Control Of Space Robot Research Articles

Robust proximity rendezvous and coordinated control of space robots

This paper investigates the robust proximity rendezvous and coordinated control of space robots. A robust rendezvous controller is meticulously devised to guarantee the H∞ performance of space rendezvous mission by addressing the Hamilton–Jacobi–Isaacs inequality. Additionally, a virtual control system is formulated to guarantee that the robust control inputs adhere to saturation constraints. Furthermore, this study introduces a novel fixed-time convergence constraint function aimed at constraining coordinated tracking missions for end-effector. The proposed coordinated controller ensures the achievement of end-effector pose tracking within a predetermined fixed time, while concurrently stabilizing the base attitude and bounding the transient responses of tracking errors within user-defined ranges. Notably, all these accomplishments are demonstrated under conditions of parameter uncertainty and input disturbance. Simulation results validate the effectiveness of the proposed control scheme.

Enhancing Robot End‐Effector Trajectory Tracking Using Virtual Force‐Tracking Impedance Control

This article presents an extended Cartesian space robot control framework that features a virtual force tracking impedance control to enhance the end‐effector trajectory tracking performance. Initially, the concept of a virtual surface is introduced, which is assumed to be at some constant distance from the desired end‐effector trajectory. This virtual surface generates a virtual contact force when interacting with the torque‐controlled robot end‐effector. The interaction is then manipulated using an impedance control model to track a constant desired force. If the robot end‐effector deviates from the desired trajectory, the constant force‐tracking impedance control generates a compliance trajectory that regulates the end‐effector movements, constraining it to the desired trajectory. For robust force tracking, impedance parameters are optimally tuned using a closed‐loop dynamic model incorporating both robot and impedance dynamics. Additionally, super twisting sliding mode control (STSMC) is integrated to overcome uncertainties and the impact of robot dynamics on force‐tracking performance. Experimental validation confirms the theoretical claims of the proposed approach. It demonstrates that force‐tracking impedance control improves the end‐effector trajectory tracking by quickly reacting to the dynamic trajectories compared to position control only and effectively maintains it on the desired trajectories.

Evaluation on Numerical Method of Differential Formula Based on Optimal Control of Space Robot's Attitude and Motion

Evaluation on Numerical Method of Differential Formula Based on Optimal Control of Space Robot's Attitude and Motion

Deep Koopman-operator-based model predictive control for free-floating space robots with disturbance observer

This paper investigates the optimal tracking control problem of free-floating space robots in the presence of non-ideal factors, such as model uncertainties and external disturbances. To address this issue, we first leverage the deep Koopman operator, facilitated with a deep neural network, to establish an offline formulation of a global linearization model for a micro-nano free-floating space robot. Based on the estimated linearization model, we then employ the model predictive control method for online optimization control, achieving a significantly reduced computational burden. Additionally, a decay factor is integrated into the model predictive control optimization objective function to balance the precision of joint angles tracking with the suppression of base satellite attitude disturbance. To further address the modeling inaccuracies and external disturbances, we incorporate a disturbance observer based on a radial basis function neural network for online compensation within the model predictive control framework. This augmentation enhances tracking precision and robustness. Several groups of simulation results are carried out to demonstrate the effectiveness of the proposed method, showing its capability of energy consumption, disturbance rejection and enhanced robustness. These highlight the potential of the proposed method to improve the control performance of free-floating space robots, even in the absence of a precise dynamic model.

