Task space control of free-floating space robots using constrained adaptive RBF-NTSM

It is difficult to obtain a precise mathematical model of free-floating space robot for the uncertain factors, such as current measurement technology and external disturbance. Hence, a suitable solution would be an adaptive robust control method based on neural network is proposed for free-floating space robot. The dynamic model of free-floating space robot is established; a computed torque controller based on exact model is designed, and the controller can guarantee the stability of the system. However, in practice, the mathematical model of the system cannot be accurately obtained. Therefore, a neural network controller is proposed to approximate the unknown model in the system, so that the controller avoids dependence on mathematical models. The adaptive learning laws of weights are designed to realize online real-time adjustment. The adaptive robust controller is designed to suppress the external disturbance and compensate the approximation error and improve the robustness and control precision of the system. The stability of closed-loop system is proved based on Lyapunov theory. Simulations tests verify the effectiveness of the proposed control method and are of great significance to free-floating space robot.

Decentralized adaptive sliding mode control of a space robot actuated by control moment gyroscopes

It is shown that, for a variety of reasons, conventional adaptive control schemes cannot be generally applied to the control of space robots. Therefore, a robust adaptive control scheme that can be successfully applied to the control of space robots in various applications is presented. By using a special Lagrangian formalism, a globally valid dynamic model of space robots is derived and its dynamic properties which are useful for adaptive controller design are investigated. A unified framework is then presented to formulate the various applications, based on which the robust adaptive control scheme is derived. Since the framework is general, the proposed scheme can be actually applied to wider applications. To demonstrate the proposed scheme, the design process is applied to typical applications such as joint space control and Cartesian space control of space robots. >

This work focuses on controlling satellite-mounted robots during the precapture phase, subject to uncertainties in dynamical parameters, joint friction, and external disturbances. We propose a sparse Gaussian process-based stochastic model predictive control (SGP-SMPC) scheme to address these challenges. The SGP-SMPC leverages the SGP technique to estimate uncertainties using previous operational data with low computational costs. This approach provides a more accurate dynamic model to effectively manage uncertainties, thereby enhancing the capabilities of SMPC and ensuring robust control performance. Numerical simulations using a planar free-floating space robot validate and demonstrate the effectiveness of the proposed method. In addition, the reactionless manipulation constraint is integrated into the SGP-SMPC framework to reduce attitude disturbances in the satellite caused by the motion of the robot arm.

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.

This paper investigates the problem of the dynamic balance control of multi-arm free-floating space robot during capturing an active object in close proximity. The position and orientation of space base will be affected during the operation of space manipulator because of the dynamics coupling between the manipulator and space base. This dynamics coupling is unique characteristics of space robot system. Such a disturbance will produce a serious impact between the manipulator hand and the object. To ensure reliable and precise operation, we propose to develop a space robot system consisting of two arms, with one arm (mission arm) for accomplishing the capture mission, and the other one (balance arm) compensating for the disturbance of the base. We present the coordinated control concept for balance of the attitude of the base using the balance arm. The mission arm can move along the given trajectory to approach and capture the target with no considering the disturbance from the coupling of the base. We establish a relationship between the motion of two arm that can realize the zeros reaction to the base. The simulation studies verified the validity and efficiency of the proposed control method.

The motion of a free-floating space robot is characterized by the principle of conservation of angular momentum. It is well known that these angular momentum equations are nonholonomic, i.e., are nonintegrable rate equations. If the base of the free-floating robot is partially actuated, it is difficult to attain trajectories of the joints that result in point-to-point motion of the entire robot system in its configuration space. However, if the drift-less system associated with the angular momentum conservation equations is shown to be differentially flat, point-to-point maneuvers of the free-floating robot in its configuration space can be constructed. However, an open research problem in the current literature is to show the property of differential flatness for a general space robot. The primary contributions of this paper are as follows: (i) study systematically the structure of the nonholonomic rate constraint equations of a free-floating open-chain space robot with arbitrarily oriented joints and two momentum wheels; (ii) establish the design conditions under which the system exhibits differential flatness; (iii) exploit these design conditions for point-to-point trajectory planning and control of the space robot

This paper presents an adaptive trajectory tracking neural network control using radial basis function (RBF) for an n-link robot manipulator with robust compensator to achieve the high-precision position tracking. One of the difficulties in designing a suitable control scheme which can achieve accurate trajectory tracking and good control performance is to guarantee the stability and robustness of control system, due to friction forces, external disturbances error, and parameter variations. To deal with this problem, the RBF network is investigated to the joint position control of an n-link robot manipulator. The RBF network is one approach which has shown a great promise in this sort of problems because of its fast learning algorithm and better approximation capabilities. The adaptive RBF network can effectively improve the control performance against large uncertainty of the system. The adaptive turning laws of network parameters are derived using the back-propagation algorithm and the Lyapunov stability theorem, so that the stability of the entire system and the convergence of the weight adaptation are guaranteed. In this control scheme, a robust compensator plays as an auxiliary controller to guarantee the stability and robustness under various environments such as the mass variation, the external disturbances, and modeling uncertainties. Finally, the simulation and experimental results in comparison with adaptive fuzzy and wavelet network control method are provided to verify the effectiveness of the proposed control methodology.

