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In this paper, the design problem of Gain‐Scheduled Output‐Feedback (GSOF) controllers using inexact scheduling parameters for morphing aircraft during the wing transition process is addressed. Both the stability of the closed‐loop system and the L2 gain performance can be guaranteed under the controller based on measured (not actual) scheduling parameters. Firstly, the linear parameter‐varying (LPV) model of morphing aircraft is established by Jacobian linearization and the additive uncertainty is introduced into the scheduling parameters. By employing non‐linear transformations, the problem is formulated as the solution to a set of parameter‐dependent linear matrix inequalities (LMI) with a single‐line search parameter. Finally, the robustness of the flight control system to the wing transition process is verified under the condition of both the uncertainty of aerodynamic parameters and of scheduling parameters.

In this paper, an observer-based robust fault-tolerant predictive control (ORFTPC) strategy is proposed for Linear Parameter-Varying (LPV) systems subject to input constraints and sensor failures. The main objective of this work is to establish a real observer based on a virtual observer to be used to estimate both states and sensor failures of the system. The proposed virtual observer is employed to improve the observation precision and reduce the impacts of the sensor faults and the external disturbances in the LPV systems. In addition, a real observer is proposed to overcome the virtual observer margins and to ensure that all states and sensor faults of the system are properly estimated, without the need for any fault isolation modules. The proposed solution demonstrates that, using both observers, a robust fault-tolerant predictive control is established via the Lyapunov function. Moreover, sufficient stability conditions are derived using the Lyapunov approach for the convergence of the proposed robust controller. Furthermore, the proposed approach simultaneously computes the gains of the real observer and the controller from a linear matrix inequality (LMI), which is deduced from the estimation errors. Finally, the performance of the proposed approach is investigated by a simulation example of a quarter-vehicle model, and the simulation results under a sensor fault illustrate the robustness and performance of the proposed method.

This study is concerned with the stability, l2-gain analysis and ℋ∞ control for a class of discrete-time switched linear parameter-varying systems with both mode-dependent average dwell time (MDADT) and asynchronous switching, where ‘asynchronous’ means the switching of controllers has a lag to the switching of system modes. The l2-gain for general switched systems with MDADT in non-linear setting is firstly derived. Based on the obtained results, the problem of asynchronous ℋ∞ control for the studied systems is formulated under the framework of MDADT switching logic, and the conditions for the existence of admissible asynchronous ℋ∞ controllers are deduced in the form of parameterised linear matrix inequalities. A numerical example is provided to verify the effectiveness of the acquired results.

This paper presents the design of a fault detection and diagnosis system for a quadrotor unmanned aerial vehicle under partial or total actuator fault. In order to control the quadrotor, the dynamic system is divided in two subsystems driven by the translational and the rotational dynamics, where the rotational subsystem is based on a linear parameter-varying model. A robust linear parameter-varying observer applied to the rotational subsystem is considered to detect actuator faults, which can occur as total failures (loss of a propeller or a motor) or partial faults (degradation). Furthermore, fault diagnosis is done by analyzing the displacements of the roll and pitch angles. Numerical experiments are carried out in order to illustrate the effectiveness of the proposed methodology.

This paper considers the equilibrium‐free stability and performance analysis of discrete‐time nonlinear systems. Two types of equilibrium‐free notions are considered. Namely, the universal shifted concept, which considers stability and performance w.r.t. all equilibrium points of the system, and the incremental concept, which considers stability and performance between trajectories of the system. This paper shows how universal shifted stability and performance of discrete‐time systems can be analysed by making use of the time‐difference dynamics. Moreover, the existing results are extended for incremental dissipativity for discrete‐time systems based on dissipativity analysis of the differential dynamics to more general state‐dependent storage functions for less conservative results. Finally, it is shown how both these equilibrium‐free notions can be cast as a convex analysis problem by making use of the linear parameter‐varying framework, which is also demonstrated by means of an example. This paper develops a systematic framework for analysing two equilibrium‐free notions of stability and performance for discrete‐time nonlinear systems. It is shown how the resulting analysis problems can be cast as convex optimization problems through the linear parameter‐varying framework.

