Iterative learning control for integrated system of robot and machine tool

This study is concerned with the integrated system of a robot and a machine tool. The major task of robot is loading the workpiece to the machine tool for contour cutting. An iterative learning control (ILC) algorithm is proposed to improve the accuracy of the finished product. The proposed ILC is to modify the input command of the next machining cycle for both robot and machine tool to iteratively enhance the output accuracy of the robot and machine tool. The modified command is computed based on the current tracking/contour error. For the ILC of the robot, tracking error is considered as the control objective to reduce the tracking error of motion path, in particular, the error at the endpoint. Meanwhile, for the ILC of the machine tool, contour error is considered as the control objective to improve the contouring accuracy, which determines the quality of machining. In view of the complicated contour error model, the equivalent contour error instead of the actual contour error is taken as the control objective in this study. One challenge for the integrated system is that there exists an initial state error for the machine tool dynamics, violating the basic assumption of ILC. It will be shown in this study that the effects of initial state error can be significantly reduced by the ILC of the robot. The proposed ILC algorithm is verified experimentally on an integrated system of commercial robot and machine tool. The experimental results show that the proposed ILC can achieve more than 90% of reduction on both the RMS tracking error of the robot and the RMS contour error of the machine tool within six learning iterations. The results clearly validate the effectiveness of the proposed ILC for the integrated system.     

Precise and smooth contact force control for a hybrid mobile robot used in polishing

Contact force is dominant in robotic polishing since it directly determines the material removal. However, due to the position and stiffness disturbance of mobile robotic polishing and the nonlinear contact process between the robot and workpiece, how to realize precise and smooth contact force control of the hybrid mobile polishing robot remains challenging. To solve this problem, the force tracking error is investigated, which indicates that the force overshoot mainly comes from the input step signal and the environmental disturbance causes force tracking error in stable state. Accordingly, an integrated contact force control method is proposed, which combines feedforward of the desired force and adaptive variable impedance control. The nonlinear tracking differentiator is used to smooth the input step signal of the desired force for force overshoot reduction. Through modeling of the force tracking error, the adaptive law of the damping parameter is established to compensate disturbance. After theoretical analysis and simulation verification, the polishing experiment is carried out. The improvement in force control accuracy and roughness of the polished surface proves the effectiveness of the proposed method. Sequentially, the proposed method is employed in the polishing of a 76-meter wind turbine blade. The measurement result indicates that the surface roughness after mobile robotic polishing is better than Ra1.6. The study provides a feasible approach to improve the polishing performance of the hybrid mobile polishing robot.     

Adaptive neural network control of uncertain MIMO nonlinear systems with input saturation

In this paper, an adaptive neural network (NN) tracking controller is developed for a class of uncertain multi-input multi-output (MIMO) nonlinear systems with input saturation. Radial basis function neural networks are utilized to approximate the unknown nonlinear functions in the MIMO system. A novel auxiliary system is developed to compensate the effects induced by input saturation (in both magnitude and rate) during tracking control. Endowed with a switching structure that integrates two existing representative auxiliary system designs, this novel auxiliary system improves control performance by preserving their advantages. It provides a comprehensive design structure in which parameters can be adjusted to meet the required control performance. The auxiliary system signal is utilized in both the control law and the neural network weight-update laws. The performance of the resultant closed-loop system is analyzed, and the bound of the transient error is established. Numerical simulations are presented to demonstrate the effectiveness of the proposed adaptive neural network control.     

高动态离心机系统的自抗扰控制设计

针对传统PID在控制高速精密离心机系统时难以满足其高动态过程的要求,对系统目标过渡过程进行安排并设计了自抗扰控制器.所提出的自抗扰控制器包括3个部分:跟踪微分器、扩张状态观测器和误差反馈控制器.由于离心机在启动和制动阶段,系统状态会经历一个快速变化的过程,所以在离心机系统动态变化阶段采用跟踪微分器对目标函数进行过渡过程安排,防止系统出现过大超调;并且设计了扩张状态观测器对系统未知干扰进行估计和补偿;补偿后采用误差反馈控制器实现离心机系统高动态过程的跟踪控制.最后通过对自抗扰控制进行参数整定,使得系统满足所提出的各项性能指标要求.仿真结果验证了相比于传统PID控制,所提出的自抗扰控制器在超调量,调节时间以及稳态控制精度等性能指标上具有优越性.     

