控制与自动化专业英语

Simultaneous state and parameter estimation is essential for control system design and dynamic modeling of physical systems. This capability provides critical real-time insight into system behavior, supports the discovery of underlying mechanisms, and facilitates adaptive control strategies. Surveyed in this review paper are two classes of state and parameter estimation methods: Kalman Filters and Luenberger Observers. The Kalman Filter framework, including its major variants such as the Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), Cubature Kalman Filter (CKF), and Ensemble Kalman Filter (EnKF), has been widely applied for joint and dual estimation in linear and nonlinear systems under uncertainty. In parallel, Luenberger observers, typically used in deterministic settings, offer alternative approaches through high-gain, sliding mode, and adaptive observer structures. This review focuses on the theoretical foundations, algorithmic developments, and application domains of these methods and provides a comparative analysis of their advantages, limitations, and practical relevance across diverse engineering scenarios.

We introduce a novel algorithm for controlling linear time invariant systems in a tracking problem. The controller is based on a Gaussian Process (GP) whose realizations satisfy a system of linear ordinary differential equations with constant coefficients. Control inputs for tracking are determined by conditioning the prior GP on the setpoints, i.e. control as inference. The resulting Model Predictive Control scheme incorporates pointwise soft constraints by introducing virtual setpoints to the posterior Gaussian process. We show theoretically that our controller satisfies open-loop stability for the optimal control problem by leveraging general results from Bayesian inference and demonstrate this result in a numerical example.

We consider the problem of estimating the state transition matrix of a linear time-invariant (LTI) system, given access to multiple independent trajectories sampled from the system. Several recent papers have conducted a non-asymptotic analysis of this problem, relying crucially on the assumption that the process noise is either Gaussian or sub-Gaussian, i.e., "light-tailed". In sharp contrast, we work under a significantly weaker noise model, assuming nothing more than the existence of the fourth moment of the noise distribution. For this setting, we provide the first set of results demonstrating that one can obtain sample-complexity bounds for linear system identification that are nearly of the same order as under sub-Gaussian noise. To achieve such results, we develop a novel robust system identification algorithm that relies on constructing multiple weakly-concentrated estimators, and then boosting their performance using suitable tools from high-dimensional robust statistics. Interestingly, our analysis reveals how the kurtosis of the noise distribution, a measure of heavy-tailedness, affects the number of trajectories needed to achieve desired estimation error bounds. Finally, we show that our algorithm and analysis technique can be easily extended to account for scenarios where an adversary can arbitrarily corrupt a small fraction of the collected trajectory data. Our work takes the first steps towards building a robust statistical learning theory for control under non-ideal assumptions on the data-generating process.

Rapid changes in the environment and robot parameters pose significant challenges for control systems, particularly when key parameters are not directly measurable. In this paper, we introduce a novel approach using classical Recurrent Neural Network (RNN) controllers to dynamically adapt policies in response to these changes. We propose strategies for data collection and processing that enable the successful training of efficient Gated Recurrent Unit (GRU) nonlinear controllers capable of adapting to changing parameters. We demonstrate this approach using a simulated and a physical cartpole robot. The RNNs are trained through supervised learning on data generated in simulation using Nonlinear Model Predictive Control (NMPC). We vary the cartpole’s angle sensor offset or pole length jointly with pole mass, none of which are directly measurable by the robot. Our results show how the RNN controller adjusts its policy based on past trajectories, leading to control that mimics the NMPC, outperforming Domain Randomization (DR) technique applied to feedforward neural networks. Unlike NMPC, which relies on explicit knowledge of environment parameters, the RNN implicitly estimates these parameters from past trajectories, allowing it to adapt its control policy dynamically. It also outperforms NMPC control performance when the parameters relevant for NMPC are not known.

Diffusion models have become popular for policy learning in robotics due to their ability to capture high-dimensional and multimodal distributions. However, diffusion policies are stochastic and typically trained offline, limiting their ability to handle unseen and dynamic conditions where novel constraints not represented in the training data must be satisfied. To overcome this limitation, we propose diffusion predictive control with constraints (DPCC), an algorithm for diffusion-based control with explicit state and action constraints that can deviate from those in the training data. DPCC incorporates model-based projections into the denoising process of a trained trajectory diffusion model and uses constraint tightening to account for model mismatch. This allows us to generate constraint-satisfying, dynamically feasible, and goal-reaching trajectories for predictive control. We show through simulations of a robot manipulator that DPCC outperforms existing methods in satisfying novel test-time constraints while maintaining performance on the learned control task.

Designing controllers that achieve task objectives while ensuring safety is a key challenge in control systems. This work introduces Opt-ODENet, a Neural ODE framework with a differentiable Quadratic Programming (QP) optimization layer to enforce constraints as hard requirements. Eliminating the reliance on nominal controllers or large datasets, our framework solves the optimal control problem directly using Neural ODEs. Stability and convergence are ensured through Control Lyapunov Functions (CLFs) in the loss function, while Control Barrier Functions (CBFs) embedded in the QP layer enforce real-time safety. By integrating the differentiable QP layer with Neural ODEs, we demonstrate compatibility with the adjoint method for gradient computation, enabling the learning of the CBF class-𝒦 function and control network parameters. Experiments validate its effectiveness in balancing safety and performance.

Cooperative multi-agent reinforcement learning (MARL) has shown significant promise in dynamic control environments, where effective communication and tailored exploration strategies facilitate collaboration. However, ensuring safe exploration remains challenging, as even a single unsafe action from any agent can lead to severe consequences. To mitigate this risk, we introduce Scoop-LSVI, a UCB-based cooperative parallel RL framework that achieves low cumulative regret with minimal communication demands while adhering to safety constraints. This framework enables multiple agents to concurrently solve isolated Markov Decision Processes (MDPs) and share information to enhance learning efficiency. Scoop-LSVI attains a regret of Õ(κ d³⁄² H² √(MK)), where d is the feature dimension, H is the horizon length, M is the number of agents, K is the number of episodes for each agent, and κ represents safety constraints. This result aligns with state-of-the-art findings for unsafe cooperative MARL and also matches the regret bounds of UCB-based single-agent RL algorithms (M = 1), highlighting the potential of Scoop-LSVI to support safe and efficient learning in cooperative MARL applications.