Adaptive actuator failure compensation for multivariable feedback linearizable systems

A feedback linearization‐based adaptive control scheme is developed for multivariable nonlinear systems with redundant actuators subject to uncertain failures. Such an adaptive controller contains a direct adaptive actuator failure compensator to compensate the uncertain actuator failure, a nonlinear feedback to linearize the nonlinear dynamics, and a linear feedback to stabilize the linearized system. The key new design feature is the estimation of both the failure patterns and the failure values, for direct adaptive actuator failure compensation, newly developed for multivariable feedback linearizable nonlinear systems. With direct control signal adaptation, the adaptive failure compensation design ensures closed‐loop stability and asymptotic output tracking in the presence of actuator failure uncertainties. Simulation results from an application to attitude control of a near‐space vehicle dynamic model are presented to verify the desired system performance with adaptive actuator failure compensation. Copyright © 2015 John Wiley & Sons, Ltd.     

Adaptive explicit force control of position-controlled manipulators

This article presents a new adaptive outer-loop approach for explicit force regulation of position-controlled robot manipulators. The strategy is computationally simple and does not require knowledge of the manipulator dynamic model, the inner-loop position controller parameters, or the environment. It is shown that the control strategy guarantees global uniform boundedness of all signals and convergence of the position/force regulation errors to zero when applied to the full nonlinear robot dynamic model. If bounded external disturbances are present, a slight modification to the control scheme ensures that global uniform boundedness of all signals is retained and that arbitrarily accurate stabilization of the regulation errors can be achieved. Additionally, it is shown that the adaptive controller is also applicable to robotic systems with PID inner-loop position controllers. Computer simulation results are given for a Robotics Research Corporation (RRC) Model K-1207 redundant arm and demonstrate that accurate and robust force control is achievable with the proposed controller. Experimental results are presented for the RRC Model K-1207 robot and confirm that the control scheme provides a simple and effective means of obtaining high-performance force control. © 1996 John Wiley & Sons, Inc.     

A methodology for designing controllers for industrial systems based on nonlinear separation model and control

A feasible method of controller design for industrial nonlinear dynamic systems is proposed, by separating the nonlinear dynamic system into nonlinear static parts and a linear dynamic part. The proposed method of industrial controller design reduces the existing gaps between the control theory and the actual field. In the controller construction procedure of a system, the nonlinearities are eliminated by using the inversion functions of the nonlinear static parts. Any conventional control theory is ideally applicable to a linear dynamic part of the system. Based on the proposed method of nonlinear separation, controllers were constructed for three different systems: a chemical reactor, a temperature level-controlled tanked water system, and the contour-following control of an articulated robot arm. Some encouraging control performances, superior to those of other existing controllers, showed its significant potential for application to industrial systems with nonlinearities.     

State Space Constrained Iterative Learning Control for Robotic Manipulators

Real‐life work operations of industrial robotic manipulators are performed within a constrained state space. Such operations most often require accurate planning and tracking a desired trajectory, where all the characteristics of the dynamic model are taken into consideration. This paper presents a general method and an efficient computational procedure for path planning with respect to state space constraints. Given a dynamic model of a robotic manipulator, the proposed solution takes into consideration the influence of all imprecisely measured model parameters, making use of iterative learning control (ILC). A major advantage of this solution is that it resolves the well‐known problem of interrupting the learning procedure due to a high transient tracking error or when the desired trajectory is planned closely to the state space boundaries. The numerical procedure elaborated here computes the robot arm motion to accurately track a desired trajectory in a constrained state space taking into consideration all the dynamic characteristics that influence the motion. Simulation results with a typical industrial robot arm demonstrate the robustness of the numerical procedure. In particular, the results extend the applicability of ILC in robot motion control and provide a means for improving the overall trajectory tracking performance of most robotic systems.     

