International Journal of Robust and Nonlinear Control最新文献

Microgrids are a development trend and have attracted a lot of attention worldwide. The control system plays a crucial role in implementing these systems and, due to their complexity, artificial intelligence techniques represent some enabling technologies for their future development and success. In this paper, we propose a novel formulation of an economic model predictive control (economic MPC) applied to a microgrid designed for a faculty building with the inclusion of a predictive model to deal with the energy demand disturbance using a recurrent neural network of the long short-term memory (RNN-LSTM). First, we develop a framework to identify an RNN-LSTM using historical data registered by a smart three-phase power quality analyzer to provide feedforward power demand predictions. Next, we present an economic MPC formulation that includes the prediction model for the disturbance within the optimization problem to be solved by the MPC strategy. We carried out simulations with different scenarios of energy consumption, available resources, and simulation times to highlight the results obtained and analyze the performance of the energy management system. In all cases, we observed the correct operation of the proposed control scheme, complying at all times with the objectives and operational restrictions imposed on the system.

The Kalman filter provides an optimal estimation for a linear system with Gaussian noise. However, when the noises are non-Gaussian in nature, its performance deteriorates rapidly. For non-Gaussian noises, maximum correntropy Kalman filter (MCKF) is developed which provides a more accurate result. In a scenario, where the actual system model differs from nominal consideration, the performance of the MCKF degrades. For such cases, in this article, we have proposed a new robust filtering technique for a linear system which maximizes a cost function defined by exponential of weighted past and present errors weighted with the kernel bandwidth. During filtering, at each time step, the kernel bandwidth is selected by maximizing the correntropy function of error. Further, a convergence condition of the proposed algorithm is derived. Numerical examples are presented to show the usefulness of the proposed filtering technique.

This article investigates the optimal control problem (OCP) for a class of discrete-time nonlinear systems with state constraints. First, to overcome the challenge caused by the constraints, the original constrained OCP is transformed into an unconstrained OCP by utilizing the system transformation technique. Second, a new cost function is designed to alleviate the effect of system transformation on the optimality of the original system. Further, a novel off-policy deterministic approximate dynamic programming (ADP) scheme is developed to obtain a near-optimal solution for the transformed OCP. Compared to existing off-policy deterministic ADP schemes, the developed scheme relaxes the requirement on the learning data and saves computing resources from the perspective of training neural networks. Third, considering approximation errors, we analyze the convergence and stability of the developed ADP scheme. Finally, the developed ADP with the designed cost function is tested in two numerical cases, and simulation results confirm its effectiveness.

This paper investigates the free-will arbitrary time (FWAT) tracking problem for a class of single-input single-output (SISO) strict-feedback nonlinear systems without/with parametric uncertainties. First, we design an FWAT backstepping tracking controller for strict-feedback nonlinear systems without uncertainties in a recursive manner. It is shown that the proposed virtual controllers and their derivatives are continuously differentiable for the backstepping design, and the tracking error converges to zero within the pre-specified settling time independent of design parameters and initial conditions and remains at zero after the pre-specified settling time. Second, we address the adaptive FWAT tracking problem of SISO strict-feedback nonlinear systems with parametric uncertainties in the practical stability sense. The continuously differentiable and nonsingular time-varying adjustment functions for practical FWAT tracking are developed to transform error variables for the recursive backstepping design. Then, the adaptive FWAT backstepping tracking design and stability analysis strategy are established to ensure practical FWAT convergence within the pre-specified settling time independent of design parameters and initial conditions. The designed adaptive virtual and actual controllers are continuous for all $��t�\ge �0�$. Furthermore, a practical adaptive prescribed-time tracking scheme is provided using the proposed practical adaptive FWAT tracking scheme. Finally, the effectiveness of the theoretical approaches is confirmed through a simulation example and an experimental result.