Fast fixed-time extended state observer based command filtering backstepping control of free-flying flexible-joint space robots

System parameter perturbations, external disturbances, and input saturation issues seriously affect the on-orbit operation precision and rapid response capabilities of the free-flying flexible-joint space robots (FFSR). To this end, a fixed-time extended state observer (ESO) based non-singular command filtering backstepping controller is proposed. The fast fixed-time ESO is designed to estimate the parameter perturbations and external disturbances. To avoid the “computational explosion” caused by multiple derivatives of the virtual control law in backstepping control, a second-order command filter is employed to obtain the derivative of virtual control laws. Meanwhile, to reduce the filtering errors induced by the command filter, a non-singular fixed-time filtering error compensation algorithm is developed. Furthermore, considering the constraints of control moment gyroscope (CMG) and joint motors, an anti-saturation compensation algorithm is introduced to drive the system out of the saturated region rapidly, thus reducing actuator saturation effects on the control system performance. Theoretical analysis shows the proposed controller can ensure the system tracking error converges to any arbitrarily small neighborhood of the origin within fixed time. Simulation results demonstrate that the designed non-singular fixed-time command filtering backstepping controller exhibits fast convergence speed and high tracking accuracy.

Dynamic Modeling and Improved Nonlinear Model Predictive Control of a Free-Floating Dual-Arm Space Robot

With the increasing demand for space missions, space robots have become the focus of research and attention. As a typical representative, the free-floating dual-arm space robot has the characteristics of multiple degrees of freedom, a floating base, and dynamic coupling between the manipulator and the base, so its modeling and control are very challenging. To address these challenges, a novel dynamic modeling and control method is proposed for a free-floating dual-arm space robot. First, an explicit dynamic model of a free-floating dual-arm space robot is established based on the explicit canonical multi-rigid-body dynamic modeling theory and combined with the concept of a dynamic equivalent manipulator. The establishment process of this model is not only simple and canonical to avoid the definition and calculation of many intermediate variables, but the symbolic result expression of the model also has the characteristics of iteration, which is convenient for computer automatic modeling. Next, aiming at addressing the problem of trajectory tracking and the base attitude stability of a free-floating dual-arm space robot with parameter perturbation and external disturbance, an improved nonlinear model predictive control method introducing the idea of sliding mode variable structure is proposed. Theoretical analysis shows that the proposed controller has better robustness than the traditional nonlinear model predictive controller. Then, an in-orbit service task is designed to verify the effectiveness of the proposed dynamic modeling and control strategy of the free-floating dual-arm space robot. Finally, the dynamic modeling and control methods proposed are discussed and summarized. The proposed methods can not only realize the tracking of the desired trajectory of the arms of the free-floating space robot, but can also realize the stable control of the base of the free-floating space robot. This paper provides new insights into the difficult problems regarding the dynamics and control of free-floating dual-arm space robots.

Attitude Control and Morphology Evolution of Space Robots Using Deep Reinforcement Learning and Gauge Fields on Shape Space

Attitude Control and Morphology Evolution of Space Robots Using Deep Reinforcement Learning and Gauge Fields on Shape Space

Anti-deadzone adaptive fuzzy dynamic surface control for planar space robot with elastic base and flexible links

In order to combat the impact of the dead zone and reduce vibration of the space robot's elastic base and flexible links, the trajectory tracking and vibration suppression of a multi-flexible-link free-floating space robot system are addressed. First, the elastic connection between the base and the link is considered as a linear spring. Then the assumed mode approach is used to derive the dynamic model of the flexible system. Secondly, a slow subsystem characterizing the rigid motion and a fast subsystem relating to vibration of the elastic base and multiple flexible links are generated utilizing two-time scale hypotheses of singular perturbation. For the slow subsystem with a dead zone in joint input torque, a dynamic surface control method with adaptive fuzzy approximator is designed. Dynamic surface control scheme is adopted to avoid calculation expansion and to simplify calculation. The fuzzy logic function is applied to approximate uncertain terms of the dynamic equation including the dead zone errors. For the fast subsystem, an optimal linear quadratic regulator controller is used to suppress the vibration of the multiple flexible links and elastic base, ensuring the stability and tracking accuracy of the system. Lastly, the simulation results verify the effectiveness of the proposed control strategy.

