An adaptive friction compensator for global tracking in robot manipulators

A novel adaptive friction compensator based on a dynamic model recently proposed in the literature is presented in this paper. The compensator ensures global position tracking when applied to an n degree of freedom robot manipulator perturbed by friction forces with only measurements of position and velocity, and all the system parameters (robot and friction model) unknown. Instrumental for the solution of the problem is the observation that friction compensation can be recasted as a disturbance rejection problem. The control signal is then designed in two steps, first a classical adaptive robot controller that (strictly) passifies the system, and then a relay-based outer-loop that rejects the disturbance.     

基于LuGre 摩擦模型的机械臂模糊神经网络控制

针对未知摩擦非线性会使机械臂控制精度难以提高的缺陷,建立基于动态LuGre摩擦的机械臂模型.在系统参数未知和机械臂负载变化的情况下,设计一种自适应模糊神经网络控制器,采用基函数中心和宽度均自适应变化的模糊神经网络补偿器,实现对系统中包括LuGre摩擦在内的非线性环节的逼近,并利用滑模控制项减小逼近误差.通过Lyapunov方法证明了闭环系统的稳定性,并通过仿真结果验证了所提出控制方法的有效性.     

Stability analysis of robust adaptive hybrid position/force controller for robot manipulators using neural network with uncertainties

The aim of this paper is to design a robust adaptive neural network-based hybrid position/force control scheme for robot manipulators in the presence of model uncertainties and external disturbance. The feedforward neural network employed to learn a highly nonlinear function requires no preliminary learning. The control purposes are to achieve the stability in the sense of Lyapunov for desired interaction force between the end-effector and the environment and to regulate robot tip position in cartesian space. An adaptive compensator is also developed to eliminate the effect of disturbance term of neural network approximation error and external disturbance or unmodeled dynamics etc. A key feature of this compensator is that the prior information of the disturbance bound is not required. Finally, a comparative simulation study with a model-based robust control scheme for a two-link robot manipulator is presented.     

Robust H∞ Tracking Control of Uncertain Robotic Systems with Periodic Disturbances

This paper addresses the problem of designing robust tracking control for a large class of uncertain robotic systems. A more general model of the external disturbance is employed in the sense that the external disturbance can be expressed as the sum of a modeled disturbance and an unmodeled disturbance, for example, any periodic disturbance can be expressed in this general form. An adaptive neural network system is constructed to approximate the behavior of unknown robot dynamics. An adaptive control algorithm is designed to estimate the behavior of the modeled disturbance, and in turn the robust H_∞ control algorithm is required to attenuate the effects of the unmodeled disturbance only. Consequently, an intelligent adaptive/robust tracking control scheme is constructed such that an H_∞ tracking control is achieved in the sense that all the states and signals of the closed‐loop system are bounded and the effect due to the unmodeled disturbance on the tracking error can be attenuated to any preassigned level. Finally, simulations are provided to demonstrate the effectiveness and performance of the proposed control algorithm.     

一种可保证瞬态特性的改进的鲁棒模型参考自适应控制

针对典型的鲁棒模型参考自适应控制中瞬态性能无法得到保障的问题, 提出一种改进的鲁棒模型参考自适应控制器. 该控制器在标准的鲁棒自适应控制中加入??_∞ 补偿器, 以抑制闭环自适应系统中参数估计误差和不确定扰动对系统输出跟踪性能造成的不利影响. 理论分析和仿真验证表明, 所提出的控制器不但保留了典型鲁棒模型参考自适应控制的理想特性, 并且通过设计适当的??∞ 补偿器使得闭环系统的瞬态性得到了较大的改善, 其改善的程度依赖于??∞ 补偿器性能指标的大小.

     

Adaptive friction compensation using extended Kalman–Bucy filter friction estimation

An extended Kalman–Bucy filter (EKBF)-based friction compensation method is presented and validated. The method relies on an accurate model of system rigid-body dynamics and measured motion, rather than a structured nonlinear friction model, to estimate external friction torque. The estimate is used in a traditional friction compensator to cancel friction effects. The EKBF compensator is compared with other model-based and non-model-based friction compensation strategies through position tracking experiments. Results show that when motion is dominated by static and Stribeck friction, non-model-based friction estimation and compensation using the EKBF consistently provides equal or superior performance over model-based adaptive friction compensation methods.     

