Trajectory control of an electro hydraulic actuator using an iterative backstepping control scheme

This paper introduces a new control algorithm for the trajectory control of an electrohydraulic actuator (EHA). The key feature of this paper is the combination of a modified backstepping control with an iterative learning mechanism to perform adaptive tracking control tasks for a symmetrical pump-controlled EHA. Firstly, a mathematical model of the EHA is developed and a strictly feedback form state space is obtained. Next, the control signal is formed based on an iterative learning scheme with a backstepping modifier. Then, stability analyses are also carried out to ensure the convergence of the closed loop system. Finally, four experimental cases of studies are done to evaluate the proposed control method.     

移动机器人轨迹跟踪的模糊PID-P型迭代学习控制

本文针对移动机器人轨迹跟踪控制问题的研究,提出了一种基于移动机器人运动模型的模糊开闭环PID-P型非线性离散迭代学习控制方法,给出了PID-P型迭代学习的收敛条件及其证明过程,并采用模糊控制的原理整定PID三个学习增益矩阵的参数.该控制方法提高了移动机器人对特定轨迹的重复跟踪能力,具有算法实现简单的特点.实验仿真结果表明,采用模糊开闭环PID-P型迭代学习控制算法对轨迹跟踪是可行有效的.     

Adaptive Iterative Learning Control for a Class of Uncertain Nonlinear Systems with Second-Order Sliding Mode Technique

This paper addresses the problem of robust adaptive iterative learning control for a chain of uncertain integral nonlinear systems, whose aim is to stabilize the tracking error of the system and improve convergence speed in the presence of uncertainties. In response to unknown bounded disturbances, a continuous second-order sliding mode adaptive iterative learning control scheme is proposed, in which an integral term is to attenuate the effects of the disturbances and achieve fast convergence performance. By designing a suitable controller and composite energy function, it is proved that the tracking error along iterative learning horizon will converge to a small neighborhood of zero. Numerical examples are provided to validate the efficacy of the proposed method.     

A learning scheme for low-speed precision tracking control of hybrid stepping motors

Servo control of the hybrid stepping motor is complicated due to its highly nonlinear torque-current-position characteristics, especially under low operating speeds. This paper presents a simple and efficient control algorithm for the high-precision tracking control of hybrid stepping motors. The principles of learning control have been exploited to minimize the motor's torque ripple, which is periodic and nonlinear in the system states, with specific emphasis on low-speed situations. The proposed algorithm utilizes a fixed proportional-derivative (PD) feedback controller to stabilize the transient dynamics of the servomotor and the feedforward learning controller to compensate for the effect of the torque ripple and other disturbances for improved tracking accuracy. The stability and convergence performance of the learning control scheme is presented. It has been found that all error signals in the learning control system are bounded and the motion trajectory converges to the desired value asymptotically. The experimental results demonstrated the effectiveness and performance of the proposed algorithm.     

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。     

Nonlinear computed torque control for a high-speed planar parallel manipulator

A new computed torque (CT)-type controller termed nonlinear CT (NCT) controller is developed and applied to a high-speed planar parallel manipulator. The NCT controller is designed by replacing the linear PD in the conventional CT controller with the nonlinear PD (NPD) algorithm. The stability of the parallel manipulator system with the NCT controller is proven using the Lyapunov theorem, and the proposed controller is further proven to guarantee asymptotic convergence to zero of both tracking error and error rate. The superiority of the proposed NCT controller is verified through the trajectory tracking experiments of an actual high-speed planar parallel manipulator, and the experiment results are compared with the CT controller.     

Power Line Communication with Network Transmission Data Loss Based on Learning Control

This paper proposed power line communication with transmission data. An iterative learning control method for the power line communication is studied by P-type learning control law. The data packet loss described as a stochastic Bernoulli process. The sufficient conditions are given for the convergence of the proposed algorithm by using the compression mapping method and norm theory. The convergence analysis guarantee the convergence of the tracking error in the sense of the \(\uplambda\)-norm. Finally, numerical simulations illustrate to verify the effectiveness of the proposed learning algorithm.     

Analysis of Linear Iterative Learning Control Schemes - A 2D Systems/Repetitive Processes Approach

This paper first develops results on the stability and convergence properties of a general class of iterative learning control schemes using, in the main, theory first developed for the branch of 2D linear systems known as linear repetitive processes. A general learning law that uses information from the current and a finite number of previous trials is considered and the results, in the form of fundamental limitations on the benefits of using this law, are interpreted in terms of basic systems theoretic concepts such as the relative degree and minimum phase characteristics of the example under consideration. Following this, previously reported powerful 2D predictive and adaptive control algorithms are reviewed. Finally, new iterative adaptive learning control laws which solve iterative learning control algorithms under weak assumptions are developed.     

