Application of predictive control for manipulator mounted on a satellite

Specific conditions of on-orbit environment are taken into account in the design of all devices intended to be used in space. Despite this fact malfunctions of satellites occur and sometimes lead to shortening of the satellite operational lifetime. It is considered to use unmanned servicing satellite, that could perform repairs of other satellites. Such satellites equipped with a manipulator, could be used to capture and remove from orbit large space debris. The critical part of planned missions is the capture manoeuvre. In this paper a concept of the control system for the manipulator mounted on the satellite is presented. This control system is composed of the trajectory planning module and model predictive controller (the latter is responsible for ensuring precise realization of the planned trajectory). Numerical simulations performed for the simplified planar case with a 2 DoF manipulator show that the results obtained with the predictive control are better than the results obtained with adaptive control method

Passivity based nonlinear model predictive control (PNMPC) of multi-robot systems for space applications

In the past 2 decades, there has been increasing interest in autonomous multi-robot systems for space use. They can assemble space structures and provide services for other space assets. The utmost significance lies in the performance, stability, and robustness of these space operations. By considering system dynamics and constraints, the Model Predictive Control (MPC) framework optimizes performance. Unlike other methods, standard MPC can offer greater robustness due to its receding horizon nature. However, current literature on MPC application to space robotics primarily focuses on linear models, which is not suitable for highly non-linear multi-robot systems. Although Nonlinear MPC (NMPC) shows promise for free-floating space manipulators, current NMPC applications are limited to unconstrained non-linear systems and do not guarantee closed-loop stability. This paper introduces a novel approach to NMPC using the concept of passivity to multi-robot systems for space applications. By utilizing a passivity-based state constraint and a terminal storage function, the proposed PNMPC scheme ensures closed-loop stability and a superior performance. Therefore, this approach offers an alternative method to the control Lyapunov function for control of non-linear multi-robot space systems and applications, as stability and passivity exhibit a close relationship. Finally, this paper demonstrates that the benefits of passivity-based concepts and NMPC can be combined into a single NMPC scheme that maintains the advantages of each, including closed-loop stability through passivity and good performance through one-line optimization in NMPC

The Control Toolbox - An Open-Source C++ Library for Robotics, Optimal and Model Predictive Control

We introduce the Control Toolbox (CT), an open-source C++ library for efficient modeling, control, estimation, trajectory optimization and Model Predictive Control. The CT is applicable to a broad class of dynamic systems but features interfaces to modeling tools specifically designed for robotic applications. This paper outlines the general concept of the toolbox, its main building blocks, and highlights selected application examples. The library contains several tools to design and evaluate controllers, model dynamical systems and solve optimal control problems. The CT was designed for intuitive modeling of systems governed by ordinary differential or difference equations. It supports rapid prototyping of cost functions and constraints and provides standard interfaces for different optimal control solvers. To date, we support Single Shooting, the iterative Linear-Quadratic Regulator, Gauss-Newton Multiple Shooting and classical Direct Multiple Shooting. We provide interfaces to general purpose NLP solvers and Riccati-based linear-quadratic optimal control solvers. The CT was designed to solve large-scale optimal control and estimation problems efficiently and allows for online control of dynamic systems. Some of the key features to enable fast run-time performance are full compatibility with Automatic Differentiation, derivative code generation, and multi-threading. Still, the CT is designed as a modular framework whose building blocks can also be used for other control and estimation applications such as inverse dynamics control, extended Kalman filters or kinematic planning

Adaptive-Optimal Control of Spacecraft near Asteroids

Spacecraft dynamics and control in the vicinity of an asteroid is a challenging and exciting problem. Currently, trajectory tracking near asteroid requires extensive knowledge about the asteroid and constant human intervention to successfully plan and execute proximity operation. This work aims to reduce human dependency of these missions from a guidance and controls perspective. In this work, adaptive control and model predictive control are implemented to generating and tracking obstacle avoidance trajectories in asteroid’s vicinity. Specifically, direct adaptive control derived from simple adaptive control is designed with e modification to track user-generated trajectories in the presence of unknown system and sensor noise. This adaptive control methodology assumes no information on the system dynamics, and it is shown to track trajectories successfully in the vicinity of the asteroid. Then a nonlinear model predictive control methodology is implemented to generate obstacle avoidance trajectories with minimal system information namely mass and angular velocity of the asteroid. Ultimately, the adaptive control system is modified to include feed-forward control input from the nonlinear model predictive control. It is shown through simulations that the new control methodology names direct adaptive model predictive control (DAMPC), is able to generate sub-optimal trajectories. A comparative study is done with Asteroid Bennu, Kleopatra and Eros to show the benefits of DAMPC over adaptive control and MPC. A study on effect of noisy measurements and model is also conducted on adaptive control and direct adaptive model predictive control

Dynamics and Control of Spacecraft Rendezvous By Nonlinear Model Predictive Control

This doctoral research investigates the fundamental problems in the dynamics and control of spacecraft rendezvous with a non-cooperative tumbling target. New control schemes based on nonlinear model predictive control method have been developed and validated experimentally by ground-based air-bearing satellite simulators. It is focused on the autonomous rendezvous for a chaser spacecraft to approach the target in the final rendezvous stage. Two challenges have been identified and investigated in this stage: the mathematical modeling of the targets tumbling motion and the constrained control scheme that is solvable in an on-line manner. First, the mathematical description of the tumbling motion of the target spacecraft is proposed for the chaser spacecraft to rendezvous with the target. In the meantime, the practical constraints are formulated to ensure the safety and avoid collision during the final approaching stage. This set of constraints are integrated into the trajectory planning problem as a constrained optimization problem. Second, the nonlinear model predictive control is proposed to generate the feedback control commands by iteratively solving an open-loop discrete-time nonlinear optimal control problem at each sampling instant. The proposed control scheme is validated both theoretically and experimentally by a custom-built spacecraft simulator floating on a high-accuracy granite table. Computer software for electronic hardware for the spacecraft simulator and for the controller is designed and developed in house. The experimental results demonstrate the effectiveness and advantages of the proposed nonlinear model predictive control scheme in a hardware-in-the-loop environment. Furthermore, a preliminary outlook is given for future extension of the spacecraft simulator with consideration of the robotic arms