This article is focused on the design of an LMI-based observer for the class of disturbance-affected nonlinear systems. Two novel LMI conditions are derived by deploying a more general form of the matrix multiplier compared to the one used in the literature. The first method is based on the use of the H ∞ criterion, while the second one utilises an ISS notion. Both LMIs are developed by employing the reformulated Lipschitz property, a well-known LPV approach and the new variant of Young inequality. The key element of the proposed LMI conditions is the incorporation of the novel matrix multipliers which allow us to include some additional decision variables as compared to the methods proposed in the literature. These additional variables add extra degrees of freedom, thus enhancing the LMI feasibility. Furthermore, the effectiveness of the proposed methodologies is showcased through a numerical example.

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 an artificial neural network (ANN). First, we develop a framework to identify an ANN 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 problem of fixed-time formation tracking for multi-spacecraft systems without internal collisions is investigated in this paper. A novel adaptive immersion and invariance (I&I)-based control protocol is designed to solve this technical problem, with the goal of driving formation members to accurately realize and maintain the required configuration within the user-given time. The novelty here lies in two things. First and foremost, unlike the asymptotic convergence of the traditional I&I related works, the proposed protocol guarantees the fixed-time stability by integrating the prescribed performance control. Secondly, the event-triggered mechanism is adopted to alleviate the pressure of communication resources between formation members and reduce unnecessary information interaction. Lyapunov stability analysis shows that the proposed protocol can enable the defined implicit manifold to converge to the origin for most initial conditions. Also, benefiting from the prescribed performance techniques, the convergence time eliminates the dependence of designed controller parameters or initial system conditions, relying only on the actual mission requirements. In addition, we adopt a linear extended state observer to deal with the parameter uncertainties and external disturbances, and use the I&I adaption to estimate the observer errors to further improve the system performance. Moreover, a new exponential-type artificial potential function is designed to avoid close proximity between formation members and prevent internal collisions. Finally, numerical simulations are provided to verify the theoretical results.

In this article, we study an unified resilient asymmetric bipartite consensus (URABC) problem for nonlinear multi-agent systems with both cooperative and antagonistic interactions under denial-of-service (DoS) attacks. We first prove that the URABC problem is solved by stabilizing the neighborhood asymmetric bipartite consensus error. Then, we develop a distributed compact form dynamic linearization method to linearize the neighborhood asymmetric bipartite consensus error. By using an attack compensation mechanism to eliminate the adverse effects of DoS attacks and an extended discrete state observer to enhance the robustness against unknown dynamics, we finally propose a distributed resilient model-free adaptive control algorithm to solve the URABC problem. A numerical example validates the proposed results.

This paper investigates the distributed mean square output tracking problem of heterogeneous multi-agent systems with Markovian switching topologies and infinite transmission delays. The main challenge of the concerned problem arises from how to deal with Markovian switching topologies and infinite transmission delays simultaneously. A novel distributed observer is developed based on a newly proposed Lyapunov functional method. Then, a distributed controller based on the distributed observer is developed. It is shown that the stochastic distributed tracking problem is solved in the sense of mean square if the union graph of the underlying Markovian switching topology contains a spanning tree. A distinctive feature of the proposed controller is that the infinite delays are not required to be known. Finally, the effectiveness of the proposed controller is illustrated by two numerical examples.

In this paper, the prescribed-time consensus problem of multi-agent systems with finite control gain is investigated. A novel control Lyapunov function (CLF) framework for prescribed-time stability is developed by using the time space deformation approach. For both leaderless and leader-following prescribed-time consensus, new switching time-varying gain-based protocols are proposed, in which, the infinite time-varying control gain is turned off before the prescribed time and the global finiteness of control gain is thus guaranteed. It is mathematically proved that the agents equipped with the proposed protocols can achieve less conservative prescribed-time consensus in both leaderless cases and leader-following cases, on the basis of the developed CLF framework. The superiority of the proposed prescribed-time protocols in terms of consensus accuracy, control energy consumption, and control peak value is demonstrated through comparison simulations using illustrative examples.

This paper is concerned with the stability analysis of the static output-feedback polynomial fuzzy-model-based (SOF PFMB) control systems through designing a novel membership grade integration (MGI) approach. The nonconvex problems of the SOF PFMB control systems are convexificated into the convex conditions by introducing block diagonal positive-definite Lyapunov matrix and nonsingular transformation matrix. We proposed a new approximated membership functions, i.e. Lagrange Membership Functions (LMFs) method, which can be introduced into the stabilization process to relieve the stability conservativeness results. The LMFs are general representations of piecewise-linear membership functions (PLMFs), which makes the number of stability conditions not limited by the number of sample points. In a fixed subdomain, arbitrary sample points can be employed by the LMFs method and achieve higher approximation capability by increasing more sample points, so that membership grades can be incorporated into the system analysis. Furthermore, a novel MGI approach including the information of premise variables and LMFs are proposed, which can make the stability conditions more relaxed. Finally, a simulation example is given to show the merits of the developed techniques.

