Control Problem For Robot Manipulators Research Articles

Observer based output feedback repetitive learning control of robotic manipulators

AbstractThis work tackles the tracking control problem of robotic manipulators where the robot dynamics contains uncertain parameters and joint velocity measurements are not available. Specifically when the robotic manipulator is required to perform a periodic task repetitively, as in most industrial applications, a repetitive learning controller is proposed that does not require joint velocity measurements and can compensate the uncertainties of the robot dynamical parameters and additive disturbances caused due to the periodic joint motion. The proposed solution is achieved via the use of a novel learning component in the controller design in conjunction with a novel model‐free joint velocity observer design. The stability of the closed‐loop system and the convergence of both the joint position tracking error and the joint velocity observation error to the origin are guaranteed via Lyapunov based arguments. Experimental results performed on a 2 degree of freedom robot manipulator are presented to demonstrate the performance of the proposed observer–controller couple.

Nonlinear optimal control for robotic exoskeletons with electropneumatic actuators

Nonlinear optimal control for robotic exoskeletons with electropneumatic actuators

Flatness-based control in successive loops for robotic manipulators and autonomous vehicles

ABSTRACT The control problem for the multivariable and nonlinear dynamics of robotic manipulators and autonomous vehicles is solved with the use of a flatness-based control approach which is implemented in successive loops. The state-space model of these robotic systems is separated into two subsystems, which are connected between them in cascading loops. Each one of these subsystems can be viewed independently as a differentially flat system and control about it can be performed with inversion of its dynamics as in the case of input–output linearised flat systems. The state variables of the second subsystem become virtual control inputs for the first subsystem. In turn, exogenous control inputs are applied to the first subsystem. The whole control method is implemented in two successive loops and its global stability properties are also proven through Lyapunov stability analysis. The validity of the control method is confirmed in two case studies: (a) control of a 3-DOF industrial rigid-link robotic manipulator and (b) control of a 3-DOF autonomous underwater vessel. The novel control method can simplify significantly the solution of the nonlinear control problem for robotic manipulators and vehicles. Unlike global linearisation-based control schemes, the proposed flatness-based method in successive loops does not need any changes in state variables of complicated state-space model transformations.

Actor–critic learning based PID control for robotic manipulators

In this paper, we propose a reinforcement learning structure for auto-tuning PID gains by solving an optimal tracking control problem for robot manipulators. Capitalizing on the actor–critic framework implemented by neural networks, we achieve optimal tracking performance, estimating unknown system dynamics simultaneously. The critic network is used to approximate the cost function, which serves as an indicator of control performance. With feedback from the critic, the actor network learns time-varying PID gains over time to optimize control input, thereby steering the system toward optimal performance. Furthermore, we utilize Lyapunov’s direct method to demonstrate the stability of the closed-loop system. This approach provides an analytical procedure for a stable robot manipulator system to systematically adjust PID gains, bypassing the ad-hoc and painstaking process. The resultant actor–critic PID-like control exhibits stable adaptive and learning capabilities while maintaining a simple structure and inexpensive online computational demands. Numerical simulations underscore the effectiveness and advantages of the proposed actor–critic neural network PID control.

Adaptive task‐space cooperative tracking control for manipulators with a desired trajectory estimator and a velocity observer

AbstractIn this article, the task‐space cooperative tracking control problem of robotic manipulators without velocity measurements is investigated, where the desired trajectory of an unknown object is only available to the root manipulator. A cooperative tracking control scheme is presented, which is composed of a task‐space desired trajectory estimator, a task‐space velocity observer and an adaptive cooperative control law. The estimator is presented to provide the desired trajectory for each manipulator, and the observer is designed to obtain the task‐space velocity in the absence of velocity measurements. Based on the estimator and velocity observer, an adaptive cooperative control law is proposed to satisfy task tracking and motion synchronization of manipulators in the presence of kinematic and dynamic uncertainties. Furthermore, strict stability analysis is conducted to prove that the task‐space tracking and synchronization errors converge to zero under a connected directed graph. Finally, simulation results are given to demonstrate the superiority of the proposed scheme.

