Adaptive Robot Control Research Articles

Direct adaptive fuzzy control of flexible-joint robots including actuator dynamics using particle swarm optimization

In this paper a novel direct adaptive fuzzy system is proposed to control flexible-joints robot including actuator dynamics. The design includes two interior loops: the inner loop controls the motor position using proposed approach while the outer loop controls the joint angle of the robot using a PID control law. One novelty of this paper is the use of a PSO algorithm for optimizing the control design parameters to achieve a desired performance. It is worthy of note that to form control law by considering practical considerations just the available feedbacks are used. It is beneficial for industrial applications wherethe real-time computation is costly. The proposed control approach has a fast response with a good tracking performance under the well-behaved control efforts. The stability is guaranteed in the presence of both structured and unstructured uncertainties. As a result, all system states are remained bounded. Simulation results on a two-link flexible-joint robot show the efficiency of the proposed scheme.

On the adaptive control of cooperative robots with time‐variant holonomic constraints

SummaryIn this work, we consider the problem of 2 robots handling a rigid object, while model‐parameter uncertainties are assumed. It is also assumed that the manipulators can push but not pull the object. Several control schemes proposed in the literature attempt to control the position of the object rather than its orientation. However, many industrial tasks require to move and to rotate the object. To this end, we propose an adaptive hybrid position/force control law based on time‐variant holonomic constraints, which allow for object position and orientation control. Our approach guarantees that the force error asymptotically converges to 0; therefore, a stable grasp can be accomplished by means of a proper definition of the desired pushing force. In addition, a dynamic model of the cooperative system based on the load distribution and joint‐space orthogonalization principles is developed. Experimental results are presented to validate the proposed dynamic model and control scheme.

Adaptive Tracking Control for Robots With an Interneural Computing Scheme.

Adaptive tracking control of mobile robots requires the ability to follow a trajectory generated by a moving target. The conventional analysis of adaptive tracking uses energy minimization to study the convergence and robustness of the tracking error when the mobile robot follows a desired trajectory. However, in the case that the moving target generates trajectories with uncertainties, a common Lyapunov-like function for energy minimization may be extremely difficult to determine. Here, to solve the adaptive tracking problem with uncertainties, we wish to implement an interneural computing scheme in the design of a mobile robot for behavior-based navigation. The behavior-based navigation adopts an adaptive plan of behavior patterns learning from the uncertainties of the environment. The characteristic feature of the interneural computing scheme is the use of neural path pruning with rewards and punishment interacting with the environment. On this basis, the mobile robot can be exploited to change its coupling weights in paths of neural connections systematically, which can then inhibit or enhance the effect of flow elimination in the dynamics of the evolutionary neural network. Such dynamical flow translation ultimately leads to robust sensory-to-motor transformations adapting to the uncertainties of the environment. A simulation result shows that the mobile robot with the interneural computing scheme can perform fault-tolerant behavior of tracking by maintaining suitable behavior patterns at high frequency levels.

Lyapunov-based adaptive control of a cable-driven parallel robot

This paper proposes a novel approach to the model reference adaptive control (MRAS) of a four-cable robot. A linear model is considered instead of the nonlinear model of cable-driven parallel robot system with assuming that the linear model parameters are unknown. Then, a simple control law and proper Lyapunov function are defined. Regarding to Lyapunov function, the proposed control ensures the stability of the closed-loop system. Finally, the properties of the proposed method such as performance tracking and rejecting external disturbance are demonstrated by simulations.

Lyapunov-based adaptive control of a cable-driven parallel robot

This paper proposes a novel approach to the model reference adaptive control (MRAS) of a four-cable robot. A linear model is considered instead of the nonlinear model of cable-driven parallel robot system with assuming that the linear model parameters are unknown. Then, a simple control law and proper Lyapunov function are defined. Regarding to Lyapunov function, the proposed control ensures the stability of the closed-loop system. Finally, the properties of the proposed method such as performance tracking and rejecting external disturbance are demonstrated by simulations.

