Figure - available from: International Journal of Aerospace Engineering
This content is subject to copyright. Terms and conditions apply.
Source publication
In this study, a model predictive control (MPC) method is developed for a servicer spacecraft autonomously approaching a tumbling failed spacecraft at an ultraclose range. Flight safety and collision avoidance are basic requirements during the approach. Two types of a failed spacecraft with complex configurations are considered, and a double-ellips...
Similar publications
The goal of this thesis is to design a learning model predictive controller (LMPC) that allows multiple agents to race competitively on a predefined race track in real-time. This thesis addresses two major shortcomings in the already existing single-agent formulation. Previously, the agent determines a locally optimal trajectory but does not explor...
As a flexible, autonomous and intelligent motion platform, underactuated surface vessels (USVs) are expected to be an ideal means of transport in dangerous and complex marine environments. The success and efficiency of maritime missions performed by USVs depend on their ability to accurately follow paths and remain robust against wind and wave dist...
It can be difficult to autonomously produce driver behavior so that it appears natural to other traffic participants. Through Inverse Reinforcement Learning (IRL), we can automate this process by learning the underlying reward function from human demonstrations. We propose a new IRL algorithm that learns a goal-conditioned spatiotemporal reward fun...
Various lane-keeping assist systems have been studied. To avoid collisions, automated lane crossing is sometimes required. In this paper, we propose model predictive steering control with flexible lane crossing for obstacle avoidance. In the proposed method, collision avoidance is guaranteed by hard constraints, and inappropriate lane boundary cros...
This paper presents a three-layered hybrid collision avoidance (COLAV) system for autonomous surface vehicle, compliant with rules 8 and 13-17 of the International Regulations for Preventing Collisions at Sea (COLREGs). The COLAV system consists of a high-level planner producing an energy-optimized trajectory, a model predictive control based mid-l...
Citations
... A mixed integer linear programming strategy to solve the relative motion guidance and control problems is also used in references [97,131,132], where integer optimisation variable may arise from avoidance constraints or variable-horizon for the transfer. Sequential convexifications techniques are extensively used in the resulting optimisation problems for rendezvous and proximity operations in order to achieve faster and more robust solutions [99,[133][134][135][136]. Guffanti and D'Amico [137] used a SCP method to solve the relative transfer problem formulating the problem in function of integration constants and including passive abort safety constraints to the nominal trajectory. ...
... But designing predictive control for nonlinear systems will come with some sophistication. In this field, an MPC approach is being developed by 24 for an autonomous servicer spacecraft approaching a tumbling target at an ultra-close distance. The target was considered to behave in a deterministic manner. ...
... According to the simulation results shown in 14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59 www.nature.com/scientificreports/ www.nature.com/scientificreports/ ...
Rendezvous is one of the fundamental phases of on-orbit servicing (OOS) missions. Since it requires high accuracy and safety, modeling is an indispensable part. Therefore, this article puts forward an approach for boosting the exactitude of the final proximity phase of a servicer spacecraft using precise modeling. Unlike other similar works that solely use linear models to design controllers, this paper employs a fully nonlinear model and considers most possible uncertainties and disturbances. In this regard, first a complete nonlinear relative pose (i.e., concurrent position attitude) motion dynamic is developed, which includes (1) the role of the reaction wheels and (2) the major environmental force and torque model. Second, taking the thruster's adverse torque into account, two sliding mode-based control techniques with different nonlinear sliding surfaces are designed. Moreover, the Lyapunov stability criterion is used to handle high nonlinearity effects, control input saturation, actuator misalignment, external disturbance torque/force, measurement error, uncertainties of both inertia parameters, and control inputs. Even the PWPF modulator of the thrusters has been considered to make the outcomes more realistic. Finally, three different scenarios are comprehensively simulated to illustrate the feasibility and efficiency of the designed scheme. The results prove that the proposed closed-form controller is more executable to implement than other existing approaches.
... Model Predictive Control has been used for the 3 degreeof-freedom ARPOD problem while considering various constraints, such as collision avoidance and line-of-sight constraints (Petersen et al., 2014;Li et al., 2017;Wang et al., 2018;Richards and How, 2003;Di Cairano et al., 2012). Considering only the translational dynamics can be convenient because, under mild assumptions, the dynamics are linear. ...
This paper presents a time-constrained model predictive control strategy for the 6 degree-of-freedom (6DOF) autonomous rendezvous and docking problem between a controllable "deputy" spacecraft and an uncontrollable "chief" spacecraft. The control strategy accounts for computational time constraints due to limited onboard processing speed. The translational dynamics model is derived from the Clohessy-Wiltshire equations and the angular dynamics are modeled on gas jet actuation about the deputy's center of mass. Simulation results are shown to achieve the docking configuration under computational time constraints by limiting the number of allowed algorithm iterations when computing each input. Specifically, we show that upwards of 90% of computations can be eliminated from a model predictive control implementation without significantly harming control performance.
