We present a generalization of the scalar gradient extremum seeking (ES) algorithm, which maximizes static maps in the presence of infinite‐dimensional dynamics described by parabolic partial differential equations (PDEs). The PDE dynamics contains reaction‐advection‐diffusion (RAD) like terms. Basically, the effects of the PDE dynamics in the additive dither signals are canceled out using the trajectory

Discrete‐time adaptive control algorithms can be executed directly in embedded code unlike their continuous‐time counterparts, which require discretization. However, their designs predicated on quadratic Lyapunov‐based frameworks are quite intricate due to the resulting complexity in the Lyapunov difference expressions. Therefore, a wide array of available continuous‐time results addressing transient

This article investigates an adaptive fuzzy tracking control problem for a class of nontriangular form systems with asymmetric time‐varying full state constraints. Unknown functions are approximated by the fuzzy logic systems. A domination approach is employed to tackle the nontriangular form structure. Time‐varying asymmetric barrier Lyapunov functions (ABLFs) are adopted to ensure full‐state constraints

This article is concerned with the parameter identification problem of nonlinear dynamic responses for the linear time‐invariant system by means of an impulse excitation signal and discrete observation data. Using the impulse signal as the input, the impulse response experiment is carried out and the dynamical moving sampling is designed to generate the measured data for deriving new identification

A stochastic average gradient (SAG) algorithm is proposed for multirate (MR) finite impulse response (FIR) models with varying time delays in this article. The time delays at each sampling instant are computed through the self‐organizing maps technique, and then the parameters are estimated by using the SAG algorithm. Considering that the SAG algorithm updates the parameters using all the directions

Conventional closed‐form solution to the optimal control problem using optimal control theory is only available under the assumption that there are known system dynamics/models described as differential equations. Without such models, reinforcement learning (RL) as a candidate technique has been successfully applied to iteratively solve the optimal control problem for unknown or varying systems. For

Single trial estimation of event‐related potential (ERP) components is an open research topic in neuroscience. In this article, we have proposed a method to improve the performance of spatiotemporal filtering by decreasing its dependency to prior estimates of ERP components. For this purpose, we have used a mixture of Gaussian kernels instead of a raw prior signal, and the parameters of the Gaussian

This article studies the robust adaptive tracking control problem of nontriangular nonlinear systems that are affected by multiple state delays rather than the input‐delay. Different from the related studies, the considered systems involve input dead‐zone and various uncertainties arising in the control coefficients, structure parameters, time delays, and disturbances. A new adaptive control strategy

Recently proposed adaptive dynamic programming (ADP) tracking controllers assume that the reference trajectory follows time‐invariant exo‐system dynamics—an assumption that does not hold for many applications. In order to overcome this limitation, we propose a new Q‐function that explicitly incorporates a parametrized approximation of the reference trajectory. This allows learning to track a general

This article addresses the passivity analysis and asynchronous feedback passification problems for a class of discrete‐time switched systems with dwell time constraint. By exploiting dwell time‐dependent storage functions, a state‐dependent switching strategy obeying a dwell time constraint is constructed, and the solvability conditions for asynchronous feedback passification are developed in the form

In this article, the analysis and design problems of H∞ filtering for a class of discrete‐time switched singular systems with time‐varying delay under an arbitrary switching signal are investigated. The main attention is focused on the design of a linear mode‐dependent filter guaranteeing the regularity, causality, and asymptotic stability of the resulting filtering error system with a prescribed H∞

This article investigates the sensor attack detection and isolation problems for cyber‐physical systems under attacked situation. To begin with, an overall system is constructed for the network of dynamic system and all the issues are dealt with for the overall system instead of every subsystem. Then, we prove that the preconditions which are made on the original subsystem can be transferred on to

This article studies control and performance enforcement for a class of uncertain dynamical systems that consist of actuated and unactuated portions physically interconnected to each other. The proposed approach stabilizes the overall interconnected system in the presence of unknown physical interconnections as well as system uncertainties. Performance guarantees are enforced, individually, on the

This paper is concerned with distributed data‐driven observer design problem. The existing data‐driven observers rely on a common assumption that all the information about the system, and the calculations based upon this information are centralized. Therefore the resulting algorithms cannot be applied to the distributed systems in which each local observer receives only a part of the output signal