Intelligent optimal hybrid motion/force control of constrained robot manipulator

This paper presents a neural network-based optimal control approach for hybrid motion/force control problem of robot manipulators in the presence of structured and unstructured uncertainties. The quadratic optimisation with sliding mode control and neural network is utilised to propose an optimal hybrid motion/force control of robotic manipulators. Firstly, the dynamics of robot manipulator is reduced into state-space form describing the constrained and unconstrained motion separately. Then the optimal hybrid motion/force control scheme is derived utilising the optimisation of Hamilton Jacobi Bellman (HJB) equation and sliding mode control approach. The structured and unstructured uncertainties of the system are compensated using radial basis function neural network and adaptive compensator. The radial basis function neural network approximates the unknown dynamics and adaptive compensator is used to estimate the bounds on neural network approximation error and the unstructured uncertainties of the system. The neural networks are trained in online manner using weight update algorithms derived with Lyapunov approach to guarantee the tracking stability and error convergence with prescribed quadratic performance index. Finally, the proposed control approach is verified through simulation results with two link constrained manipulator in a comparative manner.

Adaptive Fast Nonsingular Fixed-Time Tracking Control for Robot Manipulators

This paper studies the fixed-time trajectory tracking control problem of robot manipulators in the presence of uncertain dynamics and external disturbances. First, a novel nonsingular fixed-time sliding mode surface is presented, which can ensure that the convergence time of the suggested surface is bounded regardless of the initial states. Subsequently, a novel fast nonsingular fixed-time sliding mode control (NFNFSMC) is developed so that the closed-loop system is fixed-time convergent to the equilibrium. By applying the proposed NFNFSMC method and the adaptive technique, a novel adaptive nonsingular fixed-time control scheme is proposed, which can guarantee fast fixed-time convergence of the tracking errors to small regions around the origin. With the proposed control method, the lumped disturbance is compensated by the adaptive technique, whose prior information about the upper bound is not needed. The fixed-time stability of the trajectory tracking control under the proposed controller is proved by the Lyapunov stability theory. Finally, corresponding simulations are given to illustrate the validity and superiority of the proposed control approach.

Fully Uncalibrated Image-Based Visual Servoing of 2DOFs Planar Manipulators With a Fixed Camera.

We consider the uncalibrated vision-based control problem of robotic manipulators in this work. Though lots of approaches have been proposed to solve this problem, they usually require calibration (offline or online) of the camera parameters in the implementation, and the control performance may be largely affected by parameter estimation errors. In this work, we present new fully uncalibrated visual servoing approaches for position control of the 2DOFs planar manipulator with a fixed camera. In the proposed approaches, no camera calibration is required, and numerical optimization algorithms or adaptive laws for parameter estimation are not needed. One benefit of such features is that exponential convergence of the image position errors can be ensured regardless of the camera parameter uncertainties. Generally, existing uncalibrated approaches only can guarantee asymptotical convergence of the position errors. Moreover, different from most existing approaches which assume that the robot motion plane and the image plane are parallel, one of the proposed approaches allows the camera to be installed at a general pose. This also simplifies the controller implementation and improves the system design flexibility. Finally, simulation and experimental results are provided to illustrate the effectiveness of the presented fully uncalibrated visual servoing approaches.

Inverse Jacobian Adaptive Tracking Control of Robot Manipulators with Kinematic, Dynamic, and Actuator Uncertainties

In this paper, we mainly solve the adaptive control problem of robot manipulators with uncertain kinematics, dynamics, and actuators parameters, which has been a long-standing, yet unsolved problem in the robotics field, because of the technical difficulties in handling highly coupled effect between control torque and the mentioned uncertainties. To overcome the difficulties, we propose a new Lyapunov-based adaptive control methodology, which effectively fuses the inverse Jacobian technique and the actuator adaptation law, with which the chattering in tracking errors caused by actuator parameter perturbation is well suppressed. It is demonstrated that the asymptotic convergence of all closed-loop signals is guaranteed. Moreover, the effectiveness of our control scheme is illustrated through simulation studies.

An asinh-type regulator for robot manipulators with global asymptotic stability

In this paper, a new asinh-type control scheme with gravity compensation for the position control problem of robot manipulators in joint space is presented. The properties and characteristics of the asinh control structure make the position error and the motion velocity asymptotically converge to the equilibrium point. A strict Lyapunov function to formally prove global asymptotic stability is developed. The tuning of the control gains is obtained by PSO (Particle Swarm Optimization) technique without saturating the servomotors. To illustrate the effectiveness and performance of the proposed scheme, an experimental comparative analysis between the proportional-derivative (PD) and atanh controls against the proposed algorithm on a three degrees of freedom direct-drive robot manipulator is carried out.

