Modular Robot Manipulators Research Articles

Barrier-critic-disturbance approximate optimal control of nonzero-sum differential games for modular robot manipulators

In this paper, for addressing the safe control problem of modular robot manipulators (MRMs) system with uncertain disturbances, an approximate optimal control scheme of nonzero-sum (NZS) differential games is proposed based on the control barrier function (CBF). The dynamic model of the manipulator system integrates joint subsystems through the utilization of joint torque feedback (JTF) technique, incorporating interconnected dynamic coupling (IDC) effects. By integrating the cost functions relevant to each player with the CBF, the evolution of system states is ensured to remain within the safe region. Subsequently, the optimal tracking control problem for the MRM system is reformulated as an NZS game involving multiple joint subsystems. Based on the adaptive dynamic programming (ADP) algorithm, a cost function approximator for solving Hamilton–Jacobi (HJ) equation using only critic neural networks (NN) is established, which promotes the feasible derivation of the approximate optimal control strategy. The Lyapunov theory is utilized to demonstrate that the tracking error is uniformly ultimately bounded (UUB). Utilizing the CBF’s state constraint mechanism prevents the robot from deviating from the safe region, and the application of the NZS game approach ensures that the subsystems of the MRM reach a Nash equilibrium. The proposed control method effectively addresses the problem of safe and approximate optimal control of MRM system under uncertain disturbances. Finally, the effectiveness and superiority of the proposed method are verified through simulations and experiments.

Nonzero-sum game-based decentralized approximate optimal control of modular robot manipulators with coordinate operation tasks using value iteration

Nonzero-sum game-based decentralized approximate optimal control of modular robot manipulators with coordinate operation tasks using value iteration

Dynamic parameter identification of modular robot manipulators based on hybrid optimization strategy: genetic algorithm and least squares method

Dynamic parameter identification of modular robot manipulators based on hybrid optimization strategy: genetic algorithm and least squares method

Integral reinforcement learning-based event-triggered optimal tracking control for modular robot manipulators via non-zero-sum game

Abstract Under an event-triggered mechanism, a non-zero-sum (NZS) game optimal tracking control method for modular robot manipulator (MRM) systems with input constraints is proposed using the adaptive dynamic programming (ADP) method based on integral reinforcement learning (IRL). First, a dynamic model of the MRM system is developed based on joint torque feedback technology, consisting of an n-joint subsystem related to interconnected dynamic coupling (IDC). Second, we design a robust compensation controller to handle the known model term and an optimal compensation controller to deal with the uncertainty term caused by the IDC and friction, respectively. In addition, a nonlinear disturbance observer is established to dispose of the negative effects caused by the uncertain sensor output disturbance. Third, based on differential game theory, we transform the optimal tracking control problem of the MRM system into an n-player NZS game problem. Then, the IRL-based ADP method is adopted, which relaxes the need for system partial unknown dynamic information, and only a critic neural network is used to solve the coupled Hamilton–Jacobi equation, so as to obtain the optimal control policy. Then, using Lyapunov theory, the tracking error of the MRM system is demonstrated to be uniformly ultimately bounded. Finally, the effectiveness and superiority of the proposed algorithm are verified through experiments.

Decentralized variable impedance control of modular robot manipulators with physical human–robot interaction using Gaussian process-based motion intention estimation

Decentralized variable impedance control of modular robot manipulators with physical human–robot interaction using Gaussian process-based motion intention estimation

Adaptive Impedance Decentralized Control of Modular Robot Manipulators for Physical Human-robot Interaction

Adaptive Impedance Decentralized Control of Modular Robot Manipulators for Physical Human-robot Interaction

Modular robotic manipulator and ground assembly system for on-orbit assembly of space telescopes