... MPC, where the optimisation problem is quadratic, Quadratic Programming Model Predictive Control (QPMPC), is the most traditional approach, and efficient solvers for real time application exist [5]. The use of MPC has already been explored as a feasible means of autonomous rendezvous operations [6,7,8,9], and circumnavigation and powered descent on small bodies [10]. Whereas the QPMPC is superior in constraint satisfaction when the prediction model is accurate, the scenario of descent and landing on small bodies is not an environment efficiently captured by a linear model, increasing the risks of constraint violations. ...
... To solve the GC problem for descent and landing on a small body, a robust tube-based MPC is considered to solve the nominal system (6). This relies on successively solving the following finite horizon quadratic cost problem at each discrete time k: ...
In the pursuit of increasing autonomy in descent and landing missions onto small bodies, a robust, autonomous method for powered soft landing has been proposed and validated on the asteroid Eros. With a Quadratic Model Predictive Control as a baseline, robustness has been guaranteed through the use of robust invariant tubes. Seeking on-board and real-time performance on space graded hardware, the tube robust model predictive control is a tradeoff between the computationally heavy stochastic approaches, and the non-robust traditional methods. With the construction of a convex polytopic constrained state space, the tube robust model predictive control has been shown to achieve highly accurate soft-landing with only a linear prediction model in the scenario of extreme uncertainty on the gravitational field of the asteroid Eros.
This study proposes a parametric formation control method for the cooperative observation of the China Space Station (CSS) using multiple nanosatellites. First, a simplified geometrical model of the CSS is constructed using fundamental solids, such as the capsule body and cuboid. Second, the spacecraft formation configuration for the observation mission is characterized by a three-dimensional (3D) Lissajous curve using related design parameters under the full-coverage observation requirements of specific parts, such as the CSS connecting section and collision avoidance constraints. Third, a double-layer control law is designed for each nanosatellite, in which the upper layer is a distributed observer for recognizing the target formation configuration parameters, and the lower layer is a trajectory-tracking controller to make the nanosatellite converge to its temporary target position calculated from the upper layer’s outputs. The closed-loop control stability is proven under the condition that the communication network topology of the nanosatellite cluster contains a directed spanning tree. Finally, the control method is verified by numerical simulation, where the CSS connecting section is selected as the observation target, and ten small nanosatellites are assumed to perform the cooperative observation mission. The simulation results demonstrate that the double-layer control law is robust to single-point communication failures and suitable for the accompanying missions of large space objects with multiple nanosatellites.
This study investigated model predictive control (MPC) for close-proximity maneuvering of spacecraft. It is essential for a designed MPC to effectively handle collision avoidance between the servicer spacecraft and the client spacecraft, especially while the client is rotating. The rotating motion of the client leads to dynamic changes in the collision avoidance constraints, which increases the difficulty of optimizing the control input in the MPC framework. Therefore, this study presents a method to improve the performance and computational efficiency of MPC for rendezvous and docking with a nonrotating or rotating client. An ellipsoid is adopted to model the client’s keep-out zone (KOZ). Given the spherical KOZ of the servicer, an expanded ellipsoid is introduced to describe the KOZ for the center of mass of the servicer and modeled as a nonlinear constraint. The linearization method for reference points located at the expanded ellipsoid is adopted to convexify the nonlinear constraints. The reference points are adaptively determined according to the positions of the servicer, client, and expanded ellipsoidal KOZ. The resulting hyperplanes are then used to describe the collision avoidance constraints. By utilizing the aforementioned strategies, combined with the calculated reference points, an adaptive convex programming algorithm suitable for real-time implementation of MPC is derived. The performance of the proposed method is demonstrated through numerical simulations.
Space mission design places a premium on cost and operational efficiency. The search for new science and life beyond Earth calls for spacecraft that can deliver scientific payloads to geologically rich yet hazardous landing sites. At the same time, the last four decades of optimization research have put a suite of powerful optimization tools at the fingertips of the controls engineer. As we enter the new decade, optimization theory, algorithms, and software tooling have reached a critical mass to start seeing serious application in space vehicle guidance and control systems. This survey paper provides a detailed overview of recent advances, successes, and promising directions for optimization-based space vehicle control. The considered applications include planetary landing, rendezvous and proximity operations, small body landing, constrained attitude reorientation, endo-atmospheric flight including ascent and reentry, and orbit transfer and injection. The primary focus is on the last ten years of progress, which have seen a veritable rise in the number of applications using three core technologies: lossless convexification, sequential convex programming, and model predictive control. The reader will come away with a well-rounded understanding of the state-of-the-art in each space vehicle control application, and will be well positioned to tackle important current open problems using convex optimization as a core technology.