Critic Learning-Based Control for Robotic Manipulators With Prescribed Constraints.

In this article, the optimal control problem for robotic manipulators (RMs) with prescribed constraints is addressed. Considering the environmental conditions and requirements of practical applications, prescribed constraints are imposed on the system states to guarantee the control performance and normal operation of the robotic system. Accordingly, an error transformation function is adopted to cope with the prescribed constraints and generate an equivalent unconstrained error for the convenience of the intelligent control design. In order to improve the learning ability and optimize the control performance, critic learning (CL) is introduced to the control design of the constrained RM based on the transformed equivalent unconstrained system. In addition, the stability analysis is given to illustrate the feasibility of the proposed CL-based control. Finally, simulations are conducted on a two-degree-of-freedom (DOF)-constrained RM to further validate the effectiveness of the proposed controller.

Robust Tracking Control of Robot Manipulators With Actuator Faults and Joint Velocity Measurement Uncertainty

This article solves the safe trajectory tracking control problem of robot manipulators with actuator faults, uncertain dynamics, and external disturbance. Another key issue met in practical robot engineering, i.e., joint velocity measurement uncertainty is also investigated. A robust control framework is presented. In this approach, a novel reconstruction law is preliminarily developed to estimate velocity measurement uncertainty, while the estimation error is finite-time stable. An adaptive control law is, then, designed by using the joint position, the estimated velocity, and the reconstructed knowledge of velocity measurement uncertainty. The key advantage of this methodology is that actual joint velocity and any prior knowledge of actuator faults are not required. The effectiveness of this proposed scheme is experimentally validated on a real robot arm.

ANFIS-based an adaptive continuous sliding-mode controller for robot manipulators in operational space

This paper addresses the task-space robust trajectory tracking control problem for robot manipulators in the presence of uncertainties and external disturbances. First, a discontinuous sliding-mode controller-based inverse dynamics control strategy (IDSMC) with discontinuous robust control action is synthesized. Second, an adaptive inverse dynamics controller based on continuous sliding-mode control (AIDCSMC) is designed, in which the adaptation laws are addressed to compensate for the unknown parameters of the dynamical model of robot manipulators. The global stability of the closed-loop control system is proven using the Lyapunov theorem and the proposed AIDCSMC controller is further proven to guarantee convergence to zero of both trajectory tracking error and error rate. Finally, a hybrid intelligent neuro-fuzzy adaptive fuzzy inference system (ANFIS)-based adaptive inverse dynamics controller with continuous sliding-mode control (ANFIS-AIDCSMC) is adopted. Numerical simulations using the dynamic model of rigid robot manipulators with uncertainties show the effectiveness of the presented approach in simple and complex trajectory tracking problems. The simulation results indicate that the control performance of the robot system is satisfactory, and the proposed approach can achieve favorable tracking performance and it is robust with regard to uncertainties.

Finite-time tracking control for uncertain robotic manipulators using backstepping method and novel extended state observer

This article considers finite-time trajectory tracking control problem for robotic manipulators with parameter uncertainties and external disturbances. A finite-time controller that achieves high precision and strong robustness is proposed without the requirement of the exact dynamic model. First, a novel finite-time model-assisted extended state observer is designed to compensate the system uncertainties with complex and uncertain dynamics. Then, a composite finite-time controller is developed for trajectory tracking control with the help of finite-time model-assisted extended state observer. Compared to the classic extended state observer, it is proved that the estimation error of finite-time model-assisted extended state observer can be stabilized in finite time. Meanwhile, the finite-time convergence of the closed-loop system with the proposed controller can also be proved through Lyapunov’s stability theory. A variable structure term is employed to compensate the estimation errors of finite-time model-assisted extended state observer. The validity of the control scheme is demonstrated by simulations and experiments.

Proportional Retarded Control of Robot Manipulators

In the present contribution, a methodology to solve the tracking control problem of robot manipulators through the use of a Proportional Retarded plus Gravity (PR+G) compensation scheme is presented. The main advantage of the proposal is to avoid the necessity of velocity measurements or their estimation, which is commonly used in most control schemes, such as the proportional derivative-type controllers or the computed torque control. The design of the PR+G controller is addressed via σ-stability analysis and its performance is tested in an experimental platform that consists of 2 degrees of the freedom robot manipulator. The proposed controller is compared with a classic proportional derivative plus gravity compensation scheme. The results are analyzed from a frequency perspective and measured by a quadratic error index.

