Adaptive learning tracking control of robotic manipulators with uncertainties

An adaptive learning tracking control scheme is developed for robotic manipulators by a synthesis of adaptive control and learning control approaches. The proposed controller possesses both adaptive and learning properties and thereby is able to handle robotic systems with both time-varying periodic uncertainties and time invariant parameters. Theoretical proofs are established to show that proposed controllers ensure asymptotical tracking performance. The effectiveness of the proposed approaches is validated through extensive numerical simulation results.     

基于内模的机器人迭代学习控制

本文根据内模控制的概念，设计一个扰动控制器，使机器人系统表现为固定参数的解耦线性化系统．基于此线性系统，提出了一种迭代学习控制律，给出了算法收敛的充分条件．算法的参数选择非常简单，从而易于满足收敛条件．仿真结果表明了算法的有效性．     

Design and performance of an intelligent predictive controller for a six-degree-of-freedom robot using the Elman network

The aim of this paper was to propose a recurrent neural network-based predictive controller for robotic manipulators. A neural network controller for a six-joint Stanford robotic manipulator was designed using the generalized predictive control (GPC) and the Elman network. The GPC algorithm, which is a class of digital control method, requires long computational time. This is a disadvantage in real-time robot control; therefore, the Elman network controller was designed to reduce processing time by avoiding the highly mathematical and computational complexity of the GPC. The main reason for choosing the Elman network, amongst several neural network algorithms, was that the presence of feedback loops have a profound impact on the learning capability of the network. The designed neural network controller was able to recover quickly because of its significant generalization capability, which allowed it to adapt very rapidly to changes in inputs. The performance of the controller was also shown graphically using simulation software, including the dynamics and kinematics of the robot model.     

An adaptive neural network switching control approach of robotic manipulators for trajectory tracking

In this paper, an adaptive neural network (NN) switching control strategy is proposed for the trajectory tracking problem of robotic manipulators. The proposed system comprises an adaptive switching neural controller and the associated robust compensation control law. Based on the Lyapunov stability theorem and average dwell-time approach, it is shown that the proposed control scheme can guarantee tracking performance of the robotic manipulators system, in the sense that all variables of the closed-loop system are bounded and the effect due to the external disturbance and approximate error of radical basis function (RBF) NNs on the tracking error can be converged to zero in an infinite time. Finally, simulation results on a two-link robotic manipulator show the feasibility and validity of the proposed control scheme.     

Force control of a robotic manipulator by application of a neural network

In this paper, a force control method for robotic manipulators which utilize a neural network model is proposed with consideration of the dynamics of objects. The proposed system consists of a standard PID controller and a multilayered neural network model, which optimizes a set of controller's parameters via a process of learning. The neural network model has not yet been applied to force control problems, but the proposed method is shown to be applicable to force/compliance control problems. The stability of this system and a wider applicability are verified by simulation studies.     

Neural network-based robust finite-time control for robotic manipulators considering actuator dynamics

A novel neural network-based robust finite-time control strategy is proposed for the trajectory tracking of robotic manipulators with structured and unstructured uncertainties, in which the actuator dynamics is fully considered. The controller, which possesses finite-time convergence and strong robustness, consists of two parts, namely a neural network for approximating the nonlinear uncertainty function and a modified variable structure term for eliminating the approximate error and guaranteeing the finite-time convergence. According to the analysis based on the Lyapunov theory and the relative finite-time stability theory, the neural network is asymptotically convergent and the controlled robotic system is finite time stable. The proposed controller is then verified on a two-link robotic manipulator by simulations and experiments, with satisfactory control performance being obtained even in the presence of various uncertainties and external disturbances.     

非线性系统的神经网络学习控制

主要控制了一类非线性系统的神经网络学习控制问题。讨论了以迭代学习方式训练的神经网络学习控制器，在满足一定条件，可以实现一定时间内的系统输出跟踪。     

Further results on adaptive iterative learning control of robot manipulators

Based on a combination of a PD controller and a switching type two-parameter compensation force, an iterative learning controller with a projection-free adaptive algorithm is presented in this paper for repetitive control of uncertain robot manipulators. The adaptive iterative learning controller is designed without any a priori knowledge of robot parameters under certain properties on the dynamics of robot manipulators with revolute joints only. This new adaptive algorithm uses a combined time-domain and iteration-domain adaptation law allowing to guarantee the boundedness of the tracking error and the control input, in the sense of the infinity norm, as well as the convergence of the tracking error to zero, without any a priori knowledge of robot parameters. Simulation results are provided to illustrate the effectiveness of the learning controller.     