Trajectory optimization and tracking control of free-flying space robots for capturing non-cooperative tumbling objects

This paper investigates the problem of capturing non-cooperative tumbling objects by free-flying space robots. To solve the two challenges of task constraints and base-manipulator coupling, a pseudospectral method based trajectory optimization and a reinforcement learning based tracking control are proposed for the free-flying space robots. Multiple constrains, including dynamics, field of view and obstacle avoidance, are considered in trajectory optimization. The adaptive segmented Radau pseudospectral method is used to discretize the energy-optimal trajectory problem into a nonlinear programming problem. By adaptively dividing the global time interval into multiple subintervals, higher-order interpolation polynomials are avoided. A reinforcement learning based parameter tuning method is proposed for the base controller to suppress the reaction torque caused by the manipulator. Numerical simulations and experiments on air-bearing testbed verify the effectiveness of our methods in terms of planning efficiency and tracking precision.

Robust Adaptive Learning Control of Space Robot for Target Capturing Using Neural Network.

This article investigates the robust adaptive learning control for space robots with target capturing. Based on the momentum conservation theory, the impact dynamics is constructed to derive the relationship of generalized velocity in the pre-impact and post-impact phase. Considering the nonlinear dynamics with contact impact, the robust control using nonsingular terminal sliding mode (NTSM) and fast NTSM is designed to achieve the fast realization of the desired states. Furthermore, for the unknown dynamics of the combination system after capturing a target, the adaptive learning control is developed based on neural network and disturbance observer. Through the serial-parallel estimation model, the prediction error is constructed for the update of adaptive law. The system signals involved in the Lyapunov function are proved to be bounded and the sliding mode surface converges in finite time. Simulation studies present the desired tracking and learning performance.

Adaptive detumbling control of dual-arm space robot after capturing non-cooperative target

This research focuses on the detumbling planning and control method for the dual-arm space robot post-capturing a non-cooperative tumbling target, which takes the uncertainty of target’s inertial parameters and generalized input constraints of robotic system into consideration. Firstly, based on the concept of task compatibility, an efficient detumbling strategy without optimization algorithm is proposed, where the target’s desired acceleration is in the opposite direction of its velocity with the magnitude determined by scaling factor. Next, a compliance control scheme is designed to track the desired trajectory and the desired contact forces by establishing impedance control for the target and end-effectors. Finally, considering the target’s dynamic uncertainties, an adaptive controller is included to compensate the effects of uncertainty and ensure stable detumbling. The simulation results are presented for detumbling a target with inertial parameter uncertainties using a 7 degree-of-freedom dual-arm space robot, which demonstrate the effectiveness of the proposed method.

Adaptive Control of Mini Space Robot Based on Linear Separation of Inertial Parameters

Space robots exhibit a strong dynamic coupling between the manipulator and the base spacecraft, with this phenomenon being particularly pronounced in mini space robots. The uncertainty surrounding the inertial parameters of space robots often renders dynamics-based controllers ineffective, and identifying these parameters in an on-orbit environment poses significant challenges. In this paper, we propose an adaptive controller for dynamic approximation that is specifically designed for mini space robots. This controller employs a linear separation of inertial parameters and utilizes recursive least-squares and Lyapunov methods to update the inertial parameter vectors. Simulation results validate the effectiveness of this adaptive controller in enabling mini space robots to accurately track predefined trajectories. Additionally, we compare the effects of the two parameter update methods on the controller stability under varying prior inertial parameter errors. The proposed inertial parameter separation adaptive controller significantly approximates the dynamics of mini space robots and facilitates precise on-orbit control, thereby offering considerable potential for advancing space exploration, satellite missions, and robotic operations.

A Novel Motor Structure with Extended Particle Swarm Optimization for Space Robot Control.