New hybrid fuzzy controller for direct torque control induction motor drives

A new hybrid fuzzy controller for direct torque control (DTC) induction motor drives is presented in this paper. The newly developed hybrid fuzzy control law consists of proportional-integral (PI) control at steady state, PI-type fuzzy logic control at transient state, and a simple switching mechanism between steady and transient states, to achieve satisfied performance under steady and transient conditions. The features of the presented new hybrid fuzzy controller are highlighted by comparing the performance of various control approaches, including PI control, PI-type fuzzy logic control (FLC), proportional-derivative (PD) type FLC, and combination of PD-type FLC and I control, for DTC-based induction motor drives. The pros and cons of these controllers are demonstrated by intensive experimental results. It is shown that the presented induction motor drive is with fast tracking capability, less steady state error, and robust to load disturbance while not resorting to complicated control method or adaptive tuning mechanism. Experimental results derived from a test system are presented confirming the above-mentioned claims.     

Hierarchical autonomous switching control of a multi-modes omnidirectional mobile robot

Adequate control flexibility and tracking precision of the omnidirectional mobile robot (OMR) are difficult to be guaranteed with a fixed control mode. To address this challenge, this paper presents a modified hierarchical autonomous switching control scheme for an OMR with multiple maneuver-modes, which is capable of switching the alternative modes to adapt to the operational conditions and performing a satisfactory trajectory tracking control. Utilizing a hierarchical switching signal, the design begins by formulating a hybrid discrete OMR system with multiple subsystems cascading to its alternative kinematic states. Under the integrated system constraints, a new hierarchical switched fractional-order receding horizon control is presented to offer more adjusting flexibilities, which constructs a novel fractional-order cost function and then derives a numerical solvable solution. Meanwhile, with a Lyapunov-principle-based supervisory variable, an autonomous switching law is proposed so that the preferred subsystem can be selected to enhance the moving maneuverability. Under certain average dwell time conditions, the asymptotic stability of the resultant system is guaranteed. Finally, comparative experiments implemented on the real-world scenarios are provided to demonstrate the superior tracking performance and enhanced robustness of the proposed hierarchical autonomous switching control method as compared to traditional control schemes.     

A learning approach of wafer temperature control in a rapid thermalprocessing system

This paper presents a learning approach for wafer temperature control in a rapid thermal processing system (RTP). RTP is very important for semiconductor processing system and requires an accurate trajectory following. Numerous studies have addressed this problem and most research on this problem requires exact knowledge of the system dynamics. The various approaches do not guarantee the desired performance in practical applications when there exist some modeling errors between the model and the actual system. In this paper, iterative learning control scheme is applied to RTP without exact information on the dynamics. The learning gain of the iterative learning law is estimated by neural networks instead of a mathematical model. In addition, the control information obtained by the iterative learning controller (ILC) is accumulated in the feedforward neuro controller (FNC) for generalization to various reference profiles. Through numerical simulations, it is demonstrated that the proposed method can achieve an accurate output tracking even without an exact RTP model. The output errors decrease rapidly through iterations when using the learning gain estimated and the FNC yields a reduced initial error, and so requires small iterations     

Intelligent feedforward control and payload estimation for a two-link robotic manipulator

Conventional model-based computed torque control fails to produce a good trajectory tracking performance in the presence of payload uncertainty and modeling error. The challenge is to provide accurate dynamics information to the controller. A new control architecture that incorporates a neural-network, fuzzy logic and a simple proportional-derivative (PD) controller is proposed to control an articulated robot carrying a variable payload. An off-line trained feedforward (multilayer) neural network takes payload mass estimates from a fuzzy-logic mass estimator as one of the inputs to represent the inverse dynamics of the articulated robot. The effectiveness of the proposed architecture is demonstrated by experiment on a two-link planar manipulator with changing payload mass. Experimental results show that this control architecture achieves excellent tracking performance in the presence of payload uncertainty.     

On the implementation and control of a pneumatic power active lower-limb orthosis

This paper develops a pneumatic power active lower-limb orthosis (PPALO) to be a controlled plant. Due to the use of pneumatic actuators, the PPALO inherently possesses non-smooth nonlinearities, such as asymmetric dynamics, friction, and dead zone. In order to eliminate the influence of these nonlinearities on the pneumatic actuators and the dynamic coupling terms included in the dynamics of the lower-limb orthosis, an inner-loop PI controller with a differential pressure feedback and an outer-loop filter-based iterative learning control (FILC) scheme which consists of an outer PD feedback controller as well as a feedforward filter are used. Finally, a trajectory tracking control experiment is conducted to validate that the proposed method can effectively control the system to track the desired trajectory and reduce the vibration caused by nonlinearities of the pneumatic actuators.     