柔性臂漂浮基空间机器人建模与轨迹跟踪控制

利用拉格朗日法和假设模态方法建立了末端柔性的两臂漂浮基空间机器人的非线性动力学方程．通过坐标变换,推导出一种新的以可测关节角为变量的全局动态模型,并在此基础上运用基于模型的非线性解耦反馈控制方法得到关节相对转角与柔性臂的弹性变形部分解耦形式控制方程．最后,讨论了柔性臂漂浮基空间机器人的轨迹跟踪问题,并通过仿真实例计算,表明该模型转换及控制方法对于柔性臂漂浮基空间机器人末端轨迹跟踪控制的有效性．     

Intelligent adaptive model-based control of robotic dynamic systems with a hybrid fuzzy-neural approach

We describe in this paper a new method for adaptive model-based control of robotic dynamic systems using a new hybrid fuzzy-neural approach. Intelligent control of robotic systems is a difficult problem because the dynamics of these systems is highly nonlinear. We describe an intelligent system for controlling robot manipulators to illustrate our fuzzy-neural hybrid approach for adaptive control. We use a new fuzzy inference system for reasoning with multiple differential equations for model selection based on the relevant parameters for the problem. In this case, the fractal dimension of a time series of measured values of the variables is used as a selection parameter. We use neural networks for identification and control of robotic dynamic systems. We also compare our hybrid fuzzy-neural approach with conventional fuzzy control to show the advantages of the proposed method for control.     

欠驱动机械手的非线性补偿控制的实现

以具有欠驱动关节的水平两自由度机械手为对象,建立了其动力学数学模型。对具有非完全约束机器人的特点进行了分析。利用解非线性方程的有效方法一平均值法对模型进行了处理,得到平均值系统,使模型得到简化。含有非驱动关节的平面二自由度机械手具有不可积分的速度和加速度束。因而是一个二阶非完整系统。通过分析系统的动力学机构,设计了一种非线性补偿控制的方法来对欠驱动关节实现控制。实验结果表明。该控制方法在控制模型、控制算法和反馈控制方法上都是十分有效的。     

不确定遥操作系统带干扰观测器的自适应控制

遥操作系统受到不同类型的不确定性因素影响, 这些不确定性会降低系统的透明性, 甚至会使得系统不稳定. 本文提出了一种带干扰观测器的自适应控制器(adaptive controller with disturbance observer, ACWDO) 用来处理遥操作系统中同时受到的外部干扰和内部动力学参数不确定性. 首先建立了受外部干扰的遥操作系统的非线性动力学模型; 然后分别对主机器人和从机器人设计非线性干扰观测器用来对外部干扰进行估计和补偿; 之后在干扰观测器基础之上分别对主机器人和从机器人设计自适应控制器用来处理内部不确定的动力学参数; 最后再将所设计的ACWDO融入到四通道遥操作系统结构中. 理论分析和仿真结果表明, 所设计的控制器可以取得良好的位置跟踪和力跟踪效果, 确保了遥操作系统的透明性.     

Analysis of Nonlinear Multibody Systems with Elastic Couplings

This paper is concerned with the dynamic analysis of nonlinear multibody systems involving elastic members made of laminated, anisotropic composite materials. The analysis methodology can be viewed as a three-step procedure. First, the sectional properties of beams made of composite materials are determined based on an asymptotic procedure that involves a two-dimensional finite element analysis of the cross-section. Second, the dynamic response of nonlinear, flexible multibody systems is simulated within the framework of energy-preserving and energy-decaying time-integration schemes that provide unconditional stability for nonlinear systems. Finally, local three-dimensional stresses in the beams are recovered, based on the stress resultants predicted in the previous step. Numerical examples are presented and focus on the behavior of multibody systems involving members with elastic couplings.     

