-phase criterion commonly applied to the ILC filtering in the literature.     

Decentralized adaptive neural prescribed performance control for high-order stochastic switched nonlinear interconnected systems with unknown system dynamics

In this paper, the problem of decentralized adaptive neural backstepping control is investigated for high-order stochastic nonlinear systems with unknown interconnected nonlinearity and prescribed performance under arbitrary switchings. For the control of high-order nonlinear interconnected systems, it is assumed that unknown system dynamics and arbitrary switching signals are unknown. First, by utilizing the prescribed performance control (PPC), the prescribed tracking control performance can be ensured, while the requirement for the initial error is removed. Second, at each recursive step, only one adaptive parameter is constructed to overcome the over-parameterization, and RBF neural networks are employed to tackle the difficulties caused by completely unknown system dynamics. At last, based on the common Lyapunov stability method, the decentralized adaptive neural control method is proposed, which decreases the number of learning parameters. It is shown that the designed common controller can ensure that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB), and the prescribed tracking control performance is guaranteed under arbitrary switchings. The simulation results are presented to further illustrate the effectiveness of the proposed control scheme.     

Iterative learning control scheme for manipulators including actuator dynamics

This paper presents the iterative learning control for the industrial robot manipulators including actuator dynamics. Motivated by human learning, the basic idea of iterative learning control is to use information from previous execution of a trial in order to improve performance from trial to trial. This is an advantage, when accurate model of the system is not available as friction and actuator dynamics, though present in the system, are not modeled to reduce the computational complexity. In this paper different aspects of ILC including the design schemes and control algorithms are covered. The learning control scheme comprises two types of control laws: a linear feedback law and a feed-forward control law. In the feedback loop, the fixed gain PD controller provides stability of the system and keeps its state errors within uniform bounds. In the feed-forward path, a learning control rule/strategy is exploited to track the entire span of a reference input over a sequence of iterations. Algorithms are verified through detailed simulation results on a two DOF robot manipulator.     

SCARA机器人的自适应迭代学习轨迹跟踪控制

为了减小执行重复运动任务机器人的末端位置误差,提出了自适应迭代学习轨迹跟踪控制算法。根据拉格朗日方程得到SCARA机器人的动力学模型,设计了控制力矩的迭代算法,利用Lyapunov函数对该算法的稳定性进行了理论证明,搭建了具有典型机械结构的SCARA机器人实验平台。通过实验验证了自适应迭代学习控制算法能有效减小SCARA机器人的末端位置误差,具有较强的可执行性。     

基于改进PSO算法的电液位置伺服系统MRAC跟踪控制

针对电液位置伺服系统控制性能不佳的问题,提出一种基于改进PSO算法优化的模型参考自适应(Model Reference Adaptive Control,MRAC)跟踪控制方法。首先,建立电液位置伺服系统数学模型,设计出模型参考自适应控制器;其次,分析PSO算法、APSO算法在参数寻优过程中的不足,提出一种改进的PSO算法;最后,将改进的PSO算法用于模型参考自适应控制器以改善其控制性能。结果表明,改进PSO算法优化的模型参考自适应控制具有响应速度快、跟踪精度高的优点。     

Finite-time decentralized adaptive neural constrained control for interconnected nonlinear time-delay systems with dynamics couplings among subsystems

The problem of finite-time decentralized neural adaptive constrained control is studied for large-scale nonlinear time-delay systems in the non-affine form. The main features of the considered system are that 1) unknown unmatched time-delay interactions are considered, 2) the couplings among the nested subsystems are involved in uncertain nonlinear systems, 3) based on finite-time stability approach, asymmetric saturation actuators and output constraints are studied in large-scale systems. First, the smooth asymmetric saturation nonlinearity and barrier Lyapunov functions are used to achieve the input and output constraints. Second, the appropriately designed Lyapunov-Krasovskii functional and the property of hyperbolic tangent functions are used to deal with the unknown unmatched time-delay interactions, and the neural networks are employed to approximate the unknown nonlinearities. Note that, due to unknown time-delay interactions and the couplings among subsystems, the controller design is more meaningful and challenging. At last, based on finite-time stability theory and Lyapunov stability theory, a decentralized adaptive controller is proposed, which decreases the number of learning parameters. It is shown that the designed controller can ensure that all closed-loop signals are bounded and the tracking error converges to a small neighborhood of the origin. The simulation studies are presented to show the effectiveness of the proposed method.     

