基于不确定逼近的机械手间接自适应鲁棒预测控制

为了使机械手系统在含有模型不确定项时具有良好的跟踪性能和较强的抗干扰能力,提出了一种间接自适应鲁棒预测控制。首先,针对机械手模型设计出非线性鲁棒预测控制器;然后,基于三次样条函数逼近控制律中因模型不确定性产生的未知项,并在控制律中引入一个D-控制项抑制外部干扰。理论证明了所设计的控制器能够使跟踪误差收敛到原点。仿真验证了所提方法的有效性。     

基于不确定逼近的机械手自适应鲁棒预测控制

针对具有参数不确定性和未知外部干扰的机械手轨迹跟踪问题提出了一种多输入多输出自适应鲁棒预测控制方法. 首先根据机械手模型设计非线性鲁棒预测控制律, 并在控制律中引入监督控制项; 然后利用函数逼近的方法逼近控制律中因模型不确定性以及外部干扰引起的未知项. 理论证明了所设计的控制律能够使机械手无静差跟踪期望的关节角轨迹. 仿真验证了本文设计方法的有效性.     

Nonlinear robust adaptive Cartesian impedance control of UAVs equipped with a robot manipulator

In this paper, a new nonlinear robust adaptive impedance controller is addressed for Unmanned Aerial Vehicles (UAVs) equipped with a robot manipulator that physically interacts with environment. A UAV equipped with a robot manipulator is a novel system that can perform different tasks instead of human being in dangerous and/or inaccessible environments. The objective of the proposed robust adaptive controller is control of the UAV and its robotic manipulator’s end-effector impedance in Cartesian space in order to have a stable physical interaction with environment. The proposed controller is robust against parametric uncertainties in the nonlinear dynamics model of the UAV and the robot manipulator. Moreover, the controller has robustness against the bounded force sensor inaccuracies and bounded unstructured modeling (nonparametric) uncertainties and/or disturbances in the system. Tracking performance and stability of the system are proved via Lyapunov stability theorem. Using simulations on a quadrotor UAV equipped with a three-DOF robot manipulator, the effectiveness of the proposed robust adaptive impedance controller is investigated in the presence of the force sensor error, and parametric and non-parametric uncertainties.     

Design of an adaptive tracker for n-link rigid robotic manipulators based on super-twisting global nonlinear sliding mode control

This paper develops an adaptive super-twisting global nonlinear sliding mode control technique for n-link rigid robotic manipulators. A novel control law is designed to guarantee elimination of the reaching phase and existence of the sliding mode around the surface right from the initial time. Furthermore, the adaptive tuning law eliminates requirement of the knowledge about the upper bounds of external disturbances. By using the proposed method, a robust controller is designed so that the tracking error of rigid manipulator is convergent to the global nonlinear sliding surface in a finite time, and strong robustness with respect to large uncertainties and disturbances is guaranteed. Illustrative simulations on a two-link elbow robot manipulator and a three degree of freedom rigid manipulator are presented to show the robustness and effectiveness of the suggested design compared to other method. Moreover, a simulation as well as experimental study of a rotary inverted pendulum system demonstrates the applicability of the proposed method.     

高超声速飞行器非线性鲁棒控制律设计

高超声速飞行器具有模型非线性程度高、耦合程度强、参数不确定性大、抗干扰能力弱等特点,其自主控制具有较大的挑战.论文提出了一种基于鲁棒补偿技术和反馈线性化方法的非线性鲁棒控制方法.文中首先采用反馈线性化的方法对纵向模型进行输入输出线性化,实现速度和高度通道的解耦和非线性模型的线性化.针对得到的线性模型,设计包括标称控制器和鲁棒补偿器的线性控制器.基于极点配置原理,设计标称控制器使标称线性系统具有期望的输入输出特性,利用鲁棒补偿器来抑制参数不确定性和外界扰动对于闭环控制系统的影响.基于小增益定理,证明了闭环控制系统的鲁棒稳定性和鲁棒跟踪性能.相比于非线性回路成形控制方法,仿真结果表明了所设计非线性鲁棒控制算法的有效性和优越性.     