Dynamic Learning from Adaptive Neural Control of Uncertain Robots with Guaranteed Full-State Tracking Precision

A dynamic learning method is developed for an uncertain n-link robot with unknown system dynamics, achieving predefined performance attributes on the link angular position and velocity tracking errors. For a known nonsingular initial robotic condition, performance functions and unconstrained transformation errors are employed to prevent the violation of the full-state tracking error constraints. By combining two independent Lyapunov functions and radial basis function (RBF) neural network (NN) approximator, a novel and simple adaptive neural control scheme is proposed for the dynamics of the unconstrained transformation errors, which guarantees uniformly ultimate boundedness of all the signals in the closed-loop system. In the steady-state control process, RBF NNs are verified to satisfy the partial persistent excitation (PE) condition. Subsequently, an appropriate state transformation is adopted to achieve the accurate convergence of neural weight estimates. The corresponding experienced knowledge on unknown robotic dynamics is stored in NNs with constant neural weight values. Using the stored knowledge, a static neural learning controller is developed to improve the full-state tracking performance. A comparative simulation study on a 2-link robot illustrates the effectiveness of the proposed scheme.

人工小脳と小脳・マシンインタフェースによる運動学習機構の理解とロボット適応制御への応用

人工小脳と小脳・マシンインタフェースによる運動学習機構の理解とロボット適応制御への応用

Active collision avoidance for human–robot collaboration driven by vision sensors

Establishing safe human–robot collaboration is an essential factor for improving efficiency and flexibility in today’s manufacturing environment. Targeting safety in human–robot collaboration, this paper reports a novel approach for effective online collision avoidance in an augmented environment, where virtual three-dimensional (3D) models of robots and real images of human operators from depth cameras are used for monitoring and collision detection. A prototype system is developed and linked to industrial robot controllers for adaptive robot control, without the need of programming by the operators. The result of collision detection reveals four safety strategies: the system can alert an operator, stop a robot, move away the robot, or modify the robot’s trajectory away from an approaching operator. These strategies can be activated based on the operator’s existence and location with respect to the robot. The case study of the research further discusses the possibility of implementing the developed method in realistic applications, for example, collaboration between robots and humans in an assembly line.

Adaptive robust control of Mecanum-wheeled mobile robot with uncertainties

This paper presents a novel implementation of an adaptive robust second-order sliding mode control (ARSSMC) on a mobile robot with four Mecanum wheels. Each wheel of the mobile robot is actuated by separate motors. It is the first time that higher-order sliding mode control method is implemented for the trajectory tracking control of Mecanum-wheeled mobile robot. Kinematic and dynamic modeling of the robot is done to derive an equation of motion in the presence of friction, external force disturbance, and uncertainties. In order to make the system robust, second-order sliding mode control law is derived. Further, adaptive laws are defined for adaptive estimation of switching gains. To check the tracking performance of the proposed controller, simulations are performed and comparisons of the obtained results are made with adaptive robust sliding mode control (ARSMC) and PID controller. In addition, a new and low-cost experimental approach is proposed to implement the proposed control law on a real robot. Experimental results prove that without compromising on the dynamics of the robot real-time implementation is possible in less computational time. The simulation and experimental results obtained confirms the superiority of ARSSMC over ARSMC and PID controller in terms of integral square error (ISE), integral absolute error (IAE), and integral time-weighted absolute error (ITAE), control energy and total variance (TV).

Fuzzy neural network and observer-based fault-tolerant adaptive nonlinear control of uncertain 5-DOF upper-limb exoskeleton robot for passive rehabilitation

This paper investigates the control of a 5-DOF upper-limb exoskeleton robot used for passive rehabilitation therapy. The robot is subject to uncertain dynamics, disturbance torques, unavailable full-state measurement, and different types of actuation faults. An adaptive nonlinear control scheme, which uses a new reaching law-based sliding mode control strategy, is proposed. This scheme incorporates a high-gain state observer with dynamic high-gain matrix and a fuzzy neural network (FNN) for state vector and nonlinear dynamics estimation, respectively. Using dynamic parameters, the scheme provides an efficient mean for simultaneously tackling the effects of FNN approximation errors, disturbance torques and actuation faults without any prior bounds knowledge and fault detection and diagnosis components. Using simulation results, it is shown that with the presented scheme, faster response, fewer oscillations during transient phase, good tracking accuracy, and chattering-free control torques with lower amplitudes are obtained.