This paper proposes an adaptive neural network (NN) optimal control approach for autonomous relative motion control of non-cooperative spacecraft in proximity. The proposed method aims to minimize fuel consumption under various challenges including model uncertainty, state constraints, external disturbances, and input saturation. To account for uncertain parameters of non-cooperative target and external disturbances, we start by designing a NN disturbance observer. Subsequently, a novel optimal control index function is presented. An adaptive NN based on the actor-critic (A-C) framework and backstepping theory is then utilized to approximate the solution of Hamilton-Jacobi–Bellman (HJB) equation and obtain an optimal control law. The Lyapunov framework is leveraged to establish the stability of the closed-loop control system. Finally, numerical simulations are conducted to assess the feasibility and effectiveness of the proposed control scheme in comparison with an existing approach.

Health monitoring is critical for the maintenance and risk management of reinforced concrete (RC) structures. In this paper, a robust adaptive Kalman filter is proposed for an interstory drift estimation problem to show the health condition of RC structures in the case that the statistics or internal dynamics describing the signals and measurements are not known precisely. More precisely, we build an adaptive current Jerk model (ACJM) where the model parameters are updated in each time step to presuppose the statistics characterization of the RC dynamic, while the unknown measurement noise covariance is adapted based on a fixed-lag innovation with respect to measurements. Moreover, a robust adaptive Kalman filter is designed for the modeling mismatch in each time increment by solving a minimax game: one “hostile” player tries to select a worst model far from the proposed ACJM with an exponential decay tolerance, while an optimum filter is designed by minimizing the estimation error according to this worst model. Finally, some simulation and experimental results show the effectiveness of the proposed algorithm.

For the nonlinear parameter-varying (NPV) model of unmanned surface vehicle (USV) with the consideration of the velocities on yaw and surge as well as wave disturbances, a robust H ∞ control method is proposed based on extended homogeneous polynomial Lyapunov function (EHPLF) to regulate heading for the superior performance on the rapidity, accuracy and robustness. Firstly, a NPV model of heading error is established to design a general form of a state feedback controller with a robust H ∞ performance. Secondly, a Lyapunov matrix with full states and varying parameter is constructed to derive the robust H ∞ global exponential stability conditions by Euler’s homogeneity relation for the NPV system, known as the EHPLF stability conditions. Thirdly, since the EHPLF stability conditions consist of a set of nonlinear coupled inequalities that cannot be directly solved by sum of squares (SOS) toolboxes, they are decoupled with matrix transformations to obtain the EHPLF-SOS stability conditions, which is solved for the parameters of the state feedback controller. Finally, the simulation results indicate that EHPLF method exhibits a superior performance on dynamic, steady-state and robustness.

We develop a delay-adaptive controller for a class of first-order hyperbolic partial integro-differential equations (PIDEs) with an unknown input delay. By employing a transport PDE to represent delayed actuator states, the system is transformed into a transport partial differential equation (PDE) with unknown propagation speed cascaded with a PIDE. A parameter update law is designed using a Lyapunov argument and the infinite-dimensional backstepping technique to establish global stability results. Furthermore, the well-posedness of the closed-loop system is analyzed. Finally, the effectiveness of the proposed method was validated through numerical simulations.

In this paper, an event-triggered nearly optimal tracking control method is investigated for a class of uncertain nonlinear systems by integrating adaptive dynamic programming (ADP) and integral sliding mode (ISM) control. By introducing a neural network (NN) adaptive term, the designed ISM-based discontinuous control law is employed to eliminate the influence of the uncertainties and obtain the tracking error system constructed from the sliding mode dynamics, as well as relax the known upper-bounded condition of uncertainties. In order to guarantee the stability of tracking error system and improve the control performance, under the ADP technique, a critic NN is applied to approximate the optimal value function for solving the event-triggered Hamilton-Jacobi-Bellman equation and the event-triggered nearly optimal feedback control is obtained. The feedback control law is updated and transmitted to plant only when events occur, thus both the communication and the computational resources can be saved. Furthermore, the stability of tracking error is proven thanks to Lyapunov’s direct method. Finally, we provide two simulation examples to validate the developed control scheme.

