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Summary: This article proposes an extremum‐seeking control design that achieves finite‐time stability of the optimum of an unknown measured cost function. The finite‐time extremum‐seeking control technique is shown to achieve finite‐time practical stability of the optimum of the cost function. The main characteristic of the proposed extremum seeking control approach is that the corresponding target averaged system achieves finite‐time stability. A simulation study is presented to demonstrate the effectiveness of the approach. [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]

We study in this paper the problem of iterative feedback gains auto-tuning for a class of nonlinear systems. For the class of input-output linearizable nonlinear systems with bounded additive uncertainties, we first design a nominal input-output linearization-based robust controller that ensures global uniform boundedness of the output tracking error dynamics. Then, we complement the robust controller with a model-free multi-parametric extremum seeking control to iteratively auto-tune the feedback gains. We analyze the stability of the whole controller, that is, the robust nonlinear controller combined with the multi-parametric extremum seeking model-free learning algorithm. We use numerical tests to demonstrate the performance of this method on a mechatronics example. Copyright © 2016 John Wiley & Sons, Ltd. [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]

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]

Abstract-In this brief, we propose a control allocation method for a particular class of uncertain over-actuated affine nonlinear systems, with unstable internal dynamics. Dynamic inversion technique is used for the commanded output to track a smooth output reference trajectory. The corresponding controlallocation law has to guarantee the boundedness of the states, including the internal dynamics, and satisfy control constraints. The proposed method is based on a Lyapunov design approach with finite-time convergence to a given invariant set. The derived control allocation is in the form of a dynamic update law which, together with a sliding mode control law, guarantees boundedness of the output tracking error as well as of the internal dynamics. The effectiveness of the control law is tested on a numerical model of the non-minimum phase planar vertical take-off and landing (P VIOL) system. [ABSTRACT FROM AUTHOR]

Abstract-In this paper, we consider the problem of graceful performance degradation, for affine nonlinear systems. The method is an optimization based scheme, that gives a constructive way to reshape online the output reference for the post-fault system, and explicitly take into account the actuators and states saturations. The online output reference reshaping is associated with an online, Model Predictive control (MPC)-based, controller reconfiguration, that forces the post-fault system to track the new output reference. The effect of fault detection and diagnosis (FDD) uncertainties on the online controller reconfiguration stability are studied, to ensure at least boundedness of the closed-loop system's states. The reshaping and reconfiguration schemes are applied to the Caltech ducted fan numerical example, which is described by a non-minimum phase nonlinear model. [ABSTRACT FROM AUTHOR]

In this paper, a modular observer design methodology is formulated for nonlinear systems with partial model knowledge. Our design consists of three phases: (i) an initial robust observer design that enables one to learn the dynamics without allowing the state estimation error to diverge (hence, safe); (ii) a learning phase wherein the unmodeled components are estimated using Gaussian process-based Bayesian optimization; and, (iii) a re-design phase that leverages the learned dynamics to improve convergence rate of the state estimation error. The potential of our proposed learning-based observer is demonstrated on a benchmark nonlinear system. Additionally, certificates of guaranteed estimation performance are provided. [ABSTRACT FROM AUTHOR]