Periodic Model Predictive Control for Tracking Halo Orbits in the Elliptic Restricted Three-Body Problem

A periodic model predictive control (MPC) scheme is proposed for tracking halo orbits. The problem is formulated and solved in the elliptic restricted three-body problem (ER3BP) setting. The reference trajectory to be tracked is designed by using eccentricity continuation techniques. The MPC design exploits the periodicity of the tracking model and guarantees exponential stability of the linearized closed-loop system, through a suitable choice of the terminal set and weight matrices. A sum-of-norms cost function is adopted to promote fuel saving. The proposed control scheme is validated on two simulated missions in the Earth-Moon system, which, respectively, involve station keeping on a halo orbit near the L1 Lagrange point and rendezvous to a halo orbit near the L2 Lagrange point. Results illustrate the advantage of designing the reference trajectory and the periodic control directly in the ER3BP setting versus approximate solutions based on the circular restricted three-body problem (CR3BP)

Adaptive Control Allocation for Over-Actuated Systems with Actuator Saturation

This paper proposes an adaptive control allocation approach for over-actuated systems with actuator saturation. The methodology can tolerate actuator loss of effectiveness without utilizing the control input matrix estimation, eliminating the need for persistence of excitation. Closed loop reference model adaptive controller is used for identifying adaptive parameters, which provides improved performance without introducing undesired oscillations. The modular design of the proposed control allocation method improves the flexibility to develop the outer loop controller and the control allocation strategy separately. The ADMIRE model is used as an over-actuated system, to demonstrate the effectiveness of the proposed method using simulation results. © 201

Fault tolerant control for over-actuated systems: An adaptive correction approach

This paper proposes an adaptive fault tolerant control allocation approach for over-actuated systems. The methodology does not utilize the control input matrix estimation to tolerate actuator faults and, therefore, the proposed control allocation method does not require persistence of excitation. Adaptive control approach with a closed loop reference model is used for identifying control allocation parameters, which provides improved performance without introducing undesired oscillations. Furthermore, a sliding mode controller is used to guarantee the outer loop asymptotic stability. Simulation results are provided, where the ADMIRE model is used as an over-actuated system, to demonstrate the effectiveness of the proposed method. © 2016 American Automatic Control Council (AACC)

Learning reference governor for constrained spacecraft rendezvous and proximity maneuvering

Spacecraft automated rendezvous, proximity maneuvering, and docking (ARPOD) play significant roles in many space missions, including on-orbit servicing and active debris removal. Precise modeling and prediction of spacecraft dynamics can be challenging due to the uncertainties and perturbation forces in the spacecraft operating environment and due to the multilayered structure of its nominal control system. Despite this complication, spacecraft maneuvers need to satisfy required constraints (thrust limits, line-of-sight cone constraints, relative velocity of approach constraints, etc.) to ensure safety and achieve ARPOD objectives. This paper considers an application of a learning-based reference governor (LRG) to spacecraft ARPOD operations to enforce constraints without relying on a dynamic model of the spacecraft during the mission. Similar to the conventional reference governor (RG), the LRG is an add-on supervisor to a closed-loop control system, serving as a prefilter on the command generated by the ARPOD planner. The LRG modifies, if it becomes necessary, the reference command to a constraint-admissible value to enforce specified constraints. The LRG is distinguished, however, by the ability to rely on learning instead of an explicit model of the system; and it guarantees constraints’ satisfaction during and after the learning. In this paper, the LRG is applied to the control of combined translational and rotational motions of a chaser spacecraft, and three case studies with different sets of safety constraints and thruster assumptions are used to demonstrate the benefits of the LRG in ARPOD missions

Reference Governor Strategies for Vehicle Rollover Avoidance

This paper addresses the problem of vehicle rollover avoidance using reference governors (RGs) applied to modify the driver steering input in vehicles with an active steering system. Several RG designs are presented and tested with a detailed nonlinear simulation model. The vehicle dynamics are highly nonlinear for large steering angles, including the conditions where the vehicle approaches a rollover onset, which necessitates RG design changes. Simulation results show that RG designs are effective in avoiding rollover. The results also demonstrate that the controllers are not overly conservative, adjusting the driver steering input only for very high steering angles. IEE

Reference Governor Strategies for Vehicle Rollover Avoidance

This paper addresses the problem of vehicle rollover avoidance using reference governors (RGs) applied to modify the driver steering input in vehicles with an active steering system. Several RG designs are presented and tested with a detailed nonlinear simulation model. The vehicle dynamics are highly nonlinear for large steering angles, including the conditions where the vehicle approaches a rollover onset, which necessitates RG design changes. Simulation results show that RG designs are effective in avoiding rollover. The results also demonstrate that the controllers are not overly conservative, adjusting the driver steering input only for very high steering angles. IEE

Hierarchical reasoning game theory based approach for evaluation and testing of autonomous vehicle control systems

A hierarchical game theoretic decision making framework is exploited to model driver decisions and interactions in traffic. In this paper, we apply this framework to develop a simulator to evaluate various existing autonomous driving algorithms. Specifically, two algorithms, based on Stackelberg policies and decision trees, are quantitatively compared in a traffic scenario where all the human-driven vehicles are modeled using the presented game theoretic approach. © 2016 IEEE