Malicious attack is a potential threat for collision-free and connectivity-preserving formation control. In this paper, a predictor-based collision-free and connectivity-preserving resilient formation control strategy is presented for a class of nonlinear multi-agent systems under sensor deception attacks. The predictor states are designed to replace original states in the control strategy, and a novel attack compensator is constructed to suppress sensor deception attack. Prediction errors, instead of compromised errors, are introduced to update radial basis function neural networks (RBFNNs) weights. To achieve collision avoidance and connectivity preservation, a transformation function in logarithmic form is proposed. To avoid static and dynamic obstacles, an improved artificial potential function (APF) combined with their velocity information is constructed. Furthermore, to solve the local minimum in the combining of transformation function and APF, a virtual force is added to make agents get away. Based on the Lyapunov stability criterion, all closed-loop signals are bounded and all control objectives can be achieved. The simulation of a group of quadrotors has verified the effectiveness of the proposed resilient control strategy.

Under the framework of the step-function method, the stability of a nonlinear fuzzy hybrid control system combining an impulsive controller and a continuous state feedback controller is investigated. Both the two controllers are assumed to be subject to both actuator saturation and time-varying delays, which has received little attention if any, in the existing studies. A new assumption is established enabling the use of generalized sector conditions to tackle the double saturation, and the conservatism of the stability results is remarkably reduced thanks to the improved step-function method. The stability theorem proposed in this paper removes restriction on the time delays of both controllers, which can be also applied to wider scopes of systems, including hybrid control systems with both stabilizing and instabilizing impulses, systems with varying impulsive gain, and systems with Zeno behavior. Numerical simulations of stabilization for different systems by delayed saturated hybrid control have been conducted, which demonstrate the validity of proposed theorems.

The identification of multiple-input multiple-output (MIMO) systems is an important part of designing complex control systems. This paper studies an auxiliary model least squares iterative (AM-LSI) algorithm for MIMO systems. With the expansion of the system scale and limitations of the computer resources, there is an urgent need for an identification algorithm that provides higher computational efficiency. To address this issue, this paper further derives a hierarchical identification model and proposes a new auxiliary model hierarchical least squares iterative (AM-HLSI) algorithm for MIMO systems by applying the hierarchical identification principle. Through the analysis of the computational efficiency, the AM-HLSI algorithm has higher computational efficiency than the AM-LSI algorithm. Additionally, the feasibility of the AM-LSI and AM-HLSI algorithms is validated by a simulation example.

This article researches the trajectory tracking problem for unmanned marine vehicles (UMVs) with disturbances and under denial-of-services (DoS) attacks in the wireless channel. By applying the partial form dynamic linearization algorithm, an equivalent data-driven model of the UMVs with ocean disturbances is firstly established. And the disturbances are estimated by using extended state observer, which improves the immunity of the UMVs to disturbances in the environment, and the robustness of the UMVs systems is better. It is the first time that the DoS attacks are considered under the data model for UMVs, and a novel data-driven adaptive trajectory tracking control framework is constructed. When the proposed equivalent data model suffers from DoS attacks which follows the Bernoulli distribution, an attack predictive compensation mechanism is devised to relieve the influence of DoS attacks. Based on it, the data-driven adaptive trajectory tracking controller is designed such that the error of trajectory tracking is convergent under DoS attacks and external disturbances. Finally, the effectiveness of the proposed data-driven control scheme and the predictive compensation mechanism is validated through the simulations.

In the emergency rescue and disposal of social public emergencies, supply transportation effectively provides a strong supply foundation and realistic conditions. The trajectory tracking control of emergency supplies transportation robot is the key technology to ensure the timeliness of transportation. In this paper, the emergency supplies transportation robot is taken as the research object, based on Koopman operator theory, combined with radial basis function (RBF) neural network disturbance observer and adaptive prediction horizon event-triggered model predictive control (APET-MPC) algorithm to investigate the purely data-driven trajectory tracking control problem of emergency supplies transportation robot when the model parameters and models are unknown. Firstly, the Koopman operator is used to establish a high-dimensional linear model of the robot. Secondly, the RBF neural network disturbance observer is designed to estimate the disturbance during the robot operation and compensate it to the controller. Thirdly, APET-MPC is used to optimize the trajectory tracking control of the emergency supplies transportation robot to reduce computational complexity. Finally, the performance of the proposed trajectory tracking controller is verified by Carsim/ Simulink joint simulation. The simulation results show that the model established by Koopman operator theory can achieve the high accuracy approximation of the robot. Compared with the MPC trajectory tracking controller, the APET-MPC trajectory tracking controller based on RBF neural network disturbance observer (RBF-APET-MPC) improves the tracking accuracy of the robot and reduces the total triggering times of the system by more than 50%.

This article explores recursive algorithms for parameter identification issues of Hammerstein output-error systems. The proposed approach includes the key term separation auxiliary model recursive gradient algorithm, which utilizes the gradient search and the key term separation. To enhance computational efficiency, the system is decomposed into two or three subsystems through the hierarchical identification principle. Based on this, a key term separation auxiliary model two-stage recursive gradient algorithm and a key term separation auxiliary model three-stage recursive gradient algorithm are presented. The simulation results verify the validity of the obtained algorithms.