Accurate dynamic modeling and control parameters design of an industrial hybrid spray-painting robot

To obtain higher performance for hybrid robots subject to nonlinear dynamics and friction, feedforward compensations have been ubiquitously utilized in the industrial robotic field to attenuate these disturbances. However, due to the complex friction model and the coupling and time-varying dynamic of hybrid robots, there is no effective approach to realize accurate feedforward compensations in industrial control systems. This paper investigated an accurate dynamic modeling and control parameters design method to address these issues all at once. Taking the friction of each joint into account, the accurate dynamic model of the hybrid robot is developed and verified by experiments. With the accurate dynamic model, an exact control parameter design method is proposed based on the mapping relationship between the dynamic model and the feedforward compensations. Additionally, the control system designed by the method proposed in this paper is compared with the one tuned by an experienced engineer. Particularly, the robot's position and motion accuracy are also tested by a third-party inspection agency. The experimental and test results show that the position and velocity accuracy of the robot is improved significantly when the control system is designed by using the method proposed in this paper, which proves the effectiveness of the proposed method.     

基于动力学模型的轮式移动机器人电机控制

针对移动机器人两路电机协同控制问题,提出基于动力学模型的轮式移动机器人电机控制律 （DMMC）．首先推导出质心位置不一定在几何中心的移动机器人运动学模型和动力学模型,并求解出两轮速 度与力矩之间的非线性微分方程．然后,基于两轮速度与力矩间非线性微分方程、电机电气方程和电机机电 方程,推导出移动机器人系统状态方程．最后采用极点配置得到I 型状态反馈控制律．仿真显示,DMMC 法实 现了对输入指令的零稳态误差快速响应．     

A perturbation analysis of interactive static and dynamicbifurcations

The instability behavior of a nonlinear autonomous system in the vicinity of a coincident critical point, which leads to interactions between static and dynamic bifurcations, is studied. The critical point considered is characterized by a simple zero and a pair of pure imaginary eigenvalues of the Jacobian, and the system contains two independent parameters. The static and dynamic bifurcations and quasiperiodic motions resulting from the interaction of the bifurcation modes and the associated invariant tori are analyzed by a novel unification technique that is based on an intrinsic perturbation procedure. Divergence boundary, dynamic bifurcation boundary, secondary bifurcations, and invariant tori are determined explicitly. Two illustrative examples concerning control systems are presented     

Dynamic Load Carrying Capacity of Flexible Cable Suspended Robot: Robust Feedback Linearization Control Approach

In this paper dynamic load carrying capacity (DLCC) of a cable robot equipped with a closed loop control system based on feedback linearization, is calculated for both rigid and flexible joint systems. This parameter is the most important character of a cable robot since the main application of this kind of robots is their high load carrying capacity. First of all the dynamic equations required for control approach are represented and then the formulation of control approach is driven based on feedback linearization method which is the most suitable control algorithm for nonlinear dynamic systems like robots. This method provides a perfect accuracy and also satisfies the Lyapunov stability since any desired pole placement can be achieved by using suitable gain for controller. Flexible joint cable robot is also analyzed in this paper and its stability is ensured by implementing robust control for the designed control system. DLCC of the robot is calculated considering motor torque constrain and accuracy constrain. Finally a simulation study is done for two samples of rigid cable robot, a planar complete constrained sample with three cables and 2 degrees of freedom and a spatial unconstrained case with six cables and 6 degrees of freedom. Simulation studies continue with the same spatial robot but flexible joint characteristics. Not only the DLCC of the mentioned robots are calculated but also required motors torque and desired angular velocity of the motors are calculated in the closed loop condition for a predefined trajectory. The effectiveness of the designed controller is shown by the aid of simulation results as well as comparison between rigid and flexible systems.     

A normal form augmentation approach to adaptive control of space robot systems

In this paper, we model a free-floating space robot system as anextended robot which is composed of a pseudo-arm representing the base motion resulting from six hyperthetic passive joints, and a real robot arm. The model allows us to categorize the space robot as an under-actuated system, and reveal fundamental properties of the system. Through input-output linearization of the model, we demonstrate a non-trivial internal dynamics, and propose an adaptive control scheme based on a normal form augmentation approach. This approach overcomes two fundamental difficulties in adaptive control design of space robot systems, i.e., nonlinear parameterization of the dynamic equation, and uncertainty of kinematic mapping from Cartesian space to joint space.     

