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Summary: This survey paper studies deterministic control systems that integrate three of the most active research areas during the last years: (1) online learning control systems, (2) distributed control of networked multiagent systems, and (3) hybrid dynamical systems (HDSs). The interest for these types of systems has been motivated mainly by two reasons: First, the development of cheap massive computational power and advanced communication technologies, which allows to carry out large computations in complex networked systems, and second, the recent development of a comprehensive theory for HDSs that allows to integrate continuous‐time dynamical systems and discrete‐time dynamical systems in a unified manner, thus providing a unifying modeling language for complex learning‐based control systems. In this paper, we aim to give a comprehensive survey of the current state of the art in the area of online learning control in multiagent systems, presenting an overview of the different types of problems that can be addressed, as well as the most representative control architectures found in the literature. These control architectures are modeled as HDSs, which include as special subsets continuous‐time dynamical systems and discrete‐time dynamical systems. We highlight the different advantages and limitations of the existing results as well as some interesting potential future directions and open problems. [ABSTRACT FROM AUTHOR]

Summary: In this paper, we present an overview of adaptive control by contrasting model‐based approaches with data‐driven approaches. Indeed, we propose to classify adaptive controllers into two main subfields, namely, model‐based adaptive control and data‐driven adaptive control. In each subfield, we cite monographs, survey papers, and recent research papers published in the last few years. We also include a few simple examples to illustrate some general concepts in each subfield. [ABSTRACT FROM AUTHOR]

Summary: The Special Issue presents results of current research on learning‐based adaptive methods, merging together model‐based and data‐driven adaptive approaches. The special issue contains two main types of contributions. The first type of papers presents new theoretical developments for learning‐based adaptive algorithms, while the second type focuses on challenging practical applications ranging from UAVs, and autonomous vehicles, to heating and ventilation systems. These papers are compiled in a special issue of the journal. To access all of the papers please follow the following link (https://onlinelibrary.wiley.com/toc/10991115/2019/33/2). [ABSTRACT FROM AUTHOR]

In this paper, we present a learning-based adaptive method to solve the problem of robust trajectory tracking for electromagnetic actuators. We merge a nonlinear backstepping controller that ensures bounded input/bounded states stability, with a multi-variable extremum seeking model-free learning algorithm. The learning algorithm is used to estimate online the uncertain parameters of the model, in this sense, we propose a learning-based adaptive controller. We present a proof of stability of this learning-based nonlinear controller when considering uncertainties with linear parametrisation. The efficiency of this approach is shown on a numerical example. [ABSTRACT FROM AUTHOR]

The authors study here the problem of adaptive 'soft‐landing' control for electromagnetic actuators. The soft landing requires accurate control of the actuator's moving element between two desired positions. They propose a non‐linear adaptive controller to solve the problem of robust trajectory tracking for the moving element, when considering model uncertainties with linear parametrisation. The controller is an integral input‐to‐state stability (iISS) backstepping controller, merged with gradient descent estimation filters to estimate model uncertainties with linear parametrisation. They show that it ensures bounded tracking errors for bounded estimation errors. Furthermore, iISS result allows us to represent the bound on tracking error as a decreasing function of the estimation error. They demonstrate the effectiveness of this controller with numerical tests. [ABSTRACT FROM AUTHOR]

Despite the remarkable theoretical accomplishments and successful applications of adaptive control, this field is not mature enough to solve challenging problems where strict performance and robustness guarantees are required. The needs of an approach that explicitly accounts for robust performance and stability specifications is a critical to the design of practical adaptive control systems. Towards this goal, this study extends the robust adaptive controller using multiple models, switching and tuning to multiple input multiple output and non‐linear systems. The use of 'extended superstability', instead of superstability, allows us to establish overall performance guarantees and reduce the conservativeness of the resulting closed‐loop system. The authors show that under the proposed framework, the output and states remain bounded for bounded disturbances, as a direct consequence of the passivation properties of superstability. The effectiveness of the proposed algorithm is demonstrated in numerical simulations of a non‐linear continuous stirred tank reactor. [ABSTRACT FROM AUTHOR]