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1. Anti-Windup Compensation for Nonlinear Dynamic Inversion Rigid Body Attitude Control

   https://doi.org/10.1109/LCSYS.2025.3577574  

   Soltani, Ali; Turner, Matthew C; Richards, Christopher M (June 2025, IEEE Control Systems Letters)

   Manzie, C (Ed.)

   The rigid body attitude stabilization problem with constrained control inputs has been studied by many researchers. However, if perfect eigen-axis rotation in rest to-rest maneuvers is also desirable, the control design problem becomes more challenging and, to the best of the authors’ knowledge, has not yet been addressed. In this letter, an anti-windup compensation approach to this problem is developed. A nonlinear dynamic inversion control is used to obtain satisfactory unconstrained performance and this is supplemented by an anti-windup compensator when constraints are encountered. The compensator provides global L2 performance under reasonable conditions. A highlight of the approach is that the anti-windup compensator can have a nonlinear structure, giving flexibility in the choice of its parameters. Simulation results demonstrate the effectiveness of the proposed scheme as well as the performance improvement achieved using a compensator with state-dependent parameters. 

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2. Consensus-based adaptive sampling and approximation for high-dimensional energy landscapes

   Lyu, Liyao; Lei, Huan (May 2025, arXivorg)

   We present a consensus-based framework that unifies phase space exploration with posterior-residual-based adaptive sampling for surrogate construction in high-dimensional energy landscapes. Unlike standard approximation tasks where sampling points can be freely queried, systems with complex energy landscapes such as molecular dynamics (MD) do not have direct access to arbitrary sampling regions due to the physical constraints and energy barriers; the surrogate construction further relies on the dynamical exploration of phase space, posing a significant numerical challenge. We formulate the problem as a minimax optimization that jointly adapts both the surrogate approximation and residual-enhanced sampling. The construction of free energy surfaces (FESs) for high-dimensional collective variables (CVs) of MD systems is used as a motivating example to illustrate the essential idea. Specifically, the maximization step establishes a stochastic interacting particle system to impose adaptive sampling through both exploitation of a Laplace approximation of the max-residual region and exploration of uncharted phase space via temperature control. The minimization step updates the FES surrogate with the new sample set. Numerical results demonstrate the effectiveness of the present approach for biomolecular systems with up to 30 CVs. While we focus on the FES construction, the developed framework is general for efficient surrogate construction for complex systems with high-dimensional energy landscapes. 

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3. Finite-Horizon Separation-Based Covariance Control for Discrete-Time Stochastic Linear Systems

   https://doi.org/10.1109/CDC.2018.8619542  

   Bakolas, Efstathios (December 2018, 2018 IEEE Conference on Decision and Control (CDC))

   In this paper, we address a finite-horizon stochastic optimal control problem with covariance assignment and input energy constraints for discrete-time stochastic linear systems with partial state information. In our approach, we consider separation-based control policies that correspond to sequences of control laws that are affine functions of either the complete history of the output estimation errors, that is, the differences between the actual output measurements and their corresponding estimated outputs produced by a discrete-time Kalman filter, or a truncation of the same history. This particular feedback parametrization allows us to associate the stochastic optimal control problem with a tractable semidefinite (convex) program. We argue that the proposed procedure for the reduction of the stochastic optimal control problem to a convex program has significant advantages in terms of improved scalability and tractability over the approaches proposed in the relevant literature. 

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4. SEPARATING THE WORLD AND EGO MODELS FOR SELF-DRIVING

   Alfredo Canziani, Nicolas Carion (April 2022, ICLR 2022 workshop on Generalizable Policy Learning in the Physical World. 2022.)

   Training self-driving systems to be robust to the long-tail of driving scenarios is a critical problem. Model-based approaches leverage simulation to emulate a wide range of scenarios without putting users at risk in the real world. One promising path to faithful simulation is to train a forward model of the world to predict the future states of both the environment and the ego-vehicle given past states and a sequence of actions. In this paper, we argue that it is beneficial to model the state of the ego-vehicle, which often has simple, predictable and deterministic behavior, separately from the rest of the environment, which is much more complex and highly multimodal. We propose to model the ego-vehicle using a simple and differentiable kinematic model, while training a stochastic convolutional forward model on raster representations of the state to predict the behavior of the rest of the environment. We explore several configurations of such decoupled models, and evaluate their performance both with Model Predictive Control (MPC) and direct policy learning. We test our methods on the task of highway driving and demonstrate lower crash rates and better stability. The code is available at https://github.com/vladisai/pytorch-PPUU/tree/ICLR2022. 

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5. Reflected Schrödinger Bridge: Density Control with Path Constraints

   https://doi.org/10.23919/ACC50511.2021.9482813  

   Caluya, Kenneth F.; Halder, Abhishek (January 2021, 2021 American Control Conference (ACC))

   How to steer a given joint state probability density function to another over finite horizon subject to a controlled stochastic dynamics with hard state (sample path) constraints? In applications, state constraints may encode safety requirements such as obstacle avoidance. In this paper, we perform the feedback synthesis for minimum control effort density steering (a.k.a. Schrödinger bridge) problem subject to state constraints. We extend the theory of Schrödinger bridges to account the reflecting boundary conditions for the sample paths, and provide a computational framework building on our previous work on proximal recursions, to solve the same. 

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