Adaptive neural controller for cooperative multiple robot manipulator system manipulating a single rigid object

In this article, an adaptive neural controller is developed for cooperative multiple robot manipulator system carrying and manipulating a common rigid object. In coordinated manipulation of a single object using multiple robot manipulators simultaneous control of the object motion and the internal force exerted by manipulators on the object is required. Firstly, an integrated dynamic model of the manipulators and the object is derived in terms of object position and orientation as the states of the derived model. Based on this model, a controller is proposed that achieves required trajectory tracking of the object as well as tracking of the desired internal forces arising in the system. A feedforward neural network is employed to learn the unknown dynamics of robot manipulators and the object. It is shown that the neural network can cope with the unknown nonlinearities through the adaptive learning process and requires no preliminary offline learning. The adaptive learning algorithm is derived from Lyapunov stability analysis so that both error convergence and tracking stability are guaranteed in the closed loop system. Finally, simulation studies and analysis are carried out for two three-link planar manipulators moving a circular disc on specified trajectory.     

Robust adaptive tracking control of robotic systems with uncertainties

To deal with the uncertainty factors of robotic systems, a robust adaptive tracking controller is proposed. The knowledge of the uncertainty factors is assumed to be unidentified; the proposed controller can guarantee robustness to parametric and dynamics uncertainties and can also reject any bounded, immeasurable disturbances entering the system. The stability of the proposed controller is proven by the Lyapunov method. The proposed controller can easily be implemented and the stability of the closed system can be ensured; the tracking error and adaptation parameter error are uniformly ultimately bounded （UUB）. Finally, some simulation examples are utilized to illustrate the control performance.     

Locomotion control of a four-legged robot embedding real-time reasoning in the force distribution

This paper discusses the application of rule-based reasoning to manage in real time the force distribution computation within a locomotion control of quadruped robots. The control uses input–output linearization in the attitude subsystem, and optimal linear control in the overall locomotion system. The force distribution approach provides more adaptability and flexibility to the locomotion control, because the system is capable of fast adaptation to a wide variety of situations. Rules defining the knowledge about how to deal with walk events and feet forces calculation are presented. The rule-based reasoning is made using the system (LAAS).     

On trajectory and force tracking control of constrained mobile manipulators with parameter uncertainty

This paper studies the trajectory and force tracking control problem of mobile manipulators subject to holonomic and nonholonomic constraints with unknown inertia parameters. Adaptive controllers are proposed based on a suitable reduced dynamic model, the defined reference signals and the mixed tracking errors. The proposed controllers not only ensure the entire state of the system to asymptotically converge to the desired trajectory but also ensure the constraint force to asymptotically converge to the desired force. A detailed numerical example is presented to illustrate the developed methods.     

Two adaptive control structures of robot manipulators

This paper proposes two simple adaptive control schemes of robot manipulators. The first one is the state feedback control which consists of feedforward from the desired position trajectory, PD feedback from the actual trajectory, and an auxiliary input. The second one is the feedforward/feedback control which consists of a feedforward term from the desired position, velocity, and acceleration trajectory based on the inverse of robot dynamics. The feedforward, feedback, and auxiliary gains are adapted using simple equations derived from the decentralized adaptive control theory based on Lyapunov's direct method, and using only the local information of the corresponding joint. The proposed control schemes are computationally fast and do not require a priori knowledge of the detail parameters of the manipulator or the payload. Simulation results are presented in support of the proposed schemes. The results demonstrate that both controllers perform well with bounded adaptive gains.     

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.     

An adaptive control strategy for postural stability using a wearable robot

Wearable robots are expected to expand the use of robotics in rehabilitation since they can widen the assistance application context. An important aspect of a rehabilitation therapy, in terms of lower extremity assistance, is balance control. In this article, we propose and evaluate an adaptive control strategy for robotic rehabilitation therapies to guarantee static stability using a wearable robot. Postural balance control can be implemented either acting on the hip, on the ankle joint or on both, depending on the kind of perturbation acting on the subject: internal or external. Internal perturbations can be produced by any voluntary movement of the body, such as bending the trunk. External perturbations, in the form of an impact force, are applied by the exoskeleton without any prior notice to observe the proactive response of the subject. We have used a 6 degree of freedom planar lower limb exoskeleton, H1, to perform this analysis. The developed control strategy has been designed to provide the necessary assistance, related to balance recovery and postural stability, under the “Assist-as-needed” paradigm. The interaction forces between orthosis and subject are monitored, as they play a relevant role in the definition of assistive and resistive movements to be applied to the joints. The proposed method has been tested with 5 healthy subjects in presence of internal and external disturbances. The results demonstrate that knowing the stability limit of each subject, in combination with a therapeutically selected scaling factor, the proposed adaptive control helps in providing an effective assistance in therapy. This method is efficient in handling the individual and combined effect of external perturbations acting on any joint movements.     