Prescribed Performance Control of One-DOF Link Manipulator With Uncertainties and Input Saturation Constraint

In this paper, we mainly address the position control problem for one-degree of freedom (DOF) link manipulator despite uncertainties and the input saturation via the backstepping technique, active disturbance rejection control (ADRC) as well as predefined tracking performance functions. The extended state observer (ESO) is employed to compensate uncertain dynamics and disturbances, and it does not rely on the accurate model of systems. The tracking differentiator (TD) is utilized to substitute the derivative of the virtual control signals, and the explosion of complexity caused by repeated differentiations of nonlinear functions is removed. The auxiliary system is used to deal with the control input limitation, and the tracking accuracy and speed are improved by predefined tracking performance functions. With the help of the input-to-state stability (ISS) and Lyapunov stability theories, it is proven that the tracking error can be gradually converged into arbitrarily small neighborhood of the origin, and the tracking error is adjusted by suitable choice of control parameters. The simulation results are presented for the verification of the theoretical claims.      

阀控电液伺服压力脉冲实验自抗扰控制方法

针对压力脉冲疲劳测试系统在测试过程中工件体积的不确定性以及脉冲疲劳测试系统的机械与液压双动态耦合问题,首先,提出通过奇异值摄动理论将压力脉冲疲劳测试系统的多动态耦合进行解耦降阶;然后,利用自抗扰控制算法实现对系统模型降阶误差以及体积参数不确定性等干扰的补偿,保证测试系统输出的压力对指令信号的准确跟踪;最后,对基于降阶模型的自抗扰算法的稳定性和误差收敛性进行理论和定量分析,并对算法的可行性和有效性进行联合仿真和实验验证.研究结果表明,基于降阶模型的自抗扰控制算法对压力脉冲疲劳测试系统中工件体积参数的变化具有良好的鲁棒性且能够有效估计和补偿系统模型降阶误差等干扰,其跟踪性能相比于传统的PID控制器最大提升35.4%.     

Compensation for state‐dependent nonlinearity in a modified repetitive control system

This paper presents an estimation and compensation of state‐dependent nonlinearity for a modified repetitive control system. It is based on the equivalent‐input‐disturbance (EID) approach. The nonlinearity is estimated by an EID estimator and compensated by incorporation of the estimate into the repetitive control input. A two‐dimensional model of the EID‐based modified repetitive control system is established that enables the preferential adjustment of control and learning actions by means of 2 tuning parameters. The singular‐value‐decomposition technique and Lyapunov stability theory are used to derive a linear‐matrix‐inequality–based asymptotic stability condition. Exploiting the stability condition and an overall performance evaluation index, a design algorithm is developed. Simulation results for the tracking control of a chuck‐workpiece system show that the method not only compensates state‐dependent nonlinearity but also improves the tracking performance for the periodic reference input, thereby demonstrating the validity of the method.     

具有输入输出约束的无人直升机预设性能安全跟踪控制

针对无人直升机姿态与高度系统存在未知外部干扰、输入饱和、姿态与高度约束等问题, 本文提出一种具 有输入输出约束的预设性能安全跟踪控制方法. 首先, 针对无人直升机的姿态与高度约束, 通过设计一类边界保护 算法, 构建了新的安全期望跟踪信号. 为了保证系统对于安全期望跟踪信号的跟踪性能, 将预设性能函数与边界保 护算法进行结合, 并对跟踪误差进行转换. 针对系统的输入饱和现象, 使用Sigmoid函数进行逼近; 同时, 针对饱和函 数的逼近误差与未知外部干扰构成的复合干扰, 采用参数自适应方法对其上界进行逼近. 然后, 结合反步控制方法 设计了安全跟踪控制器, 并通过Lyapunov稳定性理论证明了闭环系统所有信号的收敛性, 保证了无人直升机的安全 跟踪性能. 最终, 通过数值仿真验证了所提控制方法的有效性.     

Incorporation of a Regularization Term to Control Negative Correlation in Mixture of Experts

Combining accurate neural networks (NN) in the ensemble with negative error correlation greatly improves the generalization ability. Mixture of experts (ME) is a popular combining method which employs special error function for the simultaneous training of NN experts to produce negatively correlated NN experts. Although ME can produce negatively correlated experts, it does not include a control parameter like negative correlation learning (NCL) method to adjust this parameter explicitly. In this study, an approach is proposed to introduce this advantage of NCL into the training algorithm of ME, i.e., mixture of negatively correlated experts (MNCE). In this proposed method, the capability of a control parameter for NCL is incorporated in the error function of ME, which enables its training algorithm to establish better balance in bias-variance-covariance trade-off and thus improves the generalization ability. The proposed hybrid ensemble method, MNCE, is compared with their constituent methods, ME and NCL, in solving several benchmark problems. The experimental results show that our proposed ensemble method significantly improves the performance over the original ensemble methods.     