Adaptive tracking control of multi-link robots actuated by pneumatic muscles with additive disturbances

SUMMARYIn this paper, we study the problem of adaptive position tracking for a multi-link robot driven by two opposing pneumatic muscle groups with additive disturbances. In contrast to widely used sliding mode control methods, the proposed controller is continuous and able to prevent chattering. All physical parameters of the robot and the pneumatic muscles, including pneumatic muscle coefficients, link lengths and moments of inertia are unknown and can be time-varying and the unknown additive disturbances can be discontinuous. Under these conditions, we prove that closed-loop trajectories of all of the joint positions can track any C1 joint reference signal. The joint errors will be within a prescribed error bound in a finite time. The adaptive controller only uses the reference signal, not its derivative. The continuous adaptive gain is one-dimensional. Simulations including a two-link robot arm with friction and realistic muscle models are presented to demonstrate the robustness of the adaptive control under severe changes of the system parameters. In all simulations, the joint positions can track C1 trajectories and all errors are within the prescribed error bound in the same time frame, even though the muscle parameters are vastly different.

Adaptive Speed Control of Wheeled Mobile Robot on Uncertain Road Condition

[abstFig src='/00280005/10.jpg' width='300' text='Adaptive speed control for mobile robot' ] This study investigated the effectiveness of a simple adaptive control system used for controlling the dynamics of the driving system of a wheeled mobile robot. This paper describes the design method of a speed control system and the gain adjustment procedure considering a practical application of the simple adaptive control system. Experiments were conducted on two different road conditions, and the results indicated that the rectilinear performance of the wheeled mobile robot improved with the use of the simple adaptive control system. In the case of uncertain road conditions, the simple adaptive control system reduced the deterioration of the control performance effectively.

Solution for a Five Link Industrial Robot Manipulator Inverse Kinematics Using Intelligent Prediction Response Method

Background: Robot kinematics suggests and interprets the relationship exists between the kinematic position connectivity and acceleration of each link. In any of the robot manipulator the kinematics solution may be forward kinematics or inverse kinematics. To determine the joint values for a provided desired end effector orientation and position, the inverse kinematics principle is applied. Inverse kinematics is the usage of kinematics equations of a robot to find out the joint parameters that gives a targeted position of the end-effector. Methods/Statistical Analysis: In this paper, inverse kinematic solution for a five joint robot involving intelligent prediction response method has been developed and the result will be analyzed based on the performance. The intelligent prediction response method will give the performance based result, which shows the five various angles of an industrial robot. The single variation in the joint angle will be analyzed for every joint angles. This method based inverse kinematics solution is much more useful in real-time adaptive robot control where shorter calculation times are required. Findings: The MATLAB 13.0 is used to find the solution for a set of joint parameters. The actual reading and MATLAB program was found acceptable level. Applications/Improvements: To solution provided for the inverse kinematics problem with number of joint angles using the intelligent prediction response method such as artificial neural network is used.

An Integrated Decision Making Approach for Adaptive Shared Control of Mobility Assistance Robots

Mobility assistance robots provide support to elderly or patients during walking. The design of a safe and intuitive assistance behavior is one of the major challenges in this context. We present an integrated approach for the context-specific, on-line adaptation of the assistance level of a rollator-type mobility assistance robot by gain-scheduling of low-level robot control parameters. A human-inspired decision-making model, the drift-diffusion Model, is introduced as the key principle to gain-schedule parameters and with this to adapt the provided robot assistance in order to achieve a human-like assistive behavior. The mobility assistance robot is designed to provide (a) cognitive assistance to help the user following a desired path towards a predefined destination as well as (b) sensorial assistance to avoid collisions with obstacles while allowing for an intentional approach of them. Further, the robot observes the user long-term performance and fatigue to adapt the overall level of (c) physical assistance provided. For each type of assistance a decision-making problem is formulated that affects different low-level control parameters. The effectiveness of the proposed approach is demonstrated in technical validation experiments. Moreover, the proposed approach is evaluated in a user study with 35 elderly persons. Obtained results indicate that the proposed gain-scheduling technique incorporating ideas of human decision-making models shows a general high potential for the application in adaptive shared control of mobility assistance robots.

Adaptive robust image-based visual servoing control of robot with unknown actuator hysteresis

This paper investigates the image-based visual servoing (IBVS) manipulator system with unknown hysteresis nonlinearity by utilizing the uncalibrated eye-to-hand configuration. Compared with the conventional IBVS system, the unknown actuator hysteresis nonlinearity, which widely exists in the real physical mechanical systems and significantly limits the performances of the robotic system, is fully considered. The problem of unknown actuator nonlinearity prevents direct input of existing controllers and will result in some challenging difficulties in control design. In this paper, an adaptive IBVS control scheme, which takes the unknown actuator hysteresis into account, is presented. Without the prior knowledge of both the intervals of hysteresis parameters and the bound of the dynamic disturbance term, a novel adaptive algorithm is developed to estimate the unknown parameters on-line. The image tracking errors are guaranteed to converge to a small neighborhood of the origin. Simulations and comparative experiments are carried out to test the performance of the proposed scheme.