Force/position tracking for a robotic manipulator in compliant contact with a surface using neuro-adaptive control

The problem of force/position tracking for a robotic manipulator in compliant contact with a surface under non-parametric uncertainties is considered. In particular, structural uncertainties are assumed to characterize the compliance and surface friction models, as well as the robot dynamic model. A novel neuro-adaptive controller is proposed, that exploits the approximation capabilities of the linear in the weights neural networks, guaranteeing the uniform ultimate boundedness of force and position error with respect to arbitrarily small sets, plus the boundedness of all signals in the closed loop. Simulations highlight the approach.     

A novel robust adaptive control algorithm with finite-time online parameter estimation of a humanoid robot arm

A novel robust adaptive control algorithm is proposed and implemented in real-time on two degrees-of-freedom (DOF) of the humanoid Bristol-Elumotion-Robotic-Torso II (BERT II) arm in joint-space. In addition to having a significant robustness property for the tracking, the algorithm also features a sliding-mode term based adaptive law that captures directly the parameter estimation error. An auxiliary filtered regression vector and filtered computed torque is introduced. This allows the definition of another auxiliary matrix, a filtered regression matrix, which facilitates the introduction of a sliding mode term into the adaptation law. Parameter error convergence to zero can be guaranteed within finite-time with a Persistent-Excitation (PE) condition or Sufficient Richness condition for the demand. The proposed scheme also exhibits robustness both in the tracking and parameter estimation errors to any bounded additive disturbance. This theoretical result is then exemplified for the BERT II robot arm in simulation and for experiments.     

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.     

机械臂鲁棒自适应运动控制

针对具有不确定性的机械臂系统,文中阐述了一种基于势函数和Lyapunov稳定性理论的鲁棒自适应控制方法．它是通过合理选择与控制目标相关的势函数,并根据模型中不确定性的实时变化,在控制器中引入可在线可调参数,使得控制器机械臂能够跟踪给定的有界参考信号,跟踪误差收敛到包含零点的很小的邻域内．同时该闭环系统的所有状态半全局最终一致有界（SGUUB）．仿真研究表明该方法的有效性．     

Decentralized and adaptive control of multiple nonholonomic robots for sensing coverage

This work deals with decentralized control of multiple nonholonomic mobile sensors for optimal coverage of a given area for sensing purposes. We assume a density function over the region to be covered, which can be viewed as a probability density of the phenomena to be sensed. The density function is unknown but assumed to be linearly parameterized with unknown parameter weights. We consider a second‐order dynamic model for the mobile agents and derive decentralized adaptive control laws to achieve optimal coverage of the region. We then consider the case where the dynamic model of the agents are not fully known, and then develop parameter adaptation laws to achieve the optimal coverage objective. We test the derived algorithms using simulations and compare our proposed controllers with kinematics‐based controllers. We find that the feedback control design based on the dynamic model performs significantly better than controllers solely relying on kinematic models. Furthermore, for the unknown dynamics case, our controller outperforms the nonadaptive controller with poor initial parameter estimates.     

Robust adaptive motion/force tracking control design for uncertain constrained robot manipulators

In the presence of uncertain constraint and robot model, an adaptive controller with robust motion/force tracking performance for constrained robot manipulators is proposed. First, robust motion and force tracking is considered, where a performance criterion containing disturbance and estimated parameter attenuations is presented. Then the proposed controller utilizes an adaptive scheme and an auxiliary control law to deal with the uncertain environmental constraint, disturbances, and robotic modeling uncertainties. After solving a simple linear matrix inequality for gain conditions, the effect from disturbance and estimated parameter errors to motion/force errors is attenuated to an arbitrary prescribed level. Moreover, if the disturbance and estimated parameter errors are square-integrable, then an asymptotic motion tracking is achieved while the force error is as small as the inversion of control gain. Finally, numerical simulation results for a constrained planar robot illustrate the expected performance.     