高精度张力控制器设计及补偿算法研究

针对绕线机对张力和速度较高的控制要求,提出了一种高精度张力解耦控制算法。在详细分析系统数学模型的基础上,将系统解耦为开卷辊恒张力控制和复卷辊恒速度控制。该算法将参考速度预测算法、前馈扰动补偿算法、张力反馈控制算法有机结合起来,实现了对张力和速度的高性能控制。通过构建以绕线机为对象的实验平台,对传统的张力反馈控制算法和所提出的控制算法分别进行实验。实验结果表明:该控制策略能有效地抑制卷径变化对张力和速度的影响,实现张力和速度的稳定控制。
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重复轨迹运动迭代学习控制的频域分析

应用频域分析方法讨论了一类闭环迭代学习算法的收敛条件和性能,指出其比Arimoto开环迭代学习算法具有明显的优越性,并在讨论迭代收敛条件的基础上给出了闭环迭代学习算法的频域设计方法.     

冗余自由度仿人机械臂自适应迭代学习控制研究

在深入研究自适应迭代学习控制理论、七自由度乒乓球机械臂动力学模型及轨迹规划的基础上,提出将改进后的自适应迭代学习控制算法运用到带有重复时变干扰的冗余自由度机械臂上。该控制系统旨在实现两大目标：一是使乒乓球机械臂准确快速地跟踪参考轨迹并在末点达到指定的击球速度;二是引入饱和函数减小输入转矩的抖振。Lyapunov理论分析及MATLAB仿真验证了整个控制系统的有效性：当迭代次数增加时,跟踪误差关于有限时间区间内一致收敛到零;加快迭代学习的收敛速度,并消除抖振。     

双马达回转同步驱动系统建模与控制研究

针对双液压马达回转高性能同步驱动问题,引入无阻尼行星系齿轮传动弹性动力学理论,基于双液压马达回转运动特性建立了系统非线性动力学模型;针对回转系统跟踪性能和同步性能要求,引入迭代学习控制算法(ILC),提出了结合离散化PID控制器结构的IL-PID同步控制策略。该控制策略基于“等同式”同步控制原理,在各单通道内部采用独立的离散化PID控制实现系统跟踪性能,在多通道间采用基于闭环D型学习律的IL控制实现系统同步性能。在五自由度液压伺服机械手上的实际应用结果表明,该控制策略相比于传统的PID控制具有较好的跟踪性能和同步驱动性能。     

Adaptive Control of Multivariable Process Using Recurrent Neural Networks

Abstract

Industrial processes are naturally multivariable in nature, which also exhibit non-linear behavior and complex dynamic properties. The multivariable four-tank system has attracted recent attention, as it illustrates many concepts in multivariable control, particularly interaction, transmission zero, and non-minimum phase characteristics that emerge from a simple cascade of tanks. So, the multivariable laboratory process of four interconnected water tanks is considered for modeling and control. For processes which show nonlinear and multivariable characteristics, classical control strategies like PIDs have performance limitations. Hence, intelligent approaches like Neural Networks (NN) is an important term in this juncture. The use of Recurrent Neural Network (RNN) is apt for modeling and control of nonlinear dynamic processes as it contains the past information about the process. The objective of the current study is to design and implement an adaptive control system using RNN for a nonlinear multivariable process.

The proposed adaptive design comprises an estimator based on RNN, which adapts online and predicts one step ahead output. A Recursive Least Square (RLS) based back propagation algorithm is used for training the network. The controller used is also a RNN, which minimizes the difference between the predicted output and reference trajectory. The objective function is minimized using a steepest descent algorithm which gives the optimum control input. Desired performance of the system is ensured by the parallel operation of both. The proposed control strategy is implemented in a laboratory scale four tank system. The trajectory tracking and disturbance rejection response obtained are compared with the response obtained by using a well designed decoupled, decentralized IMC controller.     

A novel design strategy for iterative learning and repetitive controllers of systems with a high modal density: Theoretical background

This paper discusses the design and application of iterative learning control (ILC) and repetitive control (RC) for high modal density systems. Typical examples of these systems are structural and acoustical systems considered in active structural acoustic control (ASAC) and active noise control (ANC) applications. The application of traditional ILC and RC design techniques, which are based on a parametric system model, on systems with a high modal density has several important drawbacks: the design procedure is complex, the controllers require much computational power and the robustness of the controllers is low. This paper describes a novel strategy to design noncausal ILC and RC filters, which is especially suited for high modal density systems. Since it does not require a parametric system model, the novel strategy avoids several drawbacks of the traditional techniques: no cumbersome parametric model estimation is required; the ILC and RC controllers are robust to small changes of the poles and zeros of the controlled system; and the complexity of the ILC and RC control filters is restricted. A crucial element in the proposed strategy is the noncausal filtering in the ILC and RC controllers, which requires the availability of a trigger signal to announce a new ILC trial or RC period in advance. A numerical validation on a simulation model proves the potential of the developed strategy.     

