Adaptive robust torque control of electric load simulator with strong position coupling disturbance

Electric load simulator (ELS) is an important equipment to exert aerodynamic load to actuation system according to flight condition. The key issue of ELS is how to eliminate the influence of extra torque caused by actuation system, parametric uncertainties and uncertain nonlinearities. In order to overcome these difficulties, this paper proposes a powerful model-based adaptive robust torque control (ARTC) algorithm which transfers external disturbance elimination problem to a performance-oriented problem under uncertainties and nonlinearities. A discontinuous projection-based online parameter adaptation is employed to reduce the effect of various parameter uncertainties. Instead of discontinuous friction model, a continuous friction model based on smooth shape function is applied for friction compensation. The estimated velocity of actuator is utilized in ARTC controller for eliminating extra torque. The backstepping design via adaptive robust control Lyapunov function is employed to construct ARTC control law for ELS. Extensive comparative results indicate that the proposed ARTC controller is effective to achieve a guaranteed transient as well as final tracking accuracy in the presence of both parametric uncertainties and uncertain nonlinearities.     

Robust nonlinear control of a planar 2-DOF parallel manipulator with redundant actuation

A new robust nonlinear controller is presented and applied to a planar 2-DOF parallel manipulator with redundant actuation. The robust nonlinear controller is designed by combining the nonlinear PD (NPD) control with the robust dynamics compensation. The NPD control is used to eliminate the trajectory disturbances, unmodeled dynamics and nonlinear friction, and the robust control is used to restrain the model uncertainties of the parallel manipulator. The proposed controller is proven to guarantee the uniform ultimate boundedness of the closed-loop system by the Lyapunov theory. The trajectory tracking experiment with the robust nonlinear controller is implemented on an actual planar 2-DOF parallel manipulator with redundant actuation. The experimental results are compared with the augmented PD (APD) controller, and the proposed controller shows much better trajectory tracking accuracy.     

A modified direct adaptive robust motion trajectory tracking controller of a pneumatic system

In this study, we developed and tested a high-precision motion trajectory tracking controller of a pneumatic cylinder driven by four costless on/off solenoid valves rather than by a proportional directional control valve. The relationship between the pulse width modulation （PWM） of a signal＇s duty cycle and control law was determined experimentally, and a mathematical model of the whole system established. Owing to unknown disturbances and unmodeled dynamics, there are considerable uncertain nonlinearities and parametric uncertainties in this pneumatic system. A modified direct adaptive robust controller （DARC） was constructed to cope with these issues. The controller employs a gradient type adaptation law based on discontinuous projection mapping to guarantee that estimated unknown model parameters stay within a known bounded region, and uses a deterministic robust control strategy to weaken the effects of unmodeled dynamics, disturbances, and parameter estimation errors. By using discontinuous projection mapping, the parameter adaptation law and the robust control law can be synthesized separately. A recursive backstepping technology is applied to account for unmatched model uncertainties. Kalman filters were designed sepa- rately to estimate the motion states and the derivative of the intermediate control law in synthesizing the deterministic robust control law. Experimental results illustrate the effectiveness of the proposed controller.     

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.     

Observer‐based adaptive robust control of a class of nonlinear systems with dynamic uncertainties

In this paper, a discontinuous projection‐based adaptive robust control (ARC) scheme is constructed for a class of nonlinear systems in an extended semi‐strict feedback form by incorporating a nonlinear observer and a dynamic normalization signal. The form allows for parametric uncertainties, uncertain nonlinearities, and dynamic uncertainties. The unmeasured states associated with the dynamic uncertainties are assumed to enter the system equations in an affine fashion. A novel nonlinear observer is first constructed to estimate the unmeasured states for a less conservative design. Estimation errors of dynamic uncertainties, as well as other model uncertainties, are dealt with effectively via certain robust feedback control terms for a guaranteed robust performance. In contrast with existing conservative robust adaptive control schemes, the proposed ARC method makes full use of the available structural information on the unmeasured state dynamics and the prior knowledge on the bounds of parameter variations for high performance. The resulting ARC controller achieves a prescribed output tracking transient performance and final tracking accuracy in the sense that the upper bound on the absolute value of the output tracking error over entire time‐history is given and related to certain controller design parameters in a known form. Furthermore, in the absence of uncertain nonlinearities, asymptotic output tracking is also achieved. Copyright © 2001 John Wiley & Sons, Ltd.     