Lumped disturbance compensation using extended Kalman filter for permanent magnet linear motor system

In this paper, an extended Kalman filter is designed and applied to a feed-forward based lumped disturbance compensator which consists of position dependent functions for a permanent magnet linear synchronous motor system. In our previous research, a lumped disturbance model including the force ripple and the Coulomb friction force was developed and utilized as a feed-forward controller. To improve the performance of that model, following two studies are conducted. First, an initial position estimator is designed to create synchronization between the model and real disturbance. This step is necessary because almost all linear motor systems are equipped with an incremental encoder for position measurement. Second, to cancel out a slight variation in real disturbance, an adaptive controller in the form of coefficients adaptation is designed. These two studies are combined by a sixth order extended Kalman filter. To make a comparison, a recursive least squares filter and disturbance observer and its modified version are prepared. The effectiveness of the proposed scheme is verified by the overall disturbance shape, RMS position error and FFT analysis on the position error.     

Discrete adaptive neural network disturbance feedforward compensation for non-linear disturbances in servo-control applications

This article introduces a novel adaptive neural network compensator for feedforward compensation of external disturbances affecting a closed-loop system. The neural network scheme is posed so that a non-linear disturbance model estimate for a measurable disturbance can be adapted for rejection of the disturbance affecting a closed-loop system. The non-linear neural network approach has been particularly developed for ‘mobile’ applications where the adaptation algorithm has to remain simple. For that reason, the theoretical framework justifies a very simple least-mean-square approach suggested in a mobile hard disk drive context. This approach is generalised to a non-linear adaptive neural network (NN) compensation scheme. In addition, usual assumptions are relaxed, so that it is sufficient to model the disturbance model as a stable non-linear system avoiding strictly positive real assumptions. The output of the estimated disturbance model is assumed to be matched to the compensation signal for effectiveness, although for stability this is not necessary. Practical and simulation examples show different features of the adaptation algorithm. In a realistic hard disk drive simulation and a practical application, it is shown that a non-linear adaptive compensation scheme is required for non-linear disturbance compensation providing better performance at similar computational effort in comparison to well-established schemes.     

Adaptive Friction Compensation: Modular Design with Passive Identifier

For a second‐order mechanical system incorporating Coulomb frictional effect, a nonlinear adaptive control that achieves a controller‐identifier separation is designed. This modularity is made possible by the strong input‐to‐state stability (ISS) property of the ISS controller with respect to the parameter estimation error as input. This input is independently guaranteed to be bounded by the passive identifier. We use two types of passive identifiers: z‐scheme passive identifier and x‐scheme passive identifier. These designs are more flexible than the Lyapunov‐based design and lead to lower control effort. In addition, the advantages and disadvantages of z‐scheme and x‐scheme are presented. Transient performance of the system is enhanced with a trajectory initialization technique. The validity and effectiveness of the proposed friction compensator is verified by simulation for position tracking control under the influence of Coulomb friction.     

Adaptive bearing friction compensation based on recent knowledge of dynamic friction

Gimbal bearing friction is a major source of stabilization errors for airborne pointing and tracking systems. This paper describes a novel addition to conventional stabilization techniques which has recently been incorporated in such a system to greatly improve stabilization performance. This addition contains a model in system software which predicts realtime friction torque values. This new, dynamic friction model, which is the result of recent investigations into dynamic friction characteristics, is adaptively adjusted into agreement with actual friction behavior by processing inputs from conventional system sensors. Measurements from these sensors cause on-line adjustment of model parameters, resulting in ‘adaptive’ compensator action. The model's output is used to generate an addition to conventional stabilization subsystem commands. The resulting additional gimbal motor torque is equal and opposed to the actual friction disturbance such that the residual torque, and hence stabilization errors, are a small fraction of those for an uncompensated system. The model-referenced compensator thus operates in a predictive, adaptive, feedforward manner to pre-condition the stabilization subsystem, reducing stabilization errors well below levels which are achievable through conventional feedback operation alone.     