具有磁滞输入的可调金属切削系统鲁棒自适应动态面控制

针对既有时滞环节又存在磁滞输入的可调金属切削系统,提出了一种改进的自适应动态面控制方法,其特点为:1)设计了带有跟踪误差性能指标函数的鲁棒自适应动态面控制算法,并结合神经网络,使其能够保证系统的跟踪误差及其过渡过程在预先任意给定的范围内;2)克服了反推控制方案中的"微分爆炸"问题,简化控制器结构;3)估计神经网络权值向量的范数而不是估计权值向量,极大地减少系统的计算负担,便于实时控制.仿真结果验证了该控制方法的有效性.     

基于自适应动态规划的非线性鲁棒近似最优跟踪控制

为克服现有近似最优跟踪控制方法只能跟踪连续可微参考输入的局限,本文针对一类具有未知动态的连续时间非线性时不变仿射系统,提出了一种新的基于自适应动态规划的鲁棒近似最优跟踪控制方法.首先采用递归神经网络建立系统模型,然后建立评价神经网络对最优性能指标进行估计,从而得到最优性能指标偏导数的估计值,进而得到近似最优跟踪控制器,最后利用系统输出与参考输入之间的跟踪误差设计鲁棒项对神经网络建模误差进行补偿.分别针对两个非线性系统进行仿真实验,仿真结果表明了所提方法的有效性和优越性.     

Adaptive backstepping control for a class of nonlinear systems using neural network approximations

In this paper, an adaptive neural network (NN) backstepping technique is developed for tracking control of a class of nonlinear systems. NNs are used to compensate for the unknown nonlinear functions in the system. A systematic backstepping approach is established to synthesize the adaptive NN control scheme that ensures the boundedness of all the signals in the closed‐loop system, and yields a small tracking error. The issue of transient performance is also addressed under an analytical framework. The effectiveness of the proposed scheme is demonstrated by computer simulations. Copyright © 2004 John Wiley & Sons, Ltd.     

Observer-critic structure-based adaptive dynamic programming for decentralised tracking control of unknown large-scale nonlinear systems

In this paper, a decentralised tracking control (DTC) scheme is developed for unknown large-scale nonlinear systems by using observer-critic structure-based adaptive dynamic programming. The control consists of local desired control, local tracking error control and a compensator. By introducing the local neural network observer, the subsystem dynamics can be identified. The identified subsystems can be used for the local desired control and the control input matrix, which is used in local tracking error control. Meanwhile, Hamiltonian-Jacobi-Bellman equation can be solved by constructing a critic neural network. Thus, the local tracking error control can be derived directly. To compensate the overall error caused by substitution, observation and approximation of the local tracking error control, an adaptive robustifying term is employed. Simulation examples are provided to demonstrate the effectiveness of the proposed DTC scheme.     

A simplified approach to force control for electro-hydraulic systems

In this paper, a Lyapunov-based control algorithm is developed for force tracking control of an electro-hydraulic actuator. The developed controller relies on an accurate model of the system. To compensate for the parametric uncertainties, a Lyapunov-based parameter adaptation is applied. The adaptation uses a variable structure approach to account for asymmetries present in the system. The coupled control law and the adaptation scheme are applied to an experimental valve-controlled cylinder. Friction modeling and compensation are also discussed. The experimental results show that the nonlinear control algorithm, together with the adaptation scheme, gives a good performance for the specified tracking task. The original adaptive control law is then simplified in several stages with an examination of the output tracking at each stage of simplification. It is shown that the original algorithm can be significantly simplified without too significant a loss of performance. The simplest algorithm corresponds to an adaptive velocity feedback term coupled with a simple force error feedback.     

Approximation-based adaptive control for a class of mobile robots with unknown skidding and slipping

An adaptive tracking control approach using function approximation technique is proposed for trajectory tracking of Type (2,0) wheeled mobile robots with unknown skidding and slipping in polar coordinates and at the dynamic level. The nonlinear disturbance observer (NDO) is used to estimate a nonlinear disturbance term including unknown skidding and slipping. The adaptive control system is designed via the function approximation technique using neural networks employed to compensate the NDO error. It is proved that all signals of the controlled closed-loop system are uniformly bounded and the point tracking errors converge to an adjustable neighborhood of the origin regardless of large initial tracking errors and unknown skidding and slipping. Simulation results are presented to validate the good tracking performance and robustness of the proposed control system against unknown skidding and slipping.     