Robust adaptive control using a universal approximator for SISOnonlinear systems

This paper deals with the robust adaptive control of a class of nonlinear systems in the presence of parametric uncertainties and dominant uncertain nonlinearities. The proposed controller utilizes the robust adaptive control to guarantee uniform boundedness and convergence of tracking errors. In addition, an adaptive fuzzy logic system is used as a universal approximator to reduce the model uncertainties coming from uncertain nonlinearities and to improve tracking performance. The approach does not require the matching condition imposed on control systems by using the backstepping design procedure, and provides boundedness of tracking errors under poor parameter adaptation. The method can be applied to a class of single-input single-output (SISO) nonlinear systems, transformable to a parametric-strict-feedback form     

控制力矩陀螺框架伺服系统期望补偿自适应鲁棒控制

本文针对控制力矩陀螺框架伺服系统中存在的参数不确定性、摩擦非线性及外部干扰问题,提出了一种考虑LuGre摩擦的自适应鲁棒控制方法.针对陀螺框架伺服系统未知惯量和阻尼系数、LuGre摩擦参数不确定性及未知外部干扰上界,设计参数更新律对其进行估计.在此基础上,为提高系统对不确定参数及未知干扰的鲁棒性,设计带有期望补偿的自适应鲁棒控制器,可实现对LuGre摩擦非线性的精确补偿,同时减小测量信号噪声及外部干扰对系统的不利影响.应用Lyapunov稳定性理论分析了闭环系统的稳定性.对挠性航天器姿态机动控制的仿真结果,验证了所提方法的有效性.     

Adaptive Variable Structure Control Design for Uncertain Time-Delayed Systems with Nonlinear Input

This paper presents a new robust control for uncertain dynamic time-delayed systems with series nonlinearities. It is implemented by using variable structure control. The proposed variable structure controller ensures the global reaching condition of the sliding mode of the uncertain time-delayed system. However, in the sliding mode, the investigated uncertain time-delayed system still bears the insensitivity to the uncertainties and disturbances as the systems with linear input. Furthermore, the proposed controller is achieved through adaptive variable structure control without the limitation of knowing the bounds of the uncertainties and perturbations in advance.     

一类互联机器人系统鲁棒分散自适应控制

研究一类存在模型不确定性和外部扰动的互联机器人系统的控制问题.控制器由一般线性控制器,线性自适应控制器和非线性自适应控制器综合构成.通过Lyapunov理论证明设计的鲁棒分散自适应控制器能够有效地克服不确定性对系统的影响,实现闭环系统的渐近轨迹跟踪控制.最后给出一个仿真例子进一步验证控制器的有效性.     

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.     

Neural network robust control of a 3-DOF hydraulic manipulator with asymptotic tracking

Multiaxial hydraulic manipulators are complicated systems with highly nonlinear dynamics and various modeling uncertainties, which hinders the development of high-performance controller. In this paper, a neural network feedforward with a robust integral of the sign of the error (RISE) feedback is proposed for high precise tracking control of hydraulic manipulator systems. The established nonlinear model takes three-axis dynamic coupling, hydraulic actuator dynamics, and nonlinear friction effects into consideration. A radial basis function neural network (RBFNN) is synthesized to approximate the uncertain system dynamics and external disturbance, which can greatly reduce the dependence on accurate system model. In addition, a continuous RISE feedback law is judiciously integrated to deal with the residual unknown dynamics. Since the major unknown dynamics can be estimated by the RBFNN and then compensated in the feedforward design, the high-gain feedback issue in RISE feedback control will be avoided. The proposed RISE-based neural network robust controller theoretically guarantees an excellent semi-global asymptotic stability. Comparative simulation is performed on a 3-DOF hydraulic manipulator, and the obtained results verify the effectiveness of the proposed controller.     

Non-linear adaptive robust control of electro-hydraulic systems driven by double-rod actuators

This paper studies the high performance robust motion control of electro-hydraulic servo-systems driven by double-rod hydraulic actuators. The dynamics of hydraulic systems are highly non-linear and the system may be subjected to non-smooth and discontinuous non-linearities due to directional change of valve opening, friction and valve overlap. Aside from the non-linear nature of hydraulic dynamics, hydraulic servosystems also have large extent of model uncertainties. To address these challenging issues, the recently proposed adaptive robust control (ARC) is applied and a discontinuous projection based ARC controller is constructed. The resulting controller is able to take into account the effect of the parameter variations of the inertia load and the cylinder hydraulic parameters as well as the uncertain non-linearities such as the uncompensated friction forces and external disturbances. Non-differentiability of the inherent non-linearities associated with hydraulic dynamics is carefully examined and addressing strategies are provided. Compared with previously proposed ARC controller, the controller in the paper has a more robust parameter adaptation process and may be more suitable for implementation. Finally, the controller guarantees a prescribed transient performance and final tracking accuracy in the presence of both parametric uncertainties and uncertain non-linearities while achieving asymptotic tracking in the presence of parametric uncertainties.     