This paper studies motor structures and optimization methods for space robots, proposing an optimized stepped rotor bearingless switched reluctance motor (BLSRM) to solve the poor self-starting ability and significant torque fluctuation issues in traditional BLSRMs. Firstly, the advantages and disadvantages of the 12/14 hybrid stator pole type BLSRM were analyzed, and a stepped rotor BLSRM structure was designed. Secondly, the particle swarm optimization (PSO) algorithm was improved and combined with finite element analysis for motor structure parameter optimization. Subsequently, a performance analysis of the original and new motors was conducted using finite element analysis software, and the results showed that the stepped rotor BLSRM had an improved self-starting ability and significantly reduced torque fluctuation, verifying the effectiveness of the proposed motor structure and optimization method.

Dynamic Modelling and Continuous Trajectory Tracking Control of Space Robots Based on Lie Group SE(3)

For space robots, it is difficult to track continuous time-varying manifolds on SE(3) by using traditional closed-loop control strategies, which are designed to track the position and the attitude separately. Therefore, the dynamics model should be rebuilt, and the corresponding control strategy should be redesigned. Firstly, the dynamics equations for a space robot in the joint space and workspace are established separately in the framework of Lie group SE(3) and screw theory based on the Lagrange principle. Secondly, based on the proposed feedback form, a PD (proportional derivative) control law of output force on the end-effector is designed, and a closed-loop continuous tracking control strategy is proposed using the force Jacobian matrix and the kinematic model. The simulation results show that the control scheme has good performance when the system state changes gently. Furthermore, a robust sliding mode tracking control scheme is designed. The simulation results show that the proposed robust control law has better accuracy than the PD control law because the system state changes wildly. Finally, a robust fuzzy sliding mode tracking control scheme is designed to deal with the chattering phenomenon. The simulation results show that the proposed robust fuzzy control law can eliminate the chattering well and decrease the joint control torque significantly. The robustness of the proposed robust fuzzy control law is also verified by numerical simulation.

FSTSMC Compliance Control for Dual-Arm Space Robot With SDBD Capture Satellite Operation

Abstract A space robot inevitably impacts a satellite in a capture operation. If its fragile joints are not protected, they may be damaged, resulting in failure of the capture operation. Thus, a spring damper buffer device (SDBD) is added between the joint motors and manipulators to absorb the impact energy, and provide a compliance strategy matched with the SDBD for stable control of the hybrid system. The dynamic modes of the dual-arm space robot open-loop system and target satellite system are established by the Lagrange function before capture. From the momentum theorem, velocity constraints, closed-chain geometric constraints, and Newton's third law, the closed-chain dynamic model of the hybrid system after capture is obtained, and the impact effect and impact force are calculated. For stable control of the hybrid system while limiting joint? impact torque within the safe range, an adaptive fractional-order super-twisting sliding mode compliance control strategy matching the SDBD is proposed. It can effectively improve the fast convergence and trajectory tracking performance of the system whose velocity and acceleration cannot be measured. The stability of the hybrid system is demonstrated by the Lyapunov theorem, and the anti-impact performance of the SDBD and the effectiveness of the compliance strategy are demonstrated through numerical simulation.

Dynamics-based deep learning and fractional-order fixed-time sliding mode control for space robot impedance system

To complete complex tasks with space robots in the impedance environment, sliding mode control (SMC) has been proposed and plays an important role in the past decades. Learning-based control has also met with great success in robotics and has been closely followed in the control of space robots. However, as an interdisciplinary, only a few studies have combined deep learning and traditional controller in the space robot directly. This paper proposes a novel fractional-order fixed-time SMC for impedance control of space robots, taking advantage of a model-based deep learning approach to obtain the dynamics of space robots. The learning results are used to make predictions for the data-based robot dynamics. The fractional-order fixed-time SMC controller is added to that data-based model, and it ensures the finite closed-loop convergence time. Small-dataset training is more reasonable in the situation of space robots, which reduces training time and sampling size. Compared to traditional methods, combining deep learning and fractional-order fixed-time SMC provides better tracking performance and improves closed-loop robustness. It accomplishes the control target in the impedance model using the estimated data-driven parameters. Theoretical analysis proves the closed-loop behavior, and simulation results verify its effectiveness.