模糊增益PD型迭代学习算法及其应用

普通PID型迭代学习控制算法由于其增益矩阵一旦确定,就不再改变,因而收敛速度较慢。为了提高收敛速度,结合模糊控制技术用于学习控制,提出一种模糊增益PD型迭代学习控制算法。该算法根据系统误差的大小,通过调整因子而改变增益矩阵大小。调整的方法是:在控制的初始阶段,增强控制输入中系统误差增益矩阵,同时保持误差微分增益矩阵不变,从而清除误差;而在控制趋于稳定时,增强控制输入中误差微分增益矩阵,同时保持系统误差部分增益矩阵不变,以减少超调量。针对一个单臂机械手模型,进行了仿真设计,仿真结果验证了方法的有效性。     

Discrete iterative learning controller

An effective discrete learning control method is proposed for improving the tracking performance of linear systems repeating the task from cycle to cycle. With this method, the current cycle data are intentionally introduced into the learning control law. Analysis of the convergence is conducted completely in the discrete-time domain. The fast convergence performance is further substantiated by simulation results.<>     

An adaptive controller for a direct-drive SCARA robot

A direct adaptive controller for trajectory tracking of high-speed robots such as a direct-drive SCARA robot is presented. In this robot, nonlinear effects due to centrifugal, Coriolis, and inertial forces are more important than friction and gravity forces, unlike most industrial robots. The control law of the adaptive scheme consists of a PD regulator plus feedforward compensation of full dynamics. The feedforward terms are adjusted by an adaptation law so that the steady-state position errors are zero. With this adaptive controller, the joint acceleration measurement is not required and no inversion of the estimated mass matrix is involved. The tracking performances of the controller applied to a two-degree-of-freedom SCARA is illustrated by a real-time implementation based on a single-chip digital signal processor (DSP)     

Antisway Tracking Control of Overhead Cranes With System Uncertainty and Actuator Nonlinearity Using an Adaptive Fuzzy Sliding-Mode Control

An adaptive fuzzy sliding-mode control (AFSMC) is presented for the robust antisway trajectory tracking of overhead cranes subject to both system uncertainty and actuator nonlinearity. First, a fuzzy sliding-mode control (FSMC) law is designed for the antisway trajectory tracking of the nominal plant. In association with a conventional trajectory tracking control law, this FSMC law guarantees asymptotic stability as well as improved transient response of the load sway dynamics while the trolley tracking error dynamics is rendered uniformly asymptotically stable. Second, a fuzzy uncertainty observer is designed to cope with system uncertainty as well as actuator nonlinearity present in an actual plant, and it is incorporated with the FSMC law for the development of the AFSMC law. In addition to stability analysis, the robust performance of the proposed AFSMC law is verified via numerical simulations and experiments.      

Generalized predictive control based on self-recurrent wavelet neural network for stable path tracking of mobile robots: adaptive learning rates approach

In this paper, a generalized predictive control (GPC) method based on self-recurrent wavelet neural network (SRWNN) is proposed for stable path tracking of mobile robots. Since the SRWNN has a self-recurrent mother wavelet layer, it can well attract the complex nonlinear system although the SRWNN has less mother wavelet nodes than the wavelet neural network. Thus, the SRWNN is used as a model identifier for approximating on-line the states of the mobile robot. In our control system, since the control inputs, as well as the parameters of the SRWNN identifier are trained by the gradient descent method with the adaptive learning rates (ALRs), the optimal learning rates which are suitable for the time-varying trajectory of the mobile robot can be found rapidly. The ALRs for training the parameters of the SRWNN identifier and those for learning the control inputs are derived from the discrete Lyapunov stability theorem, which are used to guarantee the convergence of the GPC system. Finally, simulation results are provided to demonstrate the effectiveness of the proposed control strategy.     

Trajectory Switching Control of Robotic Manipulators Based on RBF Neural Networks

In this paper, we discuss the trajectory switching neural control problem for the switching model of a serial n-joint robotic manipulator. The key feature of this paper is to provide the dual design of the control law for the developed adaptive switching neural controller and the associated robust compensation control law. RBF Neural Networks (NNs) are employed to approximate unknown functions of robotic manipulators and a robust controller is designed to compensate the approximation errors of the neural networks and external disturbance. Via switched multiple Lyapunov function method, the adaptive updated laws and the admissible switching signals have been developed to guarantee that the resulting closed-loop system is asymptotically Lyapunov stable such that the joint position follows any given bounded desired output signal. Finally, we give a simulation example of a two-joint robotic manipulator to demonstrate the proposed methods and make a comparative analysis.     

Experiments toward MRAC design for linkage system

In most machine design, a planar linkage is synthesized to achieve a specific trajectory of motion. However, the dynamics of the planar linkage is shown to be highly nonlinear due to the asymmetry of the geometrical structure and external loads are often present in the output link; thus, the tracking of the prescribed trajectory is difficult to achieve perfectly. A model reference adaptive control (MRAC) with a nonlinear feedback loop and a disturbance compensation loop is proposed to suppress the nonlinear dynamics of the linkage and external force, respectively, while other feedback loops are designed to achieve the desired specifications. Furthermore, switching control is applied to the derivation of the adaptation rule to obtain a satisfactory transient behavior; meanwhile, modifications are proposed to alleviate the chattering due to the switching adaptive mechanism. Experimental studies are performed in a four-bar linkage system to demonstrate the effectiveness of the proposed method.