A Neuro-interface with fuzzy compensator for controlling nonholonomic mobile robots

This paper describes a control method for mobile robots represented by a nonlinear dynamical system, which is subjected to an output deviation caused by drastically changed disturbances. We here propose some controllers in the framework of neuro-interface. It is assumed that a neural network (NN)-based feedforward controller is construcetd by following the concept of virtual master-slave robot, in which a virtual master robot as a feedforward controller is used to control the slave (i.e., actual) robot. The whole system of the present neuro-interface consists of an NN-based feedforward controller, a feedback PD controller and an adaptive fuzzy feedback compensator. The NN-based feedforward controller is trained offline by using a gradient method, the gains of the PD controller are to be chosen constant, and the adaptive fuzzy compensator is constructed with a simplified fuzzy reasoning. Some simulations are presented to confirm the validity of the present approach, where a nonholonomic mobile robot with two independent driving wheels is assmued to have a disturbance due to the change of mass for the robot.     

Visibility maintenance via controlled invariance for leader–follower vehicle formations

This paper studies the visibility maintenance problem (VMP) for a leader–follower pair of Dubins-like vehicles with input constraints and proposes an original solution based on the notion of controlled invariance. The nonlinear model describing the relative dynamics of the vehicles is interpreted as a linear uncertain system, with the leader robot acting as an external disturbance. The VMP is then reformulated as a linear constrained regulation problem with additive disturbances (DLCRP). Positive D-invariance conditions for linear uncertain systems with parametric disturbance matrix are introduced and used to solve the VMP when box bounds on the state, control input and disturbance are considered. The proposed design procedure can be easily adapted to more general scenarios. Simulation results illustrate the theory and show the effectiveness of our approach.     

High-gain adaptive position control

High-gain adaptive position control is proposed for a stiff one-mass system (1MS) and an elastic two-mass system (2MS). The control objective is (load-side) position reference tracking and disturbance rejection (of load torques and friction). Position and speed are available for feedback. Two simple high-gain adaptive position control strategies are presented and applied to a laboratory setup: an adaptive λ-tracking controller and a funnel controller. Both controllers neither estimate nor identify the plant. The λ-tracking controller achieves tracking with prescribed asymptotic accuracy: for given λ?>?0 (arbitrary small) the error approaches the interval [?λ,?λ] asymptotically. Whereas the funnel controller assures tracking with prescribed transient accuracy: the error and its derivative are bounded by prescribed positive (possibly non-increasing) functions of time. A simple proportional-integral (PI)-like extension for the 1MS, and this extension in combination with a high-pass filter for the 2MS, allow for zero tracking errors in steady-state, respectively. Oscillations in the shaft of the 2MS can be suppressed.     

Robust Adaptive Terminal Sliding Mode Control of an Omnidirectional Mobile Robot for Aircraft Skin Inspection

In this paper, an adaptive terminal sliding mode control scheme for an omnidirectional mobile robot is proposed as a robust solution to the trajectory tracking control problem. The omnidirectional mobile robot has a double-frame structure, which adsorbes on the aircraft surface by suction cups. The major difficulties lie in the existence of nonholonomic constraints, system uncertainty and external disturbance. To overcome these difficulties, the kinematic model is established, the dynamic model is derived by using Lagrange method. Then, a robust adaptive terminal sliding mode (RATSM) control scheme is proposed to solve the problem of state stabilization and trajectory tracking. In order to enhance the robustness of the system, an adaptive online estimation law is designed to overcome the total uncertainty. Subsequently, the asymptotic stability of the system without total uncertainty is proved with basis on Lyapunov theory, and the system considering total uncertainty can converge to the domain containing the origin. Simulation results are given to show the verification and validation of the proposed control scheme.