Integrated Environment for Modelling, Simulation and Control Design for Robotic Manipulators

In this paper an integrated environment for the design of robotic controllers implemented on a PC is described. It is based on the Planar Manipulators Toolbox for dynamic simulation of redundant planar manipulators. The tools are fully integrated in the MATLAB/SIMULINK and hence, a lot of standard tools are available for the analysis and control design. Using the real-time simulation it is possible to apply the developed controllers to a real robot manipulator, which can be included in the system via corresponding interfaces, without any additional coding. The main advantage is the flexibility in fast prototyping of different algorithms in the field of control of robotic systems, especially for redundant manipulators.     

Tracking in nonlinear differential-algebraic control systems with applications to constrained robot systems

We consider the design of a feedback control law for control systems described by a class of nonlinear differential-algebraic equations so that certain desired outputs track given reference inputs. The nonlinear differential-algebraic control system being considered is not in state variable form. Assumptions are introduced and a procedure is developed such that an equivalent state realization of the control system described by nonlinear differential-algebraic equations is expressed in a familiar normal form. A nonlinear feedback control law is then proposed which ensures, under appropriate assumptions, that the tracking error in the closed loop differential-algebraic system approaches zero exponentially. Applications to simultaneous contact force and position tracking in constrained robot systems with rigid joints, constrained robot systems with joint flexibility, and constrained robot systems with significant actuator dynamics are discussed.     

Finite-time control for robot manipulators

Finite-time control of the robot system is studied through both state feedback and dynamic output feedback control. The effectiveness of the proposed approach is illustrated by both theoretical analysis and computer simulation. In addition to offering an alternative approach for improving the design of the robot regulator, this research also extends the study of the finite-time control problem from second-order systems to a large class of higher order nonlinear systems.     

人工神经网络在机械手动力学辨识和位置控制中的应用

本文提出了用人工神经网络近似机械手的逆动力学模型,实现基于模型的非线性控制方案,并以实际的两臂液压机械手为对象给出了仿真结果,整个方案的实现不需要任何关于系统模型的知识.仿真结果表明所研究的位置控制系统具有良好的跟踪性能,并且展示了人工神经网络解决非线性系统辨识和控制问题的潜力.     

HIGH-PERFORMANCE COMPUTING FOR ROBOT DYNAMIC PARAMETERS LEARNING

The robot control problem deals with the computation of the actuating forces/torques in order to provide the desired motion of the end effector. Knowledge of the exact dynamic characteristics of robot manipulators is one of the most significant factors that affects the design of control systems, because the controller performance is directly dependent on the accuracy of the dynamic model. The behavior of dynamic models is normally complicated because of the geometrically varying arm configuration, uncertain loads handled by the end effector, and nonlinear effects resulting from the high interactions among joints. These problems are further complicated by the discrepancy in measuring the arm dynamic parameters (e.g., first moments, radius of gyration of each link). Unless these factors are considered while developing the manipulator dynamic model, the performance of the controller is not expected to meet the given requirements. Thus, the development of an efficient and computationally viable method for estimating the dynamic parameters of a robot arm in situ is essential. In this paper the problem of estimating these parameters in real-time situations is addressed. First, an efficient dynamic model, which is linear in the dynamic parameters, is derived based on the Lagrange-Euler representation. By using this model, the dynamic parameters can easily be isolated and regrouped. Second, to enable real-time applications, the algorithm for computing the dynamic model and the estimation of parameters is distributed     

Mini Rotorcraft Flight Formation Control Using Bounded Inputs

In this paper, the flight formation control and trajectory tracking control design of multiple mini rotorcraft systems are discussed. The dynamic model of a mini rotorcraft is presented using the Newton-Euler formalism. Our approach is based on a leader/follower structure of multiple robot systems. The centroid of the coordinated control subsystem is used for trajectory tracking purposes. A nonlinear controller based on separated saturations and a multi-agent consensus algorithm is developed. The analytic results are supported by simulation tests. Experimental results include yaw coordination and tracking only.