In the field of astronomical observation, large space telescopes are key tools for deep space exploration. However, the aperture of space telescopes is limited by the ability to fabricate large monolithic mirrors or lenses and by severe restrictions on the overall volume and mass capacity of the launch vehicle. The use of robots for autonomous assembly while in orbit is a promising option for the deployment of large-aperture space telescopes. In this paper, we describe a modular robotic manipulator that offers high precision and flexibility. A space telescope ground assembly system is then designed, the key components of which are a redundant robotic manipulator and a reconfigurable telescope unit. The ground-based assembly of the space telescope is implemented through a combination of trajectory planning, visual perception, and robotic supple control techniques, validating the ability of the robotic manipulator to automatically assemble modular space telescopes. Finally, the developed robotic manipulator is used to conduct ground assembly demonstration experiments, and the modular space telescope is assembled according to the assembly task plan, accumulating technical experience for future space telescope assembly tasks in orbit.

A Novel Decentralized Fixed-time Tracking Control for Modular Robot Manipulators: Theoretical and Experimental Verification

A Novel Decentralized Fixed-time Tracking Control for Modular Robot Manipulators: Theoretical and Experimental Verification

Decentralized fault-tolerant control of modular robot manipulators with actuator saturation: neural adaptive integral terminal sliding mode-based control approach

A novel neural adaptive integral terminal sliding mode control for decentralized fault-tolerant control strategy, including the integral terminal sliding mode surface, the nonlinear disturbance observer, the radial basis neural network and robust controller, is presented in this paper to achieve fault-tolerant control of modular robot manipulators. First, the integral terminal sliding mode is designed for the fault-tolerant controller. Then, to boost the performance of the controlled system, the radial basis neural network and disturbance observer are introduced to approximate the nonlinear terms and disturbances. The reconstructed approximate uncertainty term is applied as compensation. Next, the super-twisting technique is employed to compensate for estimation errors to ensure stability. In addition, for the actuator saturation problem, the radial basis function neural network-based compensation control is proposed. Finally, the stability of the closed-loop robotic system is demonstrated based on Lyapunov theory. Computer simulations verified the efficiency and advantages of the presented approach.

Cooperative Game-Based Approximate Optimal Control of Modular Robot Manipulators for Human-Robot Collaboration.

Major challenges of controlling human-robot collaboration (HRC)-oriented modular robot manipulators (MRMs) include the estimation of human motion intention while cooperating with a robot and performance optimization. This article proposes a cooperative game-based approximate optimal control method of MRMs for HRC tasks. A harmonic drive compliance model-based human motion intention estimation method is developed using robot position measurements only, which forms the basis of the MRM dynamic model. Based on the cooperative differential game strategy, the optimal control problem of HRC-oriented MRM systems is transformed into a cooperative game problem of multiple subsystems. By taking advantage of the adaptive dynamic programming (ADP) algorithm, a joint cost function identifier is developed via the critic neural networks, which is implemented for solving the parametric Hamilton-Jacobi-Bellman (HJB) equation and Pareto optimal solutions. The trajectory tracking error under the HRC task of the closed-loop MRM system is proved to be ultimately uniformly bounded (UUB) by the Lyapunov theory. Finally, experiment results are presented, which reveal the advantage of the proposed method.

Tansig type NLESO-based integral terminal sliding mode control for modular robot manipulators with uncertain disturbance: Theory and experimental verification

To overcome the mutual constraints between the system chattering and the arrival time in conventional sliding mode control in this paper, a tangent excitation function (tansig) type nonlinear extended state observer (NLESO)-based integral terminal sliding mode control is proposed to solve the trajectory tracking control task of the modular robot manipulators (MRMs) possessing strong coupling and complex time-varying properties. Through the joint torque feedback technology, the MRM system dynamic model is formulated; on this basis, the proposed NLESO based on tansig is utilized to estimate and compensate the system uncertain information, which are consisted of the friction, interconnected dynamic coupling, and external disturbance. The proposed NLESO can simplify the traditional extended state observer design parameters for theoretical analysis and practical application. The proposed tansig-type NLESO-based integral terminal sliding mode control integrated adaptive term exponential reaching law for MRMs is designed to diminish the arrival time when the system state converges to the equilibrium point. The presented algorithm improves the robotic system antidisturbance ability. Furthermore, the Lyapunov theory is utilized to verify stability of the proposed NLESO and closed-loop robotic system. Finally, the availability of the proposed algorithm is demonstrated by experiments.