基于分数阶微积分的机械臂迭代滑模控制策略研究

为提高工业机械臂的控制性能,将分数阶微积分理论与迭代学习控制及滑模控制相结合,提出一种有效的分数阶迭代滑模控制策略.在控制器的设计过程中,分别采用分数阶趋近律与分数阶滑模控制律两种方法将分数阶微积分引入到迭代滑模控制中,提出分数阶迭代滑模控制策略.并使用李雅普诺夫理论分析系统的稳定性.然后以一个两关节机械臂为例,通过MATLAB仿真对所提出的控制策略进行了验证.实验表明:分数阶迭代滑模控制策略可以有效提高关节的跟踪速度和跟踪精度,减小跟踪误差,具有较强的鲁棒性,并有效地抑制了滑模控制的抖振现象.     

激光跟踪仪精密跟踪系统的设计

对激光跟踪仪的跟踪伺服控制系统进行了整体研究并给出了总体设计方案。针对跟踪目标的精密探测问题,研究了新型探测手段以及微弱光电信号的精细调理技术与数字滤波方法,使得脱靶量探测稳定性优于±2.0μm。针对跟踪角度精密测量问题,设计了圆光栅数据采集系统,实现了角度脉冲的细分、辨向与准确计数;基于谐波分析方法建立了跟踪过程中的误差补偿模型,将角度测量精度由3.5″提高到1.5″。建立了跟踪伺服电机的数学模型,分析了电流环在跟踪控制中的作用机理,提出了电流、速度、位置三闭环控制结构和复合跟踪控制策略。跟踪实验表明:系统最远跟踪距离不小于41.7m,跟踪速度不低于2.0m/s。该项技术还能为空间动态目标跟踪、激光通信等提供有益借鉴。     

An adaptive sliding mode control technology for weld seam tracking

A novel adaptive sliding mode control algorithm is derived to deal with seam tracking control problem of welding robotic manipulator, during the process of large-scale structure component welding. The proposed algorithm does not require the precise dynamic model, and is more practical. Its robustness is verified by the Lyapunov stability theory. The analytical results show that the proposed algorithm enables better high-precision tracking performance with chattering-free than traditional sliding mode control algorithm under various disturbances.     

基于分数阶微积分的机械臂迭代滑模控制策略研究

为提高工业机械臂的控制性能,将分数阶微积分理论与迭代学习控制及滑模控制相结合,提出一种有效的分数阶迭代滑模控制策略.在控制器的设计过程中,分别采用分数阶趋近律与分数阶滑模控制律两种方法将分数阶微积分引入到迭代滑模控制中,提出分数阶迭代滑模控制策略.并使用李雅普诺夫理论分析系统的稳定性.然后以一个两关节机械臂为例,通过MATLAB仿真对所提出的控制策略进行了验证.实验表明:分数阶迭代滑模控制策略可以有效提高关节的跟踪速度和跟踪精度,减小跟踪误差,具有较强的鲁棒性,并有效地抑制了滑模控制的抖振现象.     

Event-triggered decentralized adaptive fault-tolerant control of uncertain interconnected nonlinear systems with actuator failures

This paper investigates the event-triggered decentralized adaptive tracking problem of a class of uncertain interconnected nonlinear systems with unexpected actuator failures. It is assumed that local control signals are transmitted to local actuators with time-varying faults whenever predefined conditions for triggering events are satisfied. Compared with the existing control-input-based event-triggering strategy for adaptive control of uncertain nonlinear systems, the aim of this paper is to propose a tracking-error-based event-triggering strategy in the decentralized adaptive fault-tolerant tracking framework. The proposed approach can relax drastic changes in control inputs caused by actuator faults in the existing triggering strategy. The stability of the proposed event-triggering control system is analyzed in the Lyapunov sense. Finally, simulation comparisons of the proposed and existing approaches are provided to show the effectiveness of the proposed theoretical result in the presence of actuator faults.