Tube-based robust output feedback model predictive control for autonomous rendezvous and docking with a tumbling target

Dong, Kaikai; Luo, Jianjun; Dang, Zhaohui; Wei, Liwa

doi:10.1016/j.asr.2019.11.014

Cited by 35 publications

(8 citation statements)

References 35 publications

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“…Remark 4. Given (11), in order to ensure the feasibility of the feedback control input (10), the control signal ū(k)must fulfil the following condition:…”

Section: Non-linear Mpc For Tracking and Disturbance Rejectionmentioning

confidence: 99%

“…To address this difficulty, Min-Max MPC [6,7] employs simplifying approximations and uses all probable realizations of disturbance, which, however, leads to computational complexity. The tube-based MPC has been presented in [8][9][10][11][12][13] to overcome this complexity. In this method, the optimal state converges asymptotically to the terminal region by the optimal control law, and the system state remains within a tube around the optimal trajectory by the feedback control law [10].…”

Section: Introductionmentioning

confidence: 99%

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Robust MPC for tracking changing setpoints in discrete‐time non‐linear systems with non‐additive unknown disturbance

Zamani

Bolandi

2022

IET Control Theory & Appl

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This paper develops a novel observer-based robust tracking predictive controller for discrete-time nonlinear affine systems capable of dealing with changing setpoints and nonadditive non-slowly varying unknown disturbance with bounded variations. The existence of disturbance and/or sudden changes in a setpoint may lead to feasibility and stability issues in the stabilizing terminal constraint-based MPC. Since robust tracking MPCs usually consider additive disturbance, the recursive feasibility of these methods may be lost in the presence of non-additive non-slowly varying disturbance. The robust tracking MPC presented here extends the artificial reference-based MPC to deal with both changing setpoints and non-additive non-slowly varying disturbance. The key idea is the addition of tightened input and state constraints as new system constraints. The authors also guarantee the boundedness of disturbance observation error and closed-loop tracking error. In this method, the optimal tracking error converges asymptotically to the terminal region, and the perturbed system tracking error remains in a variable size tube around the optimal tracking error. It is shown that the proposed controller can achieve offset-free tracking in the presence of constant disturbance. The simulation results of the satellite attitude control system are provided to demonstrate the efficiency of the proposed predictive controller.

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“…Remark 4. Given (11), in order to ensure the feasibility of the feedback control input (10), the control signal ū(k)must fulfil the following condition:…”

Section: Non-linear Mpc For Tracking and Disturbance Rejectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust MPC for tracking changing setpoints in discrete‐time non‐linear systems with non‐additive unknown disturbance

Zamani

Bolandi

2022

IET Control Theory & Appl

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“…Although RPO literature tends to focus on the relative chaser-target position using a 3-DoF model, relative attitude control also plays an important role, especially if the target is tumbling (Li et al, 2017;Dong et al, 2020). Thanks to advances in the speed and reliability of optimization solvers as mentioned in Section 2.1, there has been an increasing interest to optimize 6-DoF RPO trajectories with explicit consideration of position-attitude coupling through constraints such as plume impingement and sensor pointing (Ventura et al, 2017;Zhou et al, 2019).…”

Section: Rendezvous and Proximity Operationsmentioning

confidence: 99%

“…(Hartley, 2015) provides a tutorial and a detailed discussion. Among the many different approaches that have been developed to explicitly address robustness, we may count feedback corrections (Baldwin et al, 2013), the extended command governor (Petersen et al, 2014), worst-case analysis (Louembet et al, 2015;Xu et al, 2018), stochastic trajectory optimization (Jewison and Miller, 2018), chance constrained MPC (Gavilan et al, 2012;Zhu et al, 2018), sampling-based MPC (Mammarella et al, 2020), tube-based MPC (Mammarella et al, 2018;Dong et al, 2020), and reactive collision avoidance (Scharf et al, 2006). In addition to various uncertainties, anomalous system behavior such as guidance system shutdowns, thruster failures, and loss of sensing, also poses unique challenges in RPO.…”

Section: Rendezvous and Proximity Operationsmentioning

confidence: 99%

Advances in Trajectory Optimization for Space Vehicle Control

Malyuta¹,

Yu²,

Elango³

et al. 2021

Preprint

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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.

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“…In [23], a model predictive control (MPC) strategy is applied to a planar RVD problem with a tumbling target. This is extended to the three-dimensional case in [24,25]. Although such techniques have nowadays proven to be suitable for implementation onboard a spacecraft (see, e.g., [20]), their application to RVD missions still faces remarkable challenges.…”

Section: Introductionmentioning

confidence: 99%

Variable-Horizon Guidance for Autonomous Rendezvous and Docking to a Tumbling Target

Leomanni¹,

Quartullo²,

Bianchini³

et al. 2021

Preprint

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In this paper, the trajectory planning problem for autonomous rendezvous and docking between a controlled spacecraft and a tumbling target is addressed. The use of a variable planning horizon is proposed in order to construct an appropriate maneuver plan, within an optimization-based framework. The involved optimization problem is nonconvex and features nonlinear constraints. The main contribution is to show that such problem can be tackled effectively by solving a finite number of linear programs. To this aim, a specifically conceived horizon search algorithm is employed in combination with a polytopic constraint approximation technique. The resulting guidance scheme provides the ability to identify favourable docking configurations, by exploiting the time-varying nature of the optimization problem endpoint. Simulation results involving the capture of the nonoperational En-viSat spacecraft indicate that the method is able to generate optimal trajectories at a fraction of the computational cost incurred by a state-of-the-art nonlinear solver.

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Tube-based robust output feedback model predictive control for autonomous rendezvous and docking with a tumbling target

Cited by 35 publications

References 35 publications

Robust MPC for tracking changing setpoints in discrete‐time non‐linear systems with non‐additive unknown disturbance

Robust MPC for tracking changing setpoints in discrete‐time non‐linear systems with non‐additive unknown disturbance

Advances in Trajectory Optimization for Space Vehicle Control

Variable-Horizon Guidance for Autonomous Rendezvous and Docking to a Tumbling Target

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