Fuzzy PID control of space manipulator for both ground alignment and space applications

Considering gravity change from ground alignment to space applications, a fuzzy proportional-integral-differential (PID) control strategy is proposed to make the space manipulator track the desired trajectories in different gravity environments. The fuzzy PID controller is developed by combining the fuzzy approach with the PID control method, and the parameters of the PID controller can be adjusted on line based on the ability of the fuzzy controller. Simulations using the dynamic model of the space manipulator have shown the effectiveness of the algorithm in the trajectory tracking problem. Compared with the results of conventional PID control, the control performance of the fuzzy PID is more effective for manipulator trajectory control.     

Globally stable robust tracking of nonlinear systems using variablestructure control and with an application to a robotic manipulator

The globally stable robust output tracking for a class of nonlinear systems is considered. Based only on the knowledge of the bounds on the uncertainties, a variable structure control (VSC) law is developed under the structure matching assumption. It is shown that the outputs of the closed-loop system asymptotically track given output trajectories despite the uncertainties while maintaining the boundedness of all signals inside the loop. All signals inside the loop are shown to be bounded for all time. To illustrate the efficiency of the controller, the approach is applied to the case of a two degree-of-freedom (DOF) robotic manipulator with variable payload. Numerical simulation results are also provided     

基于细菌觅食算法的嵌入式机械臂角度控制器设计

进行机械臂角度控制器设计过程中,为提高机器人机械臂灵活性,降低关节角度控制误差,设计一种细菌觅食算法的嵌入式机械臂角度控制器。首先,构建机械臂动力学模型以获取机械臂的柔性特征及其关节位置,根据获取的信息确定角度控制器的硬件逻辑结构和算法。然后,使用ARM微处理器嵌入式操作系统,设计包含移动控制终端和机械臂控制端的控制器硬件结构。最后,采用细菌觅食算法优化控制器参数,并实现代码完成机器人机械臂角度的精准跟踪控制。仿真分析结果表明:所提方法具有较高的位姿跟踪精度、角度控制误差小、稳定性强,能够保证机械臂关节角度无超调,具有极高的机器工程应用价值。     

A predictive controller based on dynamic matrix control for a non-minimum phase robot manipulator

In this paper a hybrid control strategy is presented based on Dynamic Matrix Control (DMC) and feedback linearization methods for designing a predictive controller of five bar linkage manipulator as a MIMO system (two inputs and two outputs). Analyzing the internal dynamic of robot shows the open loop system is unstable and non-minimum phase, so in order to apply the predictive controller, special modifications are needed. These modifications on non-minimum phase behavior are performed using feedback linearization procedure based on state space realization. The design objective is to track a desirable set point as well as time varying trajectories as a command references with globally asymptotical stabilization. The proposed controller is applied to nonlinear fully coupled model of the typical five bar linkage manipulator with non-minimum phase behavior. Simulation results show that the proposed controller has good efficiency. The step responses of system with and without feedback linearization process illustrated that the mentioned modification for stabilizing is performed properly. After applying the proposed predictive controller, the joint angle of robot tracks the reference input while another input acts as the disturbance and vice versa.     

A Robust Tracking Controller for Electrically Driven Robot Manipulators: Stability Analysis and Experiment

 In this paper, a robust controller for electrically driven robotic systems is developed. The controller is designed in a backstepping manner. The main features of the controller are: 1) Control strategy is developed at the voltage level and can deal with both mechanical and electrical uncertainties. 2) The proposed control law removes the restriction of previous robust methods on the upper bound of system uncertainties. 3) It also benefits from global asymptotic stability in the Lyapunov sense. It is worth to mention that the proposed controller can be utilized for constrained and nonconstrained robotic systems. The effectiveness of the proposed controller is verified by simulations for a two link robot manipulator and a four-bar linkage. In addition to simulation results, experimental results on a two link serial manipulator are included to demonstrate the performance of the proposed controller in tracking a given trajectory.     

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.     