Adaptive hybrid force/position control of robot manipulators using an adaptive force estimator in the presence of parametric uncertainty

This study is devoted to sensorless adaptive force/position control of robot manipulators using a position-based adaptive force estimator (AFE) and a force-based adaptive environment compliance estimator. Unlike the other sensorless method in force control that uses disturbance observer and needs an accurate model of the manipulator, in this method, the unknown parameters of the robot can be estimated along with the force control. Even more, the environment compliance can be estimated simultaneously to achieve tracking force control. In fact, this study deals with three challenging problems: No force sensor is used, environment stiffness is unknown, and some parametric uncertainties exist in the robot model. A theorem offers control laws and updating laws for two control loops. In the inner loop, AFE estimates the exerted force, and then, the force control law in the outer loop modifies the desired trajectory of the manipulator for the adaptive tracking loop. Besides, an updating law updates the estimated compliance to provide an accurate tracking force control. Some experimental results of a PHANToM Premium robot are provided to validate the proposed scheme. In addition, some simulations are presented that verify the performance of the controller for different situations in interaction.     

An output feedback parallel force/position regulator for a robot manipulator in contact with a compliant environment

In this paper the problem of regulating force and position for a robot manipulator in contact with an elastically compliant environment is considered. In the framework of parallel force/position control, an output feedback regulator with gravity compensation and desired force feedforward is proposed which only requires measurements of force and position. Semiglobal stability of the closed-loop system around the equilibrium is shown via a Lyapunov argument.     

一种通过模糊自调整应用于机械手广义力/位置控制的稳定系统研究

提出一种基于模糊自调整的机械手控制结构,并针对机械手与外界环境接触时产生的作用力,定义了一种广义力,它是机械手执行机构输出力与机械手末端受到外界力的合力。那么,就可以用类似于机械手位置控制的方法达到力/位置控制目的,通过模糊自调整方法实现。在机械手受到的外力是有界限的前提下,考虑机械手非线性、耦合和多变量的动态特征,证明了整个闭环系统是全局稳定的。     

Adaptive motion control law of a robotic wheelchair

In the present work, a dynamic model of a robotic wheelchair is developed considering a lateral deviation of the center of mass. The Lyapunov and input/output stability theories are used to design a novel tracking and positioning adaptive control for the robotic wheelchair. Properties of the dynamic model with respect to its matrices and parameters are shown. A filter is used to obtain a closed loop equation that allows designing the adaptive control law. Then, a projection algorithm is used to improve the adaptive control in the sense of avoiding parameter drift. Experimental results show good performance of the adaptive control.     

基于T- S模型的非线性系统多模型直接自适应控制

针对典型的高阶非线性系统，建立被控对象的多个论域不同的基于T—S模型的模糊控制器(TSFC)，用其加权组合控制系统的行为，并报据Lyapunov的综合方法设计一种自适应算法来调整每个TSFC的权值，形成被控对象的直接自适应模糊控制器。与采用单一TSFC的自适应模糊控制算法相比，该算法计算量小，响应速度快，能在局部上更有效地控制系统的非线性，使被控系统具有Lyapunov意义上的稳定性。仿真实验证实了算法的有效性。     

Dynamic control of coordinated redundant robots with torque optimization

The problem of controlling a system of coordinated redundant robots with torque optimization based on joint redundancy is addressed. Local and global optimal control laws, both minimizing joint torque loading, are developed. A general method of load distribution among the coordinated robots is also proposed. The control problem is to regulate the motion of the object held by the coordinated robots and the internal force generated as a result of constraints on the object. The errors in the object motion and internal force converge asymptotically to zero under the proposed optimal control laws, when exact knowledge of the dynamic models is assumed. Furthermore, the robustness of the proposed method to model uncertainty is also analyzed. The motion and internal force errors are uniformly ultimately bounded under the proposed optimal controllers, when uncertainty in the dynamic models is assumed to exist.     

Decentralized adaptive control of electrically-driven manipulators

This paper presents two new decentralized strategies for motion control of uncertain electrically-driven manipulators. The first controller is an adaptive position regulation scheme which ensures semiglobal asymptotic convergence of the position error if no external disturbances are present and semiglobal convergence of the error to an arbitrarily small neighborhood of zero in the presence of bounded disturbances. It is shown that the regulation scheme can be modified to provide accurate trajectory tracking control through the introduction of adaptive feedforward elements in the control law; this second control strategy retains the simple decentralized structure of the first controller and ensures arbitrarily accurate tracking in the presence of bounded disturbances. Each of the adaptive schemes is very efficient computationally and requires virtually no information concerning either the manipulator or actuator models. The results of computer simulations and laboratory experiments with both terrestrial and space manipulators demonstrate that accurate and robust motion control can be achieved by using the proposed approach.