Event‐triggered adaptive fuzzy optimal control of modular robot manipulators using zero‐sum differential game through value iteration

SummaryIn this article, a novel event‐triggered zero‐sum differential game through value iteration (VI)‐based adaptive fuzzy control is proposed for modular robot manipulators (MRMs) with uncertain disturbance. The subsystem dynamic of MRMs is established by joint torque feedback (JTF) technology. An identifier based on fuzzy logic model (FLM) is established to identify unknown system dynamics. Then, the trajectory tracking control issue of MRMs with uncertain disturbance is transformed into a two‐player zero‐sum differential game. On the basis of event‐triggered mechanism and VI algorithm, the event‐triggered Hamilton–Jacobi–Issacs (HJI) equation is approximately solved through establishment of actor‐critic structure. Different from the traditional adaptive dynamic programming (ADP) algorithm, this scheme is based on adaptive fuzzy optimal control through an actor‐critic structure, which refers to adaptation of both optimal cost function and optimal control policy. Moreover, tracking errors of closed‐loop MRM system are demonstrated to be uniformly ultimately bounded (UUB) under Lyapunov stability theorem. At last, experimental examples and comparisons show the reliability and effectiveness of this method.

Fuzzy logic nonzero-sum game-based distributed approximated optimal control of modular robot manipulators with human-robot collaboration

In this paper, a fuzzy logic nonzero-sum game-based distributed approximated optimal control scheme is presented for modular robot manipulators (MRMs) with human-robot collaboration (HRC) tasks. The MRM dynamic model is formulated by using joint torque feedback (JTF) technique. Based on the differential game strategy, the optimal control problem of HRC task-oriented MRM systems is transformed into a nonzero-sum game problem of multiple robotic subsystems. By taking advantage of the adaptive dynamic programming (ADP) algorithm, the distributed approximate optimal control policy under HRC tasks is developed by a novel fuzzy logic nonzero-sum game manner for solving the coupled Hamilton–Jacobian (HJ) equation. The trajectory tracking error under HRC task of the closed-loop MRM system is proved to be ultimately uniformly bounded (UUB) using the Lyapunov theory. Finally, experiment results have been presented, which demonstrate the advantage and effectiveness of the developed method.

Data-driven Multiplayer Mixed-zero-sum Game Control of Modular Robot Manipulators with Uncertain Disturbance

Data-driven Multiplayer Mixed-zero-sum Game Control of Modular Robot Manipulators with Uncertain Disturbance

Event-triggered optimal interaction control of the MRM system under the complex multi-task constraints: Design and experiments

The main challenges of modular robot manipulators (MRMs) with the environmental constraints include the avoidance of catastrophic collision and the precious contacting in the whole interaction process. Consequently, an event-triggered optimal interaction control method of MRMs under the complex multi-task constraints is presented in this paper. Firstly, on the basis of the joint torque feedback (JTF) technique, the dynamic model of constrained MRM subsystem is established. Secondly, the sensorless-based decentralized nonlinear disturbance observer (NDOB) is proposed to detect and identify the sudden external collision for each joint. Then, the performance index function is improved to achieve the interaction control, which contains the fusion state variable function, the influence of external collision, the known model term, and the estimation of model uncertainties through the radial basis function neural network (RBFNN) identifier. Further, based on event-triggered mechanism and adaptive dynamic programming (ADP) algorithm, the approximate event-triggered optimal interaction control strategy is acquired by the critic neural network (NN). Next, the closed-loop MRM system is demonstrated to be uniformly ultimately bounded (UUB) through the Lyapunov stability theorem. Finally, the experiments are achieved effectively for each joint on the platform, such that the feasibility and universality of the proposed interaction control approach are testified by the experimental results.