Tracking control of a closed-chain five-bar robot with two degrees of freedom by integration of an approximation-based approach and mechanical design

The trajectory tracking problem of a closed-chain five-bar robot is studied in this paper. Based on an error transformation function and the backstepping technique, an approximation-based tracking algorithm is proposed, which can guarantee the control performance of the robotic system in both the stable and transient phases. In particular, the overshoot, settling time, and final tracking error of the robotic system can be all adjusted by properly setting the parameters in the error transformation function. The radial basis function neural network (RBFNN) is used to compensate the complicated nonlinear terms in the closed-loop dynamics of the robotic system. The approximation error of the RBFNN is only required to be bounded, which simplifies the initial "trail-and-error" configuration of the neural network. Illustrative examples are given to verify the theoretical analysis and illustrate the effectiveness of the proposed algorithm. Finally, it is also shown that the proposed approximation-based controller can be simplified by a smart mechanical design of the closed-chain robot, which demonstrates the promise of the integrated design and control philosophy.     

Mixed H/sub 2//H/sub /spl infin// design for a decentralized discrete variable structure control with application to mobile robots

In this paper, a decentralized discrete variable structure control via mixed H/sub 2//H/sub /spl infin// design was developed. In the beginning, the H/sub 2/-norm of output error and weighted control input was minimized to obtain a control such that smaller energy consumption with bounded tracking error was assured. In addition, a suitable selection of this weighted function (connected with frequency) could reduce the effect of disturbance on the control input. However, an output disturbance caused by the interactions among subsystems, modeling error, and external load deteriorated system performance or even brought about instability. In this situation, the H/sub /spl infin//-norm of weighted sensitivity between output disturbance and output error was minimized to attenuate the effect of output disturbance. Moreover, an appropriate selection of this weighted function (related to frequency) could reject the corresponding output disturbance. No solution of Diophantine equation was required; the computational advantage was especially dominated for low-order system. For further improving system performance, a switching control for every subsystem was designed. The proposed control (mixed H/sub 2//H/sub /spl infin// DDVSC) was a three-step design method. The stability of the overall system was verified by Lyapunov stability criterion. The simulations and experiments of mobile robot were carried out to evaluate the usefulness of the proposed method.     

一类不确定非线性纯反馈系统的自适应鲁棒模糊控制

针对一类带有不确定性的非线性MIMO纯反馈系统,提出一种自适应鲁棒模糊控制方法,该方法放宽了已有文献对系统模型的限制条件,基于李雅普诺夫分析方法获得了控制输入和自适应律．在控制输入设计中,鲁棒控制项用于补偿逼近误差向量．通过选择适当的设计参数。提出的控制方法使得闭环系统的所有信号是一致有界的和跟踪误差向量的范数收敛到小的零邻域内．仿真结果表明了所提出方法的有效性．     

Disturbance observer-based tracking control with prescribed performance specifications for a class of nonlinear systems subject to mismatched disturbances

This work investigates simultaneous prescribed performance tracking control and mismatched disturbance rejection problems for a class of strict-feedback nonlinear systems. A novel control scheme combining prescribed performance control, disturbance observer technique, and backstepping method is proposed. The disturbance estimations are introduced into the design of virtual control law design in each step to compensate the mismatched disturbances. To further improve the control performance, a prescribed performance function characterizing the error convergence rate, maximum overshoot, and steady-state error is used to construct the composite controller. The proposed controller guarantees transient and steady-state performance specifications of tracking error and provides much better disturbance attenuation ability simultaneously. Rigorous stability analysis for the closed-loop system is established by direct Lyapunov function method. It is shown that all the states in the resulting closed-loop system are stable, and the tracking error evolves within the prescribed performance boundaries and asymptotically converges to zero even in the presence of mismatched external disturbances. Finally, theoretical results are illustrated and demonstrated by two simulation examples.     

Tracking Control of Multi-Agent Systems Using a Networked Predictive PID Tracking Scheme

With the rapid development of network technology and control technology, a networked multi-agent control system is a key direction of modern industrial control systems, such as industrial Internet systems. This paper studies the tracking control problem of networked multi-agent systems with communication constraints, where each agent has no information on the dynamics of other agents except their outputs. A networked predictive proportional integral derivative(PPID) tracking scheme is proposed t...