     

Adaptive robust simultaneous stabilization of multiple n-degree-of-freedom robot systems

In this paper, the adaptive robust simultaneous stabilization problem of uncertain multiple n-degree-of-freedom (n-DOF) robot systems is studied using the Hamiltonian function method, and the corresponding adaptive L2 controller is designed. First, we investigate the adaptive simultaneous stabilization problem of uncertain multiple n-DOF robot systems without external disturbance. Namely, the single uncertain n-DOF robot system is transformed into an equivalent Hamiltonian form using the unified partial derivative operator (UP-DO) and potential energy shaping method, and then a high dimensional Hamiltonian system for multiple uncertain robot systems is obtained by applying augmented dimension technology, and a single output feedback controller is designed to ensure the simultaneous stabilization for the higher dimensional Hamiltonian system. On this basis, we further study the adaptive robust simultaneous stabilization control problem for the uncertain multiple n-DOF robot systems with external disturbances, and design an adaptive robust simultaneous stabilization controller. Finally, the simulation results show that the adaptive robust simultaneous stabilization controller designed in this paper is very effective in stabilizing multi-robot systems at the same time.     

Friction compensation for a sandwich dynamic system

Friction compensation for a benchmark system with load friction plus joint flexibility and damping is addressed. This is a problem of controlling a sandwich dynamic system with a non-smooth nonlinearity. Several non-adaptive and adaptive compensation designs are analyzed, based on a state feedback output tracking model reference adaptive control scheme. Sufficient output matching conditions are derived for friction compensation. Approximate linear parametrizations of nonlinear friction are developed for adaptive friction compensator designs. Simulation results verify the desired system performance.     

Adaptive RBF neural network control of robot with actuator nonlinearities

In this paper, an adaptive neural network control scheme for robot manipulators with actuator nonlinearities is presented. The control scheme consists of an adaptive neural network controller and an actuator nonlinearities compensator. Since the actuator nonlinearities are usually included in the robot driving motor, a compensator using radial basis function (RBF) network is proposed to estimate the actuator nonlinearities and eliminate their effects. Subsequently, an adaptive neural network controller that neither requires the evaluation of inverse dynamical model nor the time-consuming training process is given. In addition, GL matrix and its product operator are introduced to help prove the stability of the closed control system. Considering the adaptive neural network controller and the RBF network compensator as the whole control scheme, the closed-loop system is proved to be uniformly ultimately bounded (UUB). The whole scheme provides a general procedure to control the robot manipulators with actuator nonlinearities. Simulation results verify the effectiveness of the designed scheme and the theoretical discussion.     

A robust training algorithm of discrete-time MIMO RNN and application in fault tolerant control of robotic system

In this paper, a novel robust training algorithm of multi-input multi-output recurrent neural network and its application in the fault tolerant control of a robotic system are investigated. The proposed scheme optimizes the gradient type training on basis of three new adaptive parameters, namely, dead-zone learning rate, hybrid learning rate, and normalization factor. The adaptive dead-zone learning rate is employed to improve the steady state response. The normalization factor is used to maximize the gradient depth in the training, so as to improve the transient response. The hybrid learning rate switches the training between the back-propagation and the real-time recurrent learning mode, such that the training is robust stable. The weight convergence and L ₂ stability of the algorithm are proved via Lyapunov function and the Cluett’s law, respectively. Based upon the theoretical results, we carry out simulation studies of a two-link robot arm position tracking control system. A computed torque controller is designed to provide a specified closed-loop performance in a fault-free condition, and then the RNN compensator and the robust training algorithm are employed to recover the performance in case that fault occurs. Comparisons are given to demonstrate the advantages of the control method and the proposed training algorithm.     

自适应联邦卡尔曼滤波在机器人组合导航系统中的应用研究

利用里程计(OD)与全球定位系统(GPS)辅助捷联惯性导航系统(SINS)构成一种高可靠性的组合导航系统.推导并建立了局部滤波器的数学模型,并针对联邦滤波器在载体发生异常扰动时滤波精度较低的问题,设计了基于SINS/GPS/OD组合导航系统的自适应联邦滤波器,有效补偿了系统异常扰动或动力学模型误差.仿真模拟了机器人的全航线运行轨迹进行验证,仿真结果表明,SINS/GPS/OD组合导航系统的自适应联邦卡尔曼滤波算法与相同组合导航系统的非自适应联邦卡尔曼滤波算法相比,在保障机器人导航定位可靠性及容错能力的前提下,能有效抑制异常扰动的影响,导航精度得到进一步改善.     

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．