Event-triggered-based Decentralized Optimal Control of Modular Robot Manipulators Using RNN Identifier

Event-triggered-based Decentralized Optimal Control of Modular Robot Manipulators Using RNN Identifier

Multiplayer zero‐sum games optimal control for modular robot manipulators with interconnected dynamic couplings

SummaryThis article presents a multiplayer zero‐sum game optimal control for modular robot manipulators (MRMs) via adaptive dynamic programming (ADP). The dynamic model of MRMs with interconnected dynamic couplings (IDCs) is formulated. The control law and IDC effect are regarded as players in each module. The optimal tracking control problem of the MRM system can be transformed into a multiplayer zero‐sum game. Base on ADP algorithm, the Hamilton–Jacobi–Issacs (HJI) equation is solved via critic neural network. Then, the optimal control law can be derivated. The closed‐loop robotic system is proved to be asymptotic stable and experiments are conducted to verify the effectiveness of the control method.

Decentralized robust interaction control of modular robot manipulators via harmonic drive compliance model-based human motion intention identification

In this paper, a human motion intention estimation-based decentralized robust interaction control method of modular robot manipulators (MRMs) is proposed under the situation of physical human–robot interaction (pHRI). Different from traditional interaction control scheme that depends on the biological signal and centralized control method, the decentralized robust interaction control is implemented that using only position measurements of each joint module in this investigation. Based on the harmonic drive compliance model, a novel torque-sensorless human motion intention estimation method is developed, which utilizes only the information of local dynamic position measurements. On this basis, the decentralized robust interaction control scheme is presented to achieve high performance of position tracking and ensure the security of interaction to create the ’safety’ interaction environment. The uniformly ultimately bounded (UUB) of the tracking error is proved by the Lyapunov theory. Finally, pHRI experiments confirm the effectiveness and advancement of the proposed method.

Event-triggered-based cooperative game optimal tracking control for modular robot manipulator with constrained input

In this paper, a cooperative game optimal tracking control method based on event-triggered mechanism for constrained input modular robot manipulators (MRMs) system is introduced. According to the joint torque feedback technique, the dynamics model of the constrained input subsystem is established and the global state space equation is derived. The control inputs of n joints in the MRM system with constrained input are taken as n participants of cooperative game, and the tracking control problem of the manipulator system is transformed into the optimal control problem based on the cooperative game. Next, a fusion function containing position and velocity errors is defined to construct the performance index function. In order to improve the control performance and robustness of the manipulator system, part of the known model information is used to devise controller, the model uncertainty is dealt by the neural network (NN) observer, and the optimal compensation control strategy is used to deal with internal disturbance such as sensor measurement error and transmission ripple due to power fluctuations, electromagnetic effects, noise and vibration. Based on the adaptive dynamic programming algorithm and event-triggered mechanism, the optimal tracking control strategy is obtained by approximately solving the event-triggered Hamilton-Jacobi-Bellman equation with the critic NN. The Lyapunov theory proves that trajectory tracking error of MRM system with constrained input is uniformly ultimately bounded. Finally, the experimental results demonstrate the effectiveness of the proposed control method.

Event‐trigger‐based approximate optimal control of modular robot manipulators using zero‐sum game

AbstractAn event‐triggered‐based approximate optimal control is developed for modular robot manipulators (MRMs) using zero‐sum game strategy. By utilizing the joint torque feedback method, robotic systems' dynamic model is formulated and state space equation is derived. According to event‐triggered mechanism and neural network algorithm, the tracking control issue is converted to a zero‐sum game optimal control issue. The optimal control policy and worst disturbance policy are acquired by Hamilton–Jacobi–Isaacs function respectively. The MRM system's tracking error is ultimately uniformly bounded. Finally, experiments demonstrate advantages of the method.