A decentralised iterative learning control framework for collaborative tracking

Collaborative tracking control involves two or more subsystems working together to perform a global objective, and is increasingly used within a diverse range of applications. Decentralised iterative learning control schemes have demonstrated highly accurate collaborative tracking by using past experience gained over repeated attempts at the task. However they impose highly restrictive constraints on the system dynamics, and their reliance on inverse dynamics has degraded their robustness to model uncertainty.This paper proposes the first general decentralised iterative learning framework to address this problem, thereby enabling a wide range of existing iterative learning control methodologies to be applied in a decentralised manner to collaborative subsystems. This framework is illustrated through the derivation of a variety of new decentralised iterative learning control algorithms which balance collaborative tracking performance with optimisation of a general objective function. The framework is illustrated by application to wearable stroke rehabilitation technology in which each subsystem is a muscle artificially activated by electrical stimulation. These verify the framework’s simplified design and reduced hardware and communication overheads.     

Application of robust iterative learning algorithm in motion control system

Robustness issue is considered to be one of the major concerns in application of the iterative learning control in motion control systems. The robustness in servo systems is related to parameter uncertainties and noise accumulation. In this paper, both parameter uncertainties and noise are considered in derivation of the error dynamic equation of the ILC algorithm. Based on the error dynamics, the H_∞ framework is utilized to design the robust learning controller. An optimization design process in selecting the proper learning gain and determining the learning function is proposed to ensure that both tracking performance and convergence condition are achieved. Simulations and experiments are conducted to validate the robust learning algorithm which can be applied efficiently to machine tools with the payload varying from 0 to 20 kg. The experimental results demonstrate that the proposed method improves the tracking and contouring performances significantly when performing a complex NURBS curve on a three-axis milling machine.     

Resistance based iterative learning control of additive manufacturing with wire

This paper presents successful feed forward control of additive manufacturing of fully dense metallic components. The study is a refinement of former control solutions of the process, providing more robust and industrially acceptable measurement techniques. The system uses a solid state laser that melts metal wire, which in turn is deposited and solidified to build the desired solid feature on a substrate. The process is inherently subjected to disturbances that might hinder consecutive layers to be deposited appropriately. The control action is a modified wire feed rate depending on the surface of the deposited former layer, in this case measured as a resistance. The resistance of the wire stick-out and the weld pool has shown to give an accurate measure of the process stability, and a solution is proposed on how to measure it. By controlling the wire feed rate based on the resistance measure, the next layer surface can be made more even. A second order iterative learning control algorithm is used for determining the wire feed rate, and the solution is implemented and validated in an industrial setting for building a single bead wall in titanium alloy. A comparison is made between a controlled and an uncontrolled situation when a relevant disturbance is introduced throughout all layers. The controller proves to successfully mitigate these disturbances and maintain stable deposition while the uncontrolled deposition fails.     

Newton-method based iterative learning control for robot-assisted rehabilitation using FES

Precise control of useful movement is critical in providing effective upper limb stroke rehabilitation using functional electrical stimulation (FES). To address the lack of accuracy currently available in clinical practice, this paper develops a general framework based on iterative learning control (ILC), an approach that has been successfully employed in three clinical treatment trials. An upper limb model is first developed to encompass unconstrained movements of the upper arm. In line with clinical need, additional assistance is then incorporated via a general class of robotic support mechanism. An iterative learning scheme is then developed to enable a subset of joint angles to be controlled via stimulation of an arbitrary set of muscles. This scheme is the first ILC approach which explicitly addresses coupled multivariable nonlinear dynamics in upper-limb rehabilitation, enforcing convergence over multiple executions of a reaching task. Experiments with six participants confirm practical utility and performance.     

红外相机扫描镜轨迹跟踪的迭代学习控制方法

为提高星载广域红外相机的观测效率与凝视成像质量,扫描镜需要在几十毫秒的时间内完成角度切换,实现角秒级的轨迹跟踪控制。在闭环带宽有限的情况下,性能指标难以通过基于经典控制理论的算法实现。针对枢轴支撑的扫描镜机构,提出了一种基于迭代学习的高阶系统轨迹跟踪控制方法,推导了迭代学习律,并通过预测型算法对学习律进行了优化,避免了误差高阶导数的计算。然后通过频域分析说明了算法收敛性,选取了关键参数。通过仿真与原理样机实测验证了其应用效果。测试结果表明,算法在闭环带宽低于2 Hz的情况下,无需辨识被控对象的高阶特性,即可实现扫描镜对角加加速度超过106 （°）/s3轨迹的高精度跟踪控制,跟踪误差优于±1.5",满足相机应用要求。     

基于多传感器信息的自主式机器人模糊控制

通过对自主式机器人(AMR)运动学特性的研究，结合人工驾驶的知识经验和自主式机器人的角速度一线速度函数模型，设计了基于多传感器信息的机器人模糊控制系统。利用单输出模糊控制器实现对AMR的运动控制，便于单片机系统的实现，有助于提高控制系统的实时性。实验结果表明了所提出方法的有效性。     

Speed-sensorless control system of a bearingless induction motor based on iterative central difference Kalman filter

ABSTRACT

In order to obtain speed self-detecting with low cost for a bearingless induction motor (BIM) a speed-sensorless control strategy based on the iterative central difference Kalman filter (ICDKF) is proposed. Firstly, on the basis of the BIM mathematical model, the nonlinear state equation is established and its order is reduced from fifth-order to fourth-order using the stator terminal voltage and current as input. Then, a sterling interpolation formulation is used in the filter to reduce the model error, and an iteration loop link is adopted to improve the filter accuracy. Finally, the online speed of the BIM is identified through the filter rotor speed estimation. Theoretical analysis, simulation and experimental results by UKF and CDKF method have been compared. The results show that the proposed speed-sensorless control system not only has good speed tracking performance and reduce the load disturbance but also improves the BIM suspension performance.     

双闭环控制感应加热电源的设计与仿真分析

针对目前20kHz/10kW感应加热电源开关功耗大的缺点,提出了一种基于DSP的容性移相PWM和PFM控制的设计方案。确定以全桥IGBT逆变为主拓扑电路,采用PID和DPLL相结合的双闭环控制策略,对系统进行SIMULINK平台仿真建模。在开环和闭环条件下对仿真结果进行对比分析,实现了负载频率自动跟踪和功率闭环控制。仿真系统到达恒定功率输出的时间为0.2ms,功率因数近似为1,且无周期跳变的现象。改善系统动态特性和稳定控制的同时,验证了该方案的可行性和有效性。