An Adaptive Fuzzy Control Model for Multi-Joint Manipulators

Multi-joint manipulator systems are subject to nonlinear influences such as frictional characteristics, random disturbances and load variations. To account for uncertain disturbances in the operation of manipulators, we propose an adaptive manipulator control method based on a multi-joint fuzzy system, in which the upper bound information of the fuzzy system is constant and the state variables of the manipulator control system are measurable. The control algorithm of the system is a MIMO (multi-input-multi-output) fuzzy system that can approximate system error by using a robust adaptive control law to eliminate the shadow caused by approximation error. It can ensure the stability of complex manipulator control systems and reduce the number of fuzzy rules required. Comparison of experimental and simulation data shows that the controller designed using this algorithm has highly-precise trajectory-tracking control and can control robotic systems with complex characteristics of non-linearity, coupling and uncertainty. Therefore, the proposed algorithm has good practical application prospects and promotes the development of complex control systems.     

Nonsingular Terminal Sliding Mode Control of Robot Manipulators Using Fuzzy Wavelet Networks

This paper presents an adaptive nonsingular terminal sliding mode (NTSM) tracking control design for robotic systems using fuzzy wavelet networks. Compared with linear hyperplane-based sliding control, terminal sliding mode controller can provide faster convergence and higher precision control. Therefore, a terminal sliding controller combined with the fuzzy wavelet network, which can accurately approximate unknown dynamics of robotic systems by using an adaptive learning algorithm, is an attractive control approach for robots. In addition, the proposed learning algorithm can on-line tune parameters of dilation and translation of fuzzy wavelet basis functions and hidden-to-output weights. Therefore, a robust control law is used to eliminate uncertainties including the inevitable approximation errors resulted from the finite number of fuzzy wavelet basis functions. The proposed controller requires no prior knowledge about the dynamics of the robot and no off-line learning phase. Moreover, both tracking performance and stability of the closed-loop robotic system can be guaranteed by Lyapunov theory. Finally, the effectiveness of the fuzzy wavelet network-based control approach is illustrated through comparative simulations on a six-link robot manipulator     

输入饱和非线性系统的周期自适应补偿学习控制

针对一类输入饱和不确定Brunovsky标准型非线性时滞系统,提出一种周期自适应跟踪补偿学习算法. 利用信号置换思想重组系统,基于最小公倍周期函数变换,将时滞时变项和不确定项合并为辅助参数,进而设计周期自适应学习律估计该辅助量,并利用饱和补偿器逼近和补偿超出饱和限的部分,由此构成综合控制器,以保证系统状态对有界期望值的跟踪,解决了饱和输入周期系统的重复迭代学习控制问题. 最后通过构造Lyapunov-Krasovskii复合能量函数的差分,计算证明了系统跟踪误差的收敛性和闭环信号值的有界性. 常见耦合非线性机械臂系统的力矩控制仿真,进一步验证了该算法的有效性.     

Adaptive tracking with external force disturbance rejection for uncertain robotic systems

This paper is concerned with the tracking control problem of robotic systems perturbed by time-varying parameters, unmodelled dynamics and external force (and moment) disturbances. The upper bound of system uncertainties and disturbances is not required for controller design. Also, no limitations are assumed on the speed of variation and the magnitude of unknown parameters and perturbations. An adaptive algorithm with simplicity and universality properties is proposed to ensure robust tracking. Presenting the closed loop stability proof analytically, the tracking controller is applied to a two-link robot manipulator and the simulation results are demonstrated to show the effectiveness of the method.     

Robust fuzzy tracking control for robotic manipulators

In this paper, a stable adaptive fuzzy-based tracking control is developed for robot systems with parameter uncertainties and external disturbance. First, a fuzzy logic system is introduced to approximate the unknown robotic dynamics by using adaptive algorithm. Next, the effect of system uncertainties and external disturbance is removed by employing an integral sliding mode control algorithm. Consequently, a hybrid fuzzy adaptive robust controller is developed such that the resulting closed-loop robot system is stable and the trajectory tracking performance is guaranteed. The proposed controller is appropriate for the robust tracking of robotic systems with system uncertainties. The validity of the control scheme is shown by computer simulation of a two-link robotic manipulator.     