Human-inspired motion model of upper-limb with fast response and learning ability – a promising direction for robot system and control

Purpose – Human movement system is a Multi-DOF, redundant, complex and nonlinear system formed by coordinating combination of neural system, bones, muscles and joints, which is robust and has fast response and learning ability. Imitating human movement system can improve robustness, fast response and learning ability of the robots. Design/methodology/approach – In this paper, we propose a new motion model based on the human motion pathway, especially the information propagation mechanism between the cerebellum and spinal cord. Findings – The proposed motion model proves to have fast response and learning ability through experiments, which matches the features of human motion. Originality/value – The proposed model in this paper introduces the habitual theory in kinesiology and neuroscience into robot control, and improves robustness, fast response and learning ability of the robots. This paper proves that introduction of neuroscience has an important guiding significance for precise and adaptive robot control, such as assembly automation.

Learning from adaptive neural network output feedback control of a unicycle-type mobile robot

This paper studies learning from adaptive neural network (NN) output feedback control of nonholonomic unicycle-type mobile robots. The major difficulties are caused by the unknown robot system dynamics and the unmeasurable states. To overcome these difficulties, a new adaptive control scheme is proposed including designing a new adaptive NN output feedback controller and two high-gain observers. It is shown that the stability of the closed-loop robot system and the convergence of tracking errors are guaranteed. The unknown robot system dynamics can be approximated by radial basis function NNs. When repeating same or similar control tasks, the learned knowledge can be recalled and reused to achieve guaranteed stability and better control performance, thereby avoiding the tremendous repeated training process of NNs.

Decentralized Adaptive Coverage Control of Nonholonomic Mobile Robots

Abstract: This paper proposes an algorithm for decentralized adaptive control for coverage of a given convex region using multiple agents with double integrator dynamics. The result is then extended to nonholonomic mobile robots. The density function which describes the event of interest to be sensed by the agents is assumed to be unknown and an adaptation law is derived which can learn the sensory function with time. This work is an extension of the work by Schwager et al. (2007) for mobile sensors with single integrator dynamics. We propose an algorithm which ensures convergence to a near optimal configuration and demonstrate the same using simulations.

Neural network-based adaptive tracking control of mobile robots in the presence of wheel slip and external disturbance force

In this paper, a novel adaptive tracking controller is proposed for mobile robots in presence of wheel slip and external disturbance force based on neural networks with online weight updating laws. The uncertainties due to the wheel slip and external force are compensated online by neural networks in order to achieve the desired tracking performance. The online weight updating laws are modified versions of the backpropagation with an e-modification term added for robustness. The global uniformly ultimately bounded stability of the system to an arbitrarily small neighborhood of the origin is proven using Lyapunov method. The validity of the proposed controller is confirmed by two simulation examples of tracking a straight line and a U-shape trajectory.

Adaptive Image-Based Trajectory Tracking Control of Wheeled Mobile Robots With an Uncalibrated Fixed Camera

In this paper, the uncalibrated image-based trajectory tracking control problem of wheeled mobile robots will be studied. The motion of the wheeled mobile robot can be observed using an uncalibrated fixed camera on the ceiling. Different from traditional vision-based control strategies of wheeled mobile robots in the fixed camera configuration, the camera image plane is not required to be parallel to the motion plane of the wheeled mobile robots and the camera can be placed at a general position. To guarantee that the wheeled mobile robot can efficiently track its desired trajectory, which is specified by the desired image trajectory of a feature point at the forward axis of the wheeled mobile robot, we will propose a new adaptive image-based trajectory tracking control approach without the exact knowledge of the camera intrinsic and extrinsic parameters and the position parameter of the feature point. To eliminate the nonlinear dependence on the unknown parameters from the closed-loop system, a depth-independent image Jacobian matrix framework for the wheeled mobile robots will be developed such that unknown parameters in the closed-loop system can be linearly parameterized. In this way, adaptive laws can be designed to estimate the unknown parameters online, and the depth information of the feature point can be allowed to be time varying in this case. The Lyapunov stability analysis will also be performed to show asymptotical convergence of image position and velocity tracking errors of the wheeled mobile robot. The simulation results based on a two-wheeled mobile robot will be given in this paper to illustrate the performance of the proposed approach as well. The experimental results based on a real wheeled mobile robot will also be provided to validate the proposed approach.