Trajectory Tracking Hybrid Control and Vibration Suppression of Free-floating Multi-flexible Space Robot with Limited Input

空间机器人在实际运作中往往会遇到控制系统输入受限的情况，这将严重影响系统的控制品质。考虑输入受限的情况，对漂浮基多柔性空间机器人系统进行研究。通过对系统进行运动学和动力学分析，构建系统的动力学方程。采用奇异摄动方法，将刚柔耦合系统分为慢变子系统和快变子系统，并为各个子系统设计控制方法。最终提出由改进的鲁棒滑模模糊控制方法、速度差值反馈控制法和线性二次最优控制方法组成的混合控制方法。该控制方法能够主动减小空间机器人完成期望运动所需的力矩，使系统适应输入受限的工作条件；同时能够补偿系统的不确定参数和外部干扰，最终实现系统运动的精确控制和振动的主动抑制。仿真对比实验证明了在输入受限的情况下，所提出的混合控制方法的有效性和强适应性。

A fault-tolerant and robust controller using model predictive path integral control for free-flying space robots.

The use of manipulators in space missions has become popular, as their applications can be extended to various space missions such as on-orbit servicing, assembly, and debris removal. Due to space reachability limitations, such robots must accomplish their tasks in space autonomously and under severe operating conditions such as the occurrence of faults or uncertainties. For robots and manipulators used in space missions, this paper provides a unique, robust control technique based on Model Predictive Path Integral Control (MPPI). The proposed algorithm, named Planner-Estimator MPPI (PE-MPPI), comprises a planner and an estimator. The planner controls a system, while the estimator modifies the system parameters in the case of parameter uncertainties. The performance of the proposed controller is investigated under parameter uncertainties and system component failure in the pre-capture phase of the debris removal mission. Simulation results confirm the superior performance of PE-MPPI against vanilla MPPI.

Neural-Network-Based Terminal Sliding Mode Control of Space Robot Actuated by Control Moment Gyros

This paper studies the trajectory tracking control of a space robot system (SRS) in the presence of the lumped uncertainties with no prior knowledge of their upper bound. Although some related control methods have been proposed, most of them have either not been applied to SRSs or lack rigorous stability proof. Therefore, it is still a challenge to achieve high accuracy and rigorous theoretical proof for tracking control of SRSs. This paper proposes a new integrated neural network- based control scheme for the trajectory tracking of a SRS actuated by control moment gyros (CMGs). A new adaptive non-singular terminal sliding mode (ANTSM) control method is developed based on an improved radial basis function neural network (RBFNN). In the control method, a new weight update law is proposed to learn the upper bound of the lumped uncertainties. With the advantages of RBFNN and ANTSM, the controller has high control accuracy, fast learning speed and finite-time convergence. Different from most on-ground robotic manipulator controllers, a kinematic controller with position and attitude control laws is also designed for the satellite platform to remain stable. The stability of the closed-loop system is proved by the Lyapunov method with a high mathematical standard. Comparative simulation results demonstrate the effectiveness of the proposed control scheme with preferable performance and robustness.

RCMACNN control of flexible space robot with dead-zone compensator and friction observer

In order to improve the trajectory tracking performance of space robots, it is necessary to consider the influence of various working conditions such as dead zone and friction. To deal with them, a compensator is designed to eliminate the effects of the joint torque dead zone and a dual observer is used to estimate the friction state. In order to suppress the vibration of the links, a virtual control force is proposed. Based on this, a hybrid trajectory control based on recurrent cerebellum model articulation controller neural network (RCMACNN) is designed. This method can not only achieve trajectory tracking, but also suppress flexibly vibration, and at the same time eliminate the vibration coupling between various working conditions. Using Lyapunov function, it is proved that the filtering error is ultimately uniformly bounded (UUB). Finally, a planar space robot with two flexible links is applied for simulation, and the results prove the effectiveness of the method.