Adaptive Control of Robot Manipulators With Uncertain Kinematics and Dynamics

In this paper, we investigate the adaptive control problem for robot manipulators with both the uncertain kinematics and dynamics. We propose two adaptive control schemes to realize the objective of task-space trajectory tracking irrespective of the uncertain kinematics and dynamics. The proposed controllers have the desirable separation property, and we also show that the first adaptive controller with appropriate modifications can yield improved performance, without the expense of conservative gain choice. The performance of the proposed controllers is shown by numerical simulations.

Third order sliding mode control of a medical robot for tele-echography

This paper deals with the control problem of robotic manipulators, which are always subject to nonlinear uncertainties. In particular, a dynamic system with fast actuators is exposed to many disrupting risks, generally induced by the presence of parametric uncertainties and unmoderated dynamics. So the dynamic model becomes highly nonlinear, which requires the use of a robust stabilising control. In this context, we propose a solution based on the third order sliding mode control (SMC) approach, in order to ensure a performant trajectory tracking motion control for a medical task. The proposed controller is expected to remove the standard sliding mode restrictions, to provide for a higher accuracy and robustness in realisation with respect to the current existence of imperfections. Simulation results confirm the effectiveness of the proposed third order SMC design with respect to disturbances, mainly affecting control systems.

Third order sliding mode control of a medical robot for tele-echography

This paper deals with the control problem of robotic manipulators, which are always subject to nonlinear uncertainties. In particular, a dynamic system with fast actuators is exposed to many disrupting risks, generally induced by the presence of parametric uncertainties and unmoderated dynamics. So the dynamic model becomes highly nonlinear, which requires the use of a robust stabilising control. In this context, we propose a solution based on the third order sliding mode control (SMC) approach, in order to ensure a performant trajectory tracking motion control for a medical task. The proposed controller is expected to remove the standard sliding mode restrictions, to provide for a higher accuracy and robustness in realisation with respect to the current existence of imperfections. Simulation results confirm the effectiveness of the proposed third order SMC design with respect to disturbances, mainly affecting control systems.

ℒ2 performance control of robot manipulators with kinematics, dynamics and actuator uncertainties

Summary This paper deals with the task-space trajectory tracking control problem of robot manipulators. An improved adaptive backstepping controller is proposed to deal with the uncertainties in kinematics, dynamics, and actuator modeling. To avoid the explosion of computation in conventional backstepping techniques, a modified dynamic surface control algorithm is proposed, which guarantees the asymptotic convergence rather than the uniformly ultimately boundedness of tracking errors in conventional dynamic surface control methods. Furthermore, the expression of the ℒ2 norm of tracking errors is explicitly derived in relation to the controller parameters, which provides instructions on tuning controller parameters to adjust the system performance. Moreover, the passivity structure of the designed adaptation law is thoroughly analyzed. Simulation of a free-floating space robot is used to verify the effectiveness of the proposed control strategy in comparison with the conventional tracking control schemes. Copyright © 2016 John Wiley & Sons, Ltd.

Gravity-Compensation-Driven Position Regulation for Robotic Systems Under Input/Output Delays

Input/output (I/O) delays in a closed-loop robotic system can significantly deteriorate both its stability and performance. In this paper, we study the position control problem for robotic manipulators under I/O delays, where the delays manifest as a result of communication with a non-collocated controller. Previous research on this topic has assumed the gravitational torque to be pre-compensated in a collocated manner. This paper studies the stability and regulation performance of the closed-loop system when the compensation for the gravitational torque is provided by a non-collocated controller. We demonstrate that simply utilizing scattering transformation for robotic systems under I/O communication delays results in position drift due to gravity compensation. Hence, a new control algorithm incorporating delayed position feedback and scattering variables is studied subsequently. Stability and performance margins dependent on the round-trip delays and the gravitational model are proposed to guarantee stable position regulation. Since the control architecture with scattering transformation does not address the stability problem due to sensing-actuation delays, a new controller is developed to cope with robotic control systems under time-varying sensing-actuation delays. The proposed control algorithms are studied in this paper via experiments on a manipulator with three degrees of freedom.