In this article, we propose a new strategy for prescribed-time stabilization of uncertain nonlinear systems via continuous adaptive output feedback. Notably, the systems allow non-parameterized unknown nonlinearities and particularly permit unknown control directions for the first time. The two ingredients lead to the stabilization rather intractable and appeal to new methods and analysis routes. Following a conversion idea, we transform the system in finite-time horizon into a new system with strong time-variants in infinite-time horizon. Integrated with a capable dynamic high gain and an unbounded time-varying gain, a new concise observer which owns control-free and tractable error dynamics is worked out for the new system. We particularly exploit a new set of time-varying scalings, to devise a weakly time-varying entire system whose boundedness amounts to the wanted prescribed-time convergence. From the entire system, the adaptive controller design is conducted. The high gain, which is endowed with tailored dynamics, is integrated with (sufficiently smooth) pseudosign and pseudo-dead-zone functions to largely simplify the design and analysis.

This study discusses consensus tracking control problems for nonlinear stochastic multi-agent systems under DoS attacks and deception attacks. The above attacks are respectively described as receiving duplicate data and receiving false data. In an effort to save network resources, this study proposes an appropriate dynamic event-triggered mechanism. On this basis, a feedback controller for consensus tracking is designed. Then, a new consensus tracking criterion under dual network attacks is proposed. In addition, by selecting a new Lyapunov–Krasovskii functional and using Jesen inequality, sufficient conditions for system mean-square stability are obtained. Finally, the efficacy of this approach is validated through a numerical example.

This paper studies the design of resilient distributed Nash equilibrium (NE) algorithm for second-order multi-agent systems in the presence of Denial-of-Service (DoS) attacks, which prevent information transmission in the network with limited bandwidth and transmission rate. Resilient communication schemes, integrating periodic/event-triggered schemes and a dynamic quantized transmission method, are designed in a distributed NE seeking algorithm to ensure the convergence of agents' strategies to the NE of the game. For the designed two resilient communication schemes, the sufficient conditions for the convergence of the algorithm are established, and the tradeoff between the sampling period and the frequency and duration of DoS attacks is analyzed. Cournot competition with distributed energy resources is used as an example to verify the proposed methods.

In this article, the problem of finite-horizon state estimation is studied for a class of time-varying complex networks with sensor faults. The phenomenon of measurement quantization is considered such that the measurements are quantized probabilistically before transmitted to the state estimator. To deal with the unknown sensor fault, a neural network is introduced to appropriate the sensor fault whose weights are updated based on estimation error and the gradient descent method. Our aim is to design state estimators so that the state estimation errors are finite-time bounded. First, sufficient conditions are established to ensure the existence of the desired state estimators. Then, the gains of the state estimators are derived in terms of the solutions to a set of recursive matrix inequalities. Finally, the usefulness of our estimation approach is confirmed by an illustrative example.

This article studies the state and disturbance simultaneous estimation problem for a class of nonlinear descriptor systems with $��\delta �\text{QC}�$ using a dynamic event-triggered mechanism. $��\delta �\text{QC}�$ refers to incremental quadratic constraints, which can provide a unified description of many types of common nonlinear functions. To reduce the negative impact of measurement outliers on the identification estimation, a stubborn state and disturbance observer co-design scheme is proposed for the first time by embedding dynamic saturation output estimation errors. Meanwhile, a dynamic event-triggered mechanism is introduced to avoid the need for continuously available output information, which can reduce the pressure on communication resources. By constructing a Lyapunov function, existence conditions of the stubborn state and disturbance observer are obtained in the form of a convex optimization problem so that the error estimation maintains an acceptable estimation performance. Finally, simulation examples illustrate the universality and stubbornness of the proposed observer.

This paper focuses on the secure control of positive networked control systems under multi-channel attacks characterized by a Bernoulli distribution. Specifically, the sensor-to-controller channel suffers from false data injection (FDI) attacks, whereas the controller-to-actuator channel suffers from denial of service (DoS) attacks. The objective is to design a static output feedback controller that guarantees the positivity and stochastic stability of the closed-loop system in the presence of DoS and FDI attacks. To conserve communication resources, we employ an event-triggered scheme to reduce the frequency of information transmission. Due to the positivity of the system, the proposed event-triggering mechanism employs the 1-norm form instead of the 2-norm form, which allows that the controller parameter is determined via linear programming rather than LMI technique. Finally, two simulation examples are provided to verify the effectiveness of our methods.