Annual Reviews in Control最新文献

This survey presents recent research on determining control-theoretic properties and designing controllers with rigorous guarantees using semidefinite programming and for nonlinear systems for which no mathematical models but measured trajectories are available. Data-driven control techniques have been developed to circumvent a time-consuming modelling by first principles and because of the increasing availability of data. Recently, this research field has gained increased attention by the application of Willems’ fundamental lemma, which provides a fertile ground for the development of data-driven control schemes with guarantees for linear time-invariant systems. While the fundamental lemma can be generalized to further system classes, there does not exist a comparable data-based system representation for nonlinear systems. At the same time, nonlinear systems constitute the majority of practical systems. Moreover, they include additional challenges such as data-based surrogate models that prevent system analysis and controller design by convex optimization. Therefore, a variety of data-driven control approaches has been developed with different required prior insights into the system to ensure a guaranteed inference. In this survey, we will discuss developments in the context of data-driven control for nonlinear systems. In particular, we will focus on methods based on system representations providing guarantees from finite data, while the analysis and the controller design boil down to convex optimization problems given as semidefinite programming. Thus, these approaches achieve reasonable advances compared to the state-of-the-art system analysis and controller design by models from system identification. Specifically, the paper covers system representations based on extensions of Willems’ fundamental lemma, set membership, kernel techniques, the Koopman operator, and feedback linearization.

Data-driven predictive control (DPC) has gained an increased interest as an alternative to model predictive control in recent years, since it requires less system knowledge for implementation and reliable data is commonly available in smart engineering systems. Several data-driven predictive control algorithms have been developed recently, which largely follow similar approaches, but with specific formulations and tuning parameters. This review aims to provide a structured and accessible guide on linear data-driven predictive control methods and practices for people in both academia and the industry seeking to approach and explore this field. To do so, we first discuss standard methods, such as subspace predictive control (SPC), and data-enabled predictive control (DeePC), but we also include newer hybrid approaches to DPC, such as $\gamma$–data-driven predictive control and generalized data-driven predictive control. For all presented data-driven predictive controllers we provide a detailed analysis regarding the underlying theory, implementation details and design guidelines, including an overview of methods to guarantee closed-loop stability and promising extensions towards handling nonlinear systems. The performance of the reviewed DPC approaches is compared via simulations on two benchmark examples from the literature, allowing us to provide a comprehensive overview of the different techniques in the presence of noisy data.

We give a tutorial exposition of the analogue of the filtering equation for quantum systems focusing on the quantum probabilistic framework and developing the ideas from the classical theory. Quantum covariances and conditional expectations on von Neumann algebras play an essential part in the presentation.

This paper considers the problem of distributed motion- and task-planning of multi-agent and multi-agent-object systems under temporal-logic-based tasks and uncertain dynamics. We focus on manipulator-endowed robotic agents that can interact with their surroundings. We present first continuous control algorithms for multi-agent navigation and cooperative object manipulation that exhibit the following properties. First, they are distributed in the sense that each agent calculates its own control signal from local interaction with the other agents and the environment. Second, they guarantee safety properties in terms of inter-agent collision avoidance and obstacle avoidance. Third, they adapt on-the-fly to dynamic uncertainties and are robust to exogenous disturbances. The aforementioned algorithms allow the abstraction of the underlying system to a finite-state representation. Inspired by formal-verification techniques, we use such a representation to derive plans for the agents that satisfy the given temporal-logic tasks. Various simulation results and hardware experiments verify the efficiency of the proposed algorithms.

This paper presents and analyzes Reinforcement Learning (RL) based approaches to solve spacecraft control problems. Different application fields are considered, e.g., guidance, navigation and control systems for spacecraft landing on celestial bodies, constellation orbital control, and maneuver planning in orbit transfers. It is discussed how RL solutions can address the emerging needs of designing spacecraft with highly autonomous on-board capabilities and implementing controllers (i.e., RL agents) robust to system uncertainties and adaptive to changing environments. For each application field, the RL framework core elements (e.g., the reward function, the RL algorithm and the environment model used for the RL agent training) are discussed with the aim of providing some guidelines in the formulation of spacecraft control problems via a RL framework. At the same time, the adoption of RL in real space projects is also analyzed. Different open points are identified and discussed, e.g., the availability of high-fidelity simulators for the RL agent training and the verification of RL-based solutions. This way, recommendations for future work are proposed with the aim of reducing the technological gap between the solutions proposed by the academic community and the needs/requirements of the space industry.

Network dynamical systems are often characterized by the interlaced evolution of the node and edge dynamics, which are driven by both deterministic and stochastic factors. This manuscript offers a general mathematical model of coevolving network, which associates a state variable to each node and edge in the network, and describes their evolution through coupled stochastic differential equations. We study the emergence of synchronization, be it spontaneous or induced by a pinning control action, and provide sufficient conditions for local and global convergence. We enable the use of the Master Stability Function approach for studying coevolving networks, thereby obtaining conditions for almost sure local exponential convergence, whereas global conditions are derived using a Lyapunov-based approach. The theoretical results are then leveraged to design synchronization and pinning control protocols in two select applications. In the first one, the edge dynamics are tailored to induce spontaneous synchronization, whereas in the second the pinning edges are activated/deactivated and their weights modulated to drive the network towards the pinner’s trajectory in a distributed fashion.