With the complexity of the dynamic model of free-floating space robots (FFSR), it is difficult to design the control system to capture targets. This paper presents a controller for FFSR to capture targets without the kinematic and dynamic model equations, where the agent learns a closed-loop control policy from state information only. At first, the process of the task is described as the reinforcement learning process without the dynamic models of the space robot. Then, we use the deep deterministic policy algorithm (DDPG) to train the policy for space manipulator motion planning. And we present a skill named pre-training in the training process to further import the learning efficiency. Finally, a 3 degrees of freedom space robot is modeled and simulated to demonstrate the validity of the controller.

<abstract> <bold>Abstract.</bold> For the spraying mobile robot working in greenhouse, due to the inconsistency of drive motors and the rough walking surface, it is easy to track off. For the liquidity of pesticide, the load always changes even the speed jumps. Because of these uncertainties, external disturbances and the difficulty of constructing the system dynamic model, it is hard to implement the trajectory tracking control of the spraying mobile robot steadily, precisely and quickly. In order to solve the problem, a smooth sliding mode trajectory tracking control method is proposed based on the distribute control strategy for each branch. Moreover, its stability is proved using the Lyapunov function. The simulation results show that the proposed method can track the desired trajectories precisely, quickly and steadily under the strong white noise. The chattering phenomenon of the control is restrained compared to the conventional sliding mode control. The trajectory tracking performance is better than that of the fuzzy control. The designed method is easy to realize and doesn't need to construct the precise mathematical model, so, it affords an economical and convenient control method for solving the trajectory tracking problem of the greenhouse spraying mobile robot under various uncertain interferences.

Robust Sliding Mode Trajectory Tracking Controller for a Nonholonomic Spherical Mobile Robot

The paper concerns the problem of trajectory tracking control of a four-wheeled PIAP SCOUT mobile robot with non-steered wheels. For this kind of wheeled robots, it is impossible to find kinematic relationship between robot’s body motion and motion of driven wheels, because of inherent sliding of wheels on the ground during turning. This is an important problem from the point of view of control of the robot. The approach followed in the present work relies on introducing a simple linear controller with feedback of actual yaw rate of robot’s body. The yaw velocity is measured by inexpensive MEMS gyroscope. Experiments were conducted on two kinds of floor typical for office buildings: PVC flooring and carpet flooring. Measurements of motion parameters were possible with INS technique. It was found that the proposed yaw rate controller significantly reduces the angular error of path tracking for 90 degrees turn maneuver.

The question of control and stabilization of flexible space robots is considered. Although, this approach is applicable to space robots of other configurations, for definiteness, a flexible planar two-link robot, mounted on a rigid floating platform in space, is considered. The robotic arm has two revolute joints and its links undergo elastic deformation in the plane of rotation. Based on nonlinear inversion technique, a control law is derived for controlling output variables describing the position and orientation of the platform and the joint angles of the robot. Although, the inverse controller accomplishes reference trajectory tracking, it excites the elastic modes of the arm. Using linear quadratic optimal control theory, a composite stabilizer for stabilization of the rigid and flexible modes and a decoupled flexible mode stabilizer are designed for regulating the end point of the robot to the target point and vibration suppression. Stabilization using only elastic mode velocity feedback is also considered. For large maneuvers, first the inverse controller is active, and the stabilizer is switched for regulation when the motion of the robot lies in the neighborhood of the terminal equilibrium state. Simulation results are presented to show that in the closed-loop system including the inverse controller and the stabilizer, trajectory tracking and stabilization of elastic modes are accomplished.

A control approach is proposed for robust accurate trajectory tracking of manipulators based on the computed torque technique and variable-structure systems (VSS) theory. The computed torque structure of the control scheme, most importantly, provides greater insight to the control of manipulators and serves as a unifying framework for control law development. Looking at the control structure from a computed torque perspective also enables the extension of the technique from joint space to direct end-effector control of both non-redundant and redundant robots in task or operational space. The technique, perhaps, may be further augmented to incorporate force control to achieve hybrid force/position control. The use of sliding modes in the control scheme, in its idealized form, ensures accurate tracking while achieving insensitivity to parameter variations/uncertainties and bounded disturbances. In the development of the control laws, some useful properties of the robot dynamics, viz., positive definiteness of the inertia matrix, and its (dynamics structure) linear form with respect to its parameters, are exploited to make the control laws simple for real-time implementation and reflect the explicit inter-dependence of control non-linearity and structured dynamic model uncertainty. The control algorithms obtained by various researchers for manipulator control using VSS theory may be interpreted from the approach given, which thus to a certain extent unifies the VSS techniques suggested in the literature for robot control.

In this study, two different control logics have been designed for the position control of a robot with five degrees of freedom (DOFs). First, a sliding mode control with a sliding perturbation observer (SMCSPO) has been proposed. The SMCSPO is robust against perturbation, which is the sum of nonlinearities, parametric uncertainties, and external disturbances. To implement the SMCSPO, linear parameters of the system are required; however, these are difficult to identify. Moreover, it is exigent to derive the equation of motion for the multi-DOF robot. Accordingly, an integral sliding mode control (ISMC) has been designed for the position control of this multi-DOF robot. The ISMC controller does not require a mathematical model of the system. The ISMC control input applies a switching gain to compensate for the perturbation and system dynamics. In comparison with the SMCSPO, the ISMC has improved the tracking performance of the system because of its unique characteristics. Both control schemes have been implemented in MATLAB/Simulink. It has been observed that the tracking error of each ISMC joint is lower than that of the SMCSPO.