Fuzzy model based adaptive control for a class of nonlinear systems

A fuzzy model based adaptive control algorithm for a class of continuous-time nonlinear dynamic systems is presented. The fuzzy model consisting of a set of linear fuzzy local models that are combined using a fuzzy inference mechanism is used to model a class of nonlinear systems. Each fuzzy local model represents a linearized model corresponding to the operating point of the controlled nonlinear system. The proposed control algorithm employs the fuzzy controller that is designed by considering the linear state feedback controller corresponding to the fuzzy local model with the maximum weight and the switching-σ modification adaptive controller to adaptively compensate for the plant nonlinearities. Stability robustness of the closed-loop system is analyzed in Lyapunov sense. It is shown, that the proposed control algorithm guarantees global stability of the system with the output of the system approaching the origin if there are no disturbances and uncertainties, converging to the neighborhood of the origin for all realizations of uncertainties and disturbances. The simulation examples for controlling inverted pendulum system are given to illustrate the effectiveness of the proposed method     

具有H_∞跟踪性能的机器人自适应PID控制

针对非线性不确定机器人系统的轨迹跟踪控制问题,提出一种鲁棒自适应PID控制算法.该控制器由主控制器和监督控制器组成.主控制器以常规PID控制为基础,基于滑模控制思想设计PID参数的自适应律,根据误差实时修正PID参数.基于Lyapunov函数设计的监督控制器补偿自适应PID控制器与理想控制器之间的差异,使系统具有设定的H_∞的跟踪性能.最后,两关节机器人的仿真实验结果表明了算法的有效性.

Abstract：

A robust adaptive PID control algorithm is proposed for trajectory tracking of robot manipulators with nonlinear uncertainties.The controller is composed of a main controller and a supervisory controller.The main controller is designed based on the traditional PID controller.The parameters of the PID controller are updated online according to the system running errors with the adaptation law based on the sliding mode control.The supervisory controller is proposed to compensate the error between the adaptive PID controller and the ideal controller in the sense of the Lyapunov function with the specified H_∞ tracking performance.Finally, the simulation results based on a two-joint robot manipulator show the effectiveness of the presented controller.     

Uncertainty compensation for a flexible-link manipulator using nonlinear H control

In a flexible-link manipulator, in general the effect of some parameters such as payload, friction amplitude and damping coefficients cannot be exactly measured. One possibility is to consider the above as parameters with uncertainty. In this paper, constant as well as L₂-bounded deviations of parameters from their nominal values are considered as uncertainties. These uncertainties make it difficult for a linear controller to achieve desired closed-loop performance. To remedy this problem, a nonlinear dynamical model of a flexible-link manipulator which has a constant input vector field (g in [xdot]=f(x) + g(x)u) is obtained. Based on recent results in nonlinear robust regulation with an H∞ constraint a nonlinear controller is designed for the flexible-link manipulator. The contribution of this paper is in demonstrating that the nonlinear controller has a larger domain of attraction than the linearized controller. In fact, for the single-link flexible manipulator considered in this paper, the linear H∞ controller results in instability for step changes in the desired output of greater than 3.6 rad, whereas the nonlinear H∞ controller yields desired step changes of 2π rad. Simulation results demonstrating the advantages and superiority of the nonlinear H∞ controller over the linear H∞controller are presented.     

Direct adaptive neural control of nonlinear systems with extreme learning machine

A direct adaptive neural control scheme for a class of nonlinear systems is presented in the paper. The proposed control scheme incorporates a neural controller and a sliding mode controller. The neural controller is constructed based on the approximation capability of the single-hidden layer feedforward network (SLFN). The sliding mode controller is built to compensate for the modeling error of SLFN and system uncertainties. In the designed neural controller, its hidden node parameters are modified using the recently proposed neural algorithm named extreme learning machine (ELM), where they are assigned random values. However, different from the original ELM algorithm, the output weight is updated based on the Lyapunov synthesis approach to guarantee the stability of the overall control system. The proposed adaptive neural controller is finally applied to control the inverted pendulum system with two different reference trajectories. The simulation results demonstrate good tracking performance of the proposed control scheme.