In this study, an adaptive output feedback control with prescribed performance is proposed for unknown pure feedback nonlinear systems with external disturbances and unmeasured states. A novel prescribed performance function is developed and incorporated into an output error transformation to achieve tracking control with prescribed performance. To handle the unknown non‐affine nonlinearities and avoid

In this work, a fuzzy adaptive two‐bits‐triggered control is investigated for the nonlinear uncertain systems with input saturation and output constraint. The considered systems are more widespread. Without sufficient transmission resources, how to resolve the constraint issues while guarantee the control performance is difficult and challenging. Then, hyperbolic tangent function and barrier Lyapunov

This article investigates the issue of adaptive finite‐time tracking control for a category of output‐constrained nonlinear systems in a non‐strict‐feedback form. First, by utilizing the structural characteristics of radial basis function neural networks (RBF NNs), a backstepping design method is extended from strict‐feedback systems to a kind of more general systems, and NNs are employed to approximate

The problem of real‐time frequency estimation of nonstationary multi‐harmonic signals is important in many applications. In this paper, we propose a novel multi‐frequency tracker based on a state‐space representation of the signal with Cartesian filters and the second‐order central divided difference filter (CDDF), which improves the performance of the extended Kalman filter (EKF) by using Stirling's

A class of discrete‐time random processes arising in engineering and econometrics applications consists of a linear state‐space model whose parameters are modulated by the state of a finite‐state Markov chain. Typical filtering approaches are collapsing methods, which approximate filtered distributions by mixtures of Gaussians, each Gaussian corresponding to one possibility of the recent history of

This article focuses on the consensus problem of leader‐following fractional‐order multi‐agent systems (MASs) with general linear and Lipschitz nonlinear dynamics. First, the distributed adaptive protocols for linear and nonlinear fractional‐order MASs are constructed, respectively. We allow the control coupling gains to be time varying for each agent. Moreover, the adaptive modification schemes for

An observer‐based adaptive fuzzy backstepping approach is proposed for nonlinear systems with respect to fractional‐order differential equations, unmatched uncertainties, unmeasured states, and actuator faults. The approximation capability of fuzzy logic system and minimal learning parameter approaches are applied to identify uncertain functions in a simultaneous manner. For estimating the unavailable

In this article, the problem of asynchronous adaptive dynamic output feedback sliding mode control (SMC) for a class of Takagi‐Sugeno (T‐S) fuzzy Markovian jump systems (MJSs) with actuator faults is investigated. The asynchronous dynamic output feedback control strategy is employed, as the nonsynchronization phenomenon of jump modes exists between the plant and the controller. A novel asynchronous

Heterogeneous sensor networks (HSN) find a wide range of applications in the field of military and civilian environments, where sensor nodes are utilized to estimate the position of a target with both dynamics and control input being unknown for the purposes of tracking. In the HSN, nodes are considered active depending upon their ability to sense the target output while the others are taken passive

An electrocardiogram (ECG) signal is a record of the electrical activities of heart muscle and is used clinically to diagnose heart diseases. An ECG signal should be presented as clear as possible to support accurate decisions made by doctors. This article proposes different combinations of combined adaptive algorithms to derive different noise‐cancelling structures to remove (denoise) different kinds

In this article, an adaptive algorithm is proposed for online velocity estimation of the autonomous mobile robots (AMRs) without positioning data received from a Global Positioning System (GPS) module or other means for odometry. Unlike the popular Kalman and particle filters that use the measurements on vectors of global (or local) position and acceleration of a mobile robot, the proposed adaptive

This study outlines the problem of active fault‐tolerant control for delayed discrete‐time switched systems. Using switched proportional‐integral observer and multiple Lyapunov‐Krasovski function, less conservative sufficient conditions are established to design a robust fault estimation (FE) algorithm via linear matrix inequality form. Afterward, a fault‐tolerant performance is realized based on this

This article considers the identification problem of the jump Markov autoregressive exogenous (JMARX) systems with unknown invariant time‐delay under the framework of recursive expectation‐maximization (REM) algorithm. In this article, a recursive Q‐function is formulated for the JMARX systems, based on which the recursive sufficient statistics are obtained. Then, the parameter vectors, variance, transition