机器人视觉伺服中CMAC学习控制系统研究

根据小脑模型关联控制器（CMAC）收敛速度快,适于实时控制系统的特点,设计了一种基于CMAC学习控制方法的机器人视觉伺服系统。在该系统中,CMAC被用作前馈视觉控制器对常规反馈控制器进行补偿。所提出的CMAC控制器替代图像雅可比矩阵来获得目标图像特征和机器人关节运动之间2D/3D变换关系,通过其在线学习,可以使系统对摄像机标定误差不敏感,从而提高系统的鲁棒性。实验证明了所设计控制系统的有效性。     

Experience-Based Iterative Learning Controllers for Robotic Systems

An experience based iterative learning controller is proposed for a general class of robotic systems. Experience of the iterative learning controller is stored in the memory in terms of input output data and later used for the prediction of the initial control input for a new desired trajectory. It is proved in this paper that using this approach we can reduce the number of iterations to achieve a certain user defined tracking accuracy. This approach is very general and applicable to all kinds of existing iterative learning control schemes. Numerical illustrations showed the effectiveness of the proposed method.     

Reconfigurable Control Structure for Robots in Assembly

The increased use of changeable characteristics in modern manufacturing and robotic systems and applications call for improved system control design that offers some degree of reconfigurability. The need for control reconfiguration of robotic systems arises due to some inherent characteristics of the robotic system, variations of robot parameters due to environmental changes, major task changes typical in production changeover or manufacturing system reconfiguration, or geometry changes due to the reconfiguration of modular manipulators. In this paper, a reconfigurable controller, the Supervisory Control Switching System (SCSS), is proposed to meet the new on-line demands for changeability in robotic systems. The SCSS is capable of selecting the most suitable controller for a particular task or situation, from separate controllers designed a priori. The applicability and effectiveness of the developed switching control scheme have been illustrated through computer simulations of an AdeptOne SCARA manipulators carrying out assembly tasks.     

Compound control of a compound cosine function neural network and PD for manipulators

In this paper, a compound cosine function neural network controller for manipulators is presented based on the combination of a cosine function and a unipolar sigmoid function. The compound control scheme based on a proportional-differential (PD) feedback control plus the cosine function neural network feedforward control is used for the tracking control of manipulators. The advantages of the compound control are that the system model does not need to be identified beforehand in the manipulator control system and it can achieve better adaptive control in an on-line continuous learning manner. The simulation results for the two-link manipulator show that the proposed compound control has higher tracking accuracy and better robustness than the conventional PD controllers in the position trajectory tracking control for the manipulator. Therefore, the compound cosine function neural network controller provides a novel approach for the manipulator control with uncertain nonlinear problems.     

机器人的一种离散学习控制算法的研究

本文基于关节空间提出了机器人的一种离散学习控制算法．此算法仅仅利用了部分机器人动力学模型知识，基于关节空间的加速度信号构成迭代学习律，并给出了算法的收敛性证明．仿真表明了算法的有效性．     

Velocity observer-based iterative learning control for robot manipulators

This article addresses the problem of designing an iterative learning control for trajectory tracking of rigid robot manipulators subject to external disturbances, and performing repetitive tasks, without using the velocity measurement. For solving this problem, a velocity observer having an iterative form is proposed to reconstruct the velocity signal in the control laws. Under assumptions that the disturbances are repetitive and the velocities are bounded, it has been shown that the whole control system (robot plus controller plus observer) is asymptotically stable and the observation error is globally asymptotically stable, over the whole finite time-interval when the iteration number tends to infinity. This proof is based upon the use of a Lyapunov-like positive definite sequence, which is shown to be monotonically decreasing under the proposed observer–controller schemes.     

Decentralized adaptive controller design of large-scale uncertain robotic systems

In this paper, we develop a decentralized neural network control design for robotic systems. Using this design, it is not necessary to derive the robotic dynamical system (robotic model) for the control of each of the robotic components, as in traditional robot control. The advantage of the proposed neural network controller is that, under a mild assumption, unknown nonlinear dynamics such as inertia matrix and Coriolis/centripetal matrix and friction, as well as interconnections with arbitrary nonlinear bounds can be accommodated with on-line learning.     