基于自适应模糊补偿的机械臂复合控制策略

针对刚性机械臂存在摩擦和扰动等不确定因素给轨迹跟踪控制带来的困难,本文基于李亚普诺夫稳定性理论,给出了一种机械臂的自适应控制方案．该方案针对机械臂的标称部分,采用计算力矩的方法设计相应的控制量,在此基础上,构造模糊系统逼近摩擦得到补偿控制量,并针对随机扰动的上界设计反馈控制率,以克服扰动带来的影响,保证系统的稳定性．仿真结果表明,该复合控制对于具有不确定性摩擦以及扰动的机械臂轨迹跟踪问题效果良好．     

基于RBF神经网络的机械臂自适应控制方法

针对机械臂受内部摩擦和时变扰动等不确定性因素的影响,其轨迹跟踪控制系统的跟踪精度会下降,且影响系统的稳定性,提出一种基于径向基函数神经网络的自适应控制方法。首先,利用RBF神经网络采用离线训练和在线学习的方式对机械臂的动力学模型进行辨识;其次针对机械臂控制系统中的摩擦,设计RBF神经网络自适应控制算法对其进行逼近得到补偿控制量。针对时变扰动和神经网络逼近误差设计鲁棒项,以克服众多不确定性因素带来的影响,同时通过构造李亚普诺夫函数对所设计的控制系统进行稳定性分析;最后,仿真实验结果证明提出的控制方法具有较高的跟踪精度、抗干扰能力和较强的鲁棒性。     

移动机械手鲁棒自适应模糊控制

基于旋量理论建立了非完整移动机械手系统的动力学模型,通过反步控制技术,应用非线性参数化模糊逻辑系统设计了移动机械手的鲁棒自适应模糊控制器.该控制器放松了移动机械手控制器设计中斜对称性的要求,对移动机械手系统中存在的参数或外界扰动等不确定性具有较强的鲁棒性和自适应能力.理论证明和仿真结果表明,所设计的控制器是有效的.     

Adaptive control of a 3-DOF parallel manipulator considering payload handling and relevant parameter models

Model-based control improves robot performance provided that the dynamics parameters are estimated accurately. However, some of the model parameters change with time, e.g. friction parameters and unknown payload. Particularly, off-line identification approaches omit the payload estimation (due to practical reasons). Adaptive control copes with some of these structural uncertainties. Thus, this work implements an adaptive control scheme for a 3-DOF parallel manipulator. The controller relies on a novel relevant-parameter dynamic model that permits to study the cases in where the uncertainties affect: (1) rigid body parameters, (2) friction parameters, (3) actuator dynamics, and (4) a combination of the former cases. The simulations and experiments verify the performance of the proposed controller. The control scheme is implemented on the modular programming environment Open Robot Control Software (OROCOS). Finally, an experimental setup evaluates the controller performance when the robot handles a payload.     

双容液位系统的不确定时滞的鲁棒容错控制

针对双容液位控制系统的执行器失效等故障,通过线性化建模,研究了鲁棒自适应容错控制问题。首先在系统无故障正常工作时,考虑建模误差、输入信号的稳定性、系统参数等不确定性因素,利用不确定性上界自适应估计,设计了不确定时滞鲁棒控制器。同时,对系统进行故障检测,研究了一种修正控制律的自适应鲁棒容错控制器设计方法,该控制器通过修补执行器故障所带来的影响使该系统最终有界稳定。最后,通过仿真实验,验证了提出方法的有效性。     

A Robust Adaptive Terminal Sliding Mode Control for Rigid Robotic Manipulators

In this paper, a robust adaptive terminal sliding mode controller is developed for n-link rigid robotic manipulators with uncertain dynamics. An MIMO terminal sliding mode is defined for the error dynamics of a closed loop robot control system, and an adaptive mechanism is introduced to estimate the unknown parameters of the upper bounds of system uncertainties in the Lyapunov sense. The estimates are then used as controller parameters so that the effects of uncertain dynamics can be eliminated and a finite time error convergence in the terminal sliding mode can be guaranteed. Also, a useful bounded property of the derivative of the inertial matrix is explored, the convergence rate of the terminal sliding variable vector is investigated, and an experiment using a five bar robotic manipulator is carried out in support of the proposed control scheme.     

Robust adaptive control for mobile manipulators

This paper addresses the trajectory tracking control of a nonholonomic wheeled mobile manipulator with parameter uncertainties and disturbances. The proposed algorithm adopts a robust adaptive control strategy where parametric uncertainties are compensated by adaptive update techniques and the disturbances are suppressed. A kinematic controller is first designed to make the robot follow a desired end-effector and platform trajectories in task space coordinates simultaneously. Then, an adaptive control scheme is proposed, which ensures that the trajectories are accurately tracked even in the presence of external disturbances and uncertainties. The system stability and the convergence of tracking errors to zero are rigorously proven using Lyapunov theory. Simulations results are given to illustrate the effectiveness of the proposed robust adaptive control law in comparison with a sliding mode controller.