The paper addresses the problem of accelerating predictive control of non‐linear system models augmented with Gaussian processes (GP‐MPC). Due to the non‐linear and stochastic prediction model, predictive control of GP‐based models requires to solve a stochastic optimization problem. Different model simplification methods have to be applied to reformulate this problem to a deterministic, non‐linear optimization task that can be handled by a numerical solver. As these problems are still complex, especially with exact moment calculations, real‐time implementation of GP‐MPC is extremely challenging. The existing solutions accelerate the computations at the solver level by linearizing the non‐linear optimization problem and applying sequential convexification. In contrast, this paper proposes a novel GP‐MPC solution approach that without linearization formulates a series of surrogate quadratic programs (QP‐s) to iteratively obtain the solution of the original non‐linear optimization problem. The first step is embedding the non‐linear mean‐variance dynamics of the GP‐MPC prediction model in a linear parameter‐varying (LPV) structure and rewriting the constraints in parameter‐varying form. By fixing the scheduling trajectory at a known variation (based on previously computed or initial state‐input trajectories), optimization of the input sequence for the remaining varying linear model reduces to a linearly constrained quadratic program. After solving the QP, the non‐linear prediction model is simulated for the new control input sequence and new scheduling trajectories are updated. The procedure is iterated until the convergence of the scheduling, that is, the solution of the QP converges to the solution of the original non‐linear optimization problem. By designing a reference tracking controller for a 4DOF robot arm, we illustrate that the convergence is remarkably fast and the approach is computationally advantageous compared to current solutions. The proposed method enables the application of GP‐MPC algorithms even with exact moment matching on fast dynamical systems and requires only a QP solver. This paper aims to discuss the approach of constrained modified feedback linearization model predictive control (CMFLMPC) for the spacecraft simulator. The simulation and experimental results demonstrate that the proposed hybrid controller has an insignificant calculative cost and facilitates the spacecraft to perform the regulation manoeuvre with sufficient precision in the presence of external torques and actuator saturations.

This paper presents new synthesis conditions for gain-scheduling static output-feedback control of discrete-time linear systems with time-varying parameters. A feature of the proposed condition, unlike most approaches in the literature, is that no structural constraints on the output matrix are imposed, that is, the proposed approach is able to handle variation in the dynamics, input, and output matrices without resorting to similarity transformations or iterative procedures. The stabilization conditions are extended to cope with the H∞ control problem. The proposed conditions are formulated in the form of linear matrix inequalities. Numerical experiments illustrate the potential of the proposed techniques.

In this article, the issue of dissipative asynchronous control for continuous-time T–S fuzzy singular Markov jump linear parameter-varying systems against dual deception attacks under the dynamic event-triggered transmission protocol (DETP) is investigated. Firstly, the DETP is offered to further abate the channel congestion caused by the limited bandwidth. Meanwhile, the mutually independent random variables subject to Bernoulli distribution are utilized to model the dual deception attacks, which can destroy the integrity of the considered system to some degree. Besides, since the controller can not accurately receive the system information, a hidden Markov model is established to depict the asynchronous phenomenon. Specifically, in the light of the parameter-dependent Lyapunov functional, the stochastic admissible criterion of the closed-loop system with certain dissipative performance and uncertain transition rates is obtained. Ulteriorly, based on the parameter-dependent linear matrix inequalities, a cooperative design technique of asynchronous controller and the weighting matrix of the DETP is proposed. Finally, two examples of chaotic systems and electric truck-trailer systems under the DETP are given to illustrate the feasibility of the proposed method.

This paper investigates the observer-based consensus control problem for linear parameter-varying (LPV) multi-agent systems (MASs) with unknown inputs. Firstly, an interval observer (IO) is designed to generate the state interval estimation for each agent. Secondly, an algebraic relationship is established between the system state and unknown input (UI). Thirdly, an unknown input observer (UIO) capable of generating estimates of UI and the system state has been developed through the algebraic relations. Finally, a UIO-based distributed control protocol scheme is proposed to realize the consensus of the MASs. In the end, to verify the validity of the proposed method, an example of a numerical simulation is given.

This paper is concerned with the transformed parameter-dependent H ∞ controller design for semi-Markovian jump linear parameter varying (S-MJLPV) systems under actuator saturation and faults. In the S-MJLPV system, the semi-Markov process transition rate is time-varying during the semi-Markov process and a plant includes time-varying parameters which are bounded and measurable in magnitude. For more practical analysis and synthesis of the S-MJLPV systems, a time-varying actuator fault model and actuator saturation of the controller