This article addresses the combined estimation issues of parameters and states for multivariable systems in the state‐space form disturbed by colored noises. By utilizing the Kalman filtering principle and the coupling identification concept, we derive a Kalman filtering based partially coupled recursive generalized extended least squares (KF‐PC‐RGELS) algorithm to jointly estimate the parameters and

This article investigates the stabilization of chained‐form nonholonomic systems with strong drifts, multidisturbances, and unknown constant parameters. The disturbances include the matched disturbance with bounded variation and the unknown time‐varying unmatched disturbance. A nonlinear disturbance observer is skillfully constructed to evaluate the matched disturbance and a disturbance estimation

In this work, we consider economic model predictive control of nonlinear networked control systems subject to external disturbances and communication delays in both sensor‐to‐controller and controller‐to‐actuator channels. The problem is addressed in the framework of the min‐max model predictive control. First, a delay compensation strategy is proposed to minimize the impact of communication delays

Small faults (some weak faults with a tiny magnitude) are difficult to detect and may cause severe problems leading to degrading the system performance. This paper proposes an approach to estimate, detect, and isolate small faults in uncertain nonlinear systems subjected to model uncertainties, disturbances, and measurement noise. A robust observer is developed to alleviate the lack of full state measurement

The finite‐time tracking control problem with the output‐constraint property of robotic manipulators subjected to system uncertainties is addressed. Specifically, the radial basis function neural network is employed to compensate for system uncertainties. The finite‐time stability theorem is used for the backstepping design process, by which the limit of the settling time is set. A funnel boundary

Taking advantage of an adaptive Taylor series approximator, this research seeks to address a two‐loop robust controller for electrically‐driven differential drive wheeled mobile robots. A fictitious current signal is designed in the outer loop such that the good tracking performance as well as the asymptotic stability of system will be achieved. Also, the error of currents will be minimized by an actual

We consider the problem of optimal feedback control of an unknown, noisy, time‐varying, dynamic system that is initialized repeatedly. Examples include a robotic manipulator which must perform the same motion, such as assisting a human, repeatedly and accelerating cavities in particle accelerators which are turned on for a fraction of a second with given initial conditions and vary slowly due to temperature

In this article, we present a proximity‐based formulation for moving horizon estimation (MHE) of a class of constrained discrete‐time nonlinear systems. The cost function of the proposed estimator includes a convex stage cost as well as a Bregman distance for the a priori estimate. This rather general formulation of the cost function allows for a flexible design of the estimator depending on the setting

In this work, we consider the monitoring of the absorption column of a typical post‐combustion capture plant within a distributed framework. The column is decomposed into a few subsystems, and a distributed moving horizon state estimator network is designed to estimate the state of the entire column. A state predictor is also designed for each subsystem to approximate the future evolution of the subsystem

This paper develops a sampling-based approach to implicit dual control. Implicit dual control methods synthesize stochastic control policies by systematically approximating the stochastic dynamic programming equations of Bellman, in contrast to explicit dual control methods that artificially induce probing into the control law by modifying the cost function to include a term that rewards learning.

This paper presents a novel state estimation approach for linear dynamic systems when measurements are corrupted by outliers. Since outliers can degrade the performance of state estimation, outlier accommodation is critical. The standard approach combines outlier detection utilizing Neyman‐Pearson (NP) type tests with a Kalman filter (KF). This approach ignores all residuals greater than a designer‐specified

In modern engineering systems, reliability and safety can be conferred by efficient automatic monitoring and fault detection algorithms, allowing for the early identification and isolation of incipient faults. In case of large‐scale and complex systems, scalability issues and computational limitations make centralized monitoring and fault detection methods unapplicable. Research is therefore currently

Fast moving horizon state estimation for nonlinear discrete‐time systems affected by disturbances is addressed by means of imperfect optimization at each time instant based on few iterations of the gradient, conjugate gradient, and Newton algorithms. Linear constraints on the state vector are taken into account through a projection on the subspace associated with such constraints. The stability of

This paper deals with state estimation of a spatially distributed system given noisy measurements from pointwise‐in‐time‐and‐space threshold sensors spread over the spatial domain of interest. A maximum a posteriori probability (MAP) approach is undertaken and a moving horizon (MH) approximation of the MAP cost function is adopted. It is proved that, under system linearity and log‐concavity of the