Continuous finite-time control for robotic manipulators with terminal sliding mode

A continuous finite-time control scheme for rigid robotic manipulators is proposed using a new form of terminal sliding modes. The robustness of the controller is established using the Lyapunov stability theory. Theoretical analysis and simulation results show that faster and high-precision tracking performance is obtained compared with the conventional continuous sliding mode control method.     

基于可变学习率BP算法的空调控制系统的研究

通过分析控制器参数学习率和控制器性能之间的关系,设计一种基于可变学习速率反向传播算法VLRBP和模糊神经元网络的变频空调控制系统.该系统不仅可以通过反传误差信号训练控制器参数,而且可以根据网络的当前状态朝最优化方向调整控制器参数的学习率.实验结果表明,该控制系统不仅比传统的空调PID控制器和模糊控制器具有更好的控制性能,而且相比基于标准BP算法和动量BP算法的模糊神经网络控制系统,也具有更快的收敛速度和更好的控制精确度.     

Observer-Based Adaptive Controller Design of Flexible Manipulators Using Time-Delay Neuro-Fuzzy Networks

In this paper, a dynamical time-delay neuro-fuzzy controller is proposed for the adaptive control of a flexible manipulator. It is assumed that the robotic manipulator has only joint angle position measurements. A linear observer is used to estimate the robot joint angle velocity. For a perfect tracking control of the robot, the output redefinition approach is used in the adaptive controller design using time-delay neuro-fuzzy networks. The time-delay neuro-fuzzy networks with the rule representation of the TSK type fuzzy system have better learning ability for complex dynamics as compared with existing neural networks. The novel control structure and learning algorithm are given, and a simulation for the trajectory tracking of a flexible manipulator illustrates the control performance of the proposed control approach.     

An on-line learning neural controller for helicopters performing highly nonlinear maneuvers

This paper presents an on-line learning adaptive neural control scheme for helicopters performing highly nonlinear maneuvers. The online learning adaptive neural controller compensates the nonlinearities in the system and uncertainties in the modeling of the dynamics to provide the desired performance. The control strategy uses a neural controller aiding an existing conventional controller. The neural controller is based on a online learning dynamic radial basis function network, which uses a Lyapunov based on-line parameter update rule integrated with a neuron growth and pruning criteria. The online learning dynamic radial basis function network does not require a priori training and also it develops a compact network for implementation. The proposed adaptive law provides necessary global stability and better tracking performance. Simulation studies have been carried-out using a nonlinear (desktop) simulation model similar to that of a BO105 helicopter. The performances of the proposed adaptive controller clearly shows that it is very effective when the helicopter is performing highly nonlinear maneuvers. Finally, the robustness of the controller has been evaluated using the attitude quickness parameters (handling quality index) at different speed and flight conditions. The results indicate that the proposed online learning neural controller adapts faster and provides the necessary tracking performance for the helicopter executing highly nonlinear maneuvers.     

Motion/force control scheme for electrically driven cooperative multiple mobile manipulators

This paper presents the motion and force control problem of rigid-link electrically driven cooperative mobile manipulators handling a rigid object. Although, the motion/force control problem of cooperative mobile manipulators has been enthusiastically studied. But there is little research on the motion/force control of electrically driven cooperative mobile manipulators. Due to the inclusion of the actuator dynamics with the manipulator’s dynamics, the controller exhibits some important characteristics. For the electromechanical system, we have designed a novel controller at the dynamic level as well as at the actuator level. In the proposed control scheme, at the dynamic level, uncertain non-linear mechanical dynamics is approximated with a hybrid controller containing model-based control scheme combined with model-free neural network based control scheme together with an adaptive bound. The adaptive bound is used to suppress the effects of external disturbances, friction terms, and reconstruction error of the neural network. At the actuator level, for the approximation of the unknown electrical dynamics, the model-free neural network is utilized. The developed control scheme provides that the position tracking errors, as well as the internal force, converge to the desired levels. Additionally, direct current motors are also controlled in such a way that the desired currents and torques can be attained. In order to make the overall system to be asymptotically stable, online learning of the weights and the parameter adaptation of the parameters is utilized in the Lyapunov function. The superiority of the developed control method is carried out with the numerical simulation results and its superior robustness is shown in a comparative manner.