Tube-based robust model predictive control for spacecraft proximity operations in the presence of persistent disturbance

Mammarella, Martina; Capello, Elisa; Park, H.; Guglieri, Giorgio; Romano, Marcello

doi:10.1016/j.ast.2018.04.009

Cited by 66 publications

(24 citation statements)

References 20 publications

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“…where z k and v k are the discrete-time nominal state and input, respectively. Figure 3 provides a representation of the outer-bounding tube at the k-th time step centered on the nominal trajectory at each i-th step over a N prediction horizon [33]. Two features of this controller are (i) the TRMPC which allows to steer the uncertain trajectories to the nominal one via a classical MPC approach and (ii) the robustness which ensured tightening the constraints with respect to the initial ones in Eq.…”

Section: Tube-based Robust Model Predictive Controlmentioning

confidence: 99%

“…Q ∈  nÂn , Q≻0, and R ∈  mÂm , and R≻0 are diagonal positive definite matrices. As proposed in [33], solving the following LMI system allows to obtain the feedback gain matrix K:…”

Section: Tube-based Robust Model Predictive Controlmentioning

confidence: 99%

“…represents a suitable constraint set for the nominal state z i|k in order to obtain an inner approximation  of, where ¼ ⊖S K ∞ ðÞ . To evaluate Φ ∞ , it is possible to compute an upper bound of this set solving a simple linear programming [33]. We should define Φ ∞ ≤ 1 À β ðÞ À1 Φ T , with β ∈ 0, 1 ðÞ , where…”

Section: Tube-based Robust Model Predictive Controlmentioning

confidence: 99%

“…starting from the initial control input constraint set  and being ¼ ⊖KS K ∞ ðÞ . More details can be found in [12,33].…”

Section: Tube-based Robust Model Predictive Controlmentioning

confidence: 99%

See 3 more Smart Citations

Precise Attitude Control Techniques: Performance Analysis From Classical to Variable Structure Control

Capello¹,

Dentis²

2020

Advances in Spacecraft Attitude Control

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Small satellites have begun to play an important role in space research, especially about new technology development and attitude control. The main objective of this research is the design of a robust flight software, in which the key feature is suitably designed control laws to guarantee the robustness to uncertainties and external disturbances. To accomplish the desired mission task and to design the robust software, a classical Proportional Integrative Derivative (PID) method and two robust control system technologies are provided, focusing on applications related to small satellites and on the real-time implementability. Starting from PID approach, simulations are performed to prove the effectiveness of the proposed control systems in different scenarios and in terms of pointing stability and accuracy, including uncertainties, measurement errors, and hardware constraints. Different control techniques are analyzed: (i) a tube-based robust model predictive control (MPC) and (ii) a variable gain continuous twisting (CT) sliding mode controller. Both controllers are compared with loop shaping PID controller.

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Section: Tube-based Robust Model Predictive Controlmentioning

confidence: 99%

Section: Tube-based Robust Model Predictive Controlmentioning

confidence: 99%

Section: Tube-based Robust Model Predictive Controlmentioning

confidence: 99%

“…starting from the initial control input constraint set  and being ¼ ⊖KS K ∞ ðÞ . More details can be found in [12,33].…”

Section: Tube-based Robust Model Predictive Controlmentioning

confidence: 99%

See 2 more Smart Citations

Precise Attitude Control Techniques: Performance Analysis From Classical to Variable Structure Control

Capello¹,

Dentis²

2020

Advances in Spacecraft Attitude Control

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show abstract

“…A nonlinear model predictive control strategy was developed in [6] for spacecraft proximity missions, where relative velocity constraints were deemed to ensure safety and stability. Then, the tube-based and flatnessbased model predictive controllers were respectively developed in [7] and [8] for spacecraft rendezvous and proximity operations. A robust H ∞ adaptive feedback controller was developed to achieve asymptotic pose tracking of chaser in [9].…”

Section: Introductionmentioning

confidence: 99%

Adaptive control of space proximity missions with constrained relative states, faults and saturation

Sun

Jiang

2020

Acta Astronautica

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This paper studies a relative position and relative orientation control problem of close-range spacecraft proximity missions under control input saturation, actuator faults, relative state constraints, kinematic couplings, parametric uncertainties, and unknown external disturbances. The problem of control input saturation is handled with introducing the outputs of an augmented system into the controller, and relative state constraints are guaranteed by using barrier Lyapunov function in backstepping design. Actuator faults in dynamical model are compensated by element-wise adaptive estimations, while unknown dynamic couplings, parametric uncertainties, and unknown bounded disturbances are compensated by norm-wise adaptive estimations. Based on the developed adaptive nonlinear control strategy, relative motion states uniformly ultimately tend to small adjustable neighborhoods of zero, and if the initial relative states are constrained in the predefined ranges, then relative state constraints will never be violated. Simulation comparison validates the advantages of the control strategy.

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Rendezvous with Model Predictive Control

Botelho

Parreira²,

Rosa³

et al. 2021

Predictive Control for Spacecraft Rendezvous

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Tube-based robust model predictive control for spacecraft proximity operations in the presence of persistent disturbance

Cited by 66 publications

References 20 publications

Precise Attitude Control Techniques: Performance Analysis From Classical to Variable Structure Control

Precise Attitude Control Techniques: Performance Analysis From Classical to Variable Structure Control

Adaptive control of space proximity missions with constrained relative states, faults and saturation

Rendezvous with Model Predictive Control

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