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1. A Computationally Governed Log-domain Interior-point Method for Model Predictive Control

   https://doi.org/10.23919/ACC53348.2022.9867484  

   Leung, Jordan; Permenter, Frank; Kolmanovsky, Ilya V. (June 2022, Proceedings of 2022 American Control Conference (ACC))

   This paper introduces a computationally efficient approach for solving Model Predictive Control (MPC) reference tracking problems with state and control constraints. The approach consists of three key components: First, a log-domain interior-point quadratic programming method that forms the basis of the overall approach; second, a method of warm-starting this optimizer by using the MPC solution from the previous timestep; and third, a computational governor that bounds the suboptimality of the warm-start by altering the reference command provided to the MPC problem. As a result, the closed-loop system is altered in a manner so that MPC solutions can be computed using fewer optimizer iterations per timestep. In a numerical experiment, the computational governor reduces the worst-case computation time of a standard MPC implementation by 90%, while maintaining good closed-loop performance. 

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2. Fast and Guaranteed Safe Controller Synthesis for Nonlinear Vehicle Models

   https://doi.org/10.1007/978-3-030-53288-8\_31  

   Fan, Chuchu; Miller, Kristina; Mitra, Sayan (July 2020, Computer Aided Verification - 32nd International Conference, {CAV} 2020, Los Angeles, CA, USA, July 21-24, 2020, Proceedings, Part {I})

   We address the problem of synthesizing a controller for nonlinear systems with reach-avoid requirements. Our controller consists of a reference controller and a tracking controller which drives the actual trajectory to follow the reference trajectory. We identify a type of reference trajectory such that the tracking error between the actual trajectory of the closed-loop system and the reference trajectory can be bounded. Moreover, such a bound on the tracking error is independent of the reference trajectory. Using such bounds on the tracking error, we propose a method that can find a reference trajectory by solving a satisfiability problem over linear constraints. Our overall algorithm guarantees that the resulting controller can make sure every trajectory from the initial set of the system satisfies the given reach-avoid requirement. We also implement our technique in a tool FACTEST. We show that FACTEST can find controllers for four vehicle models (3–6 dimensional state space and 2–4 dimensional input space) across eight scenarios (with up to 22 obstacles), all with running time at the sub-second range. 

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3. Feasibility Governor for Linear Model Predictive Control

   https://doi.org/10.23919/ACC50511.2021.9483054  

   Skibik, Terrence; Liao-McPherson, Dominic; Cunis, Torbjorn; Kolmanovsky, Ilya; Nicotra, Marco M. (May 2021, Proceedings of 2021 American Control Conference, New Orleans, USA)

   This paper introduces the Feasibility Governor (FG): an add-on unit that enlarges the region of attraction of Model Predictive Control by manipulating the reference to ensure that the underlying optimal control problem remains feasible. The FG is developed for linear systems subject to polyhedral state and input constraints. Offline computations using polyhedral projection algorithms are used to construct the feasibility set. Online implementation relies on the solution of a convex quadratic program that guarantees recursive feasibility. The closed-loop system is shown to satisfy constraints, achieve asymptotic stability, and exhibit zero-offset tracking. 

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4. Integrating Failure Detection and Isolation into a Reference Governor-Based Reconfiguration Strategy for Stuck Actuators

   https://doi.org/10.23919/ACC53348.2022.9867778  

   Li, Huayi; Kolmanovsky, Ilya; Girard, Anouck (June 2022, Proceedings of 2022 American Control Conference, Atlanta, Georgia, USA, June 8-10, 2022.)

   A set-theoretic Failure Model and Effect Management (FMEM) strategy for stuck/jammed actuators in systems with redundant actuators is considered. This strategy uses a reference governor for command tracking while satisfying state and control constraints and, once the failure mode is known, generates a recovery command sequence during mode transitions triggered by actuator failures. In the paper, this FMEM strategy is enhanced with a scheme to detect and isolate failures within a finite time, and to handle unmeasured set-bounded disturbance inputs. A numerical example is reported to illustrate the offline design process and the online operation with the proposed approach. 

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5. Time Shift Governor for Constraint Satisfaction during Low-Thrust Spacecraft Rendezvous in Near Rectilinear Halo Orbits

   https://doi.org/10.1109/CCTA54093.2023.10252878  

   Kim, Taehyeun; Liu, Kaiwen; Kolmanovsky, Ilya; Girard, Anouck (August 2023, IEEE)

   A parameter governor-based control scheme is developed to enforce various constraints, such as the Line of Sight (LoS) cone angle, the thrust limit, and the relative approach velocity during rendezvous missions in a near rectilinear halo orbit (NRHO) in the Earth-Moon system. The parameter governor is an add-on scheme to the nominal closed-loop system, which dynamically adjusts controller parameters in order to enforce the constraints. For the application to the rendezvous mission, we utilize the Time Shift Governor (TSG) which time shifts the target trajectory commanded to the Deputy spacecraft controller. The time shift is gradually reduced to zero so that the virtual target trajectory gradually converges to the Chief spacecraft trajectory as time evolves, and the rendezvous mission can be accomplished. Simulation results are reported that demonstrate the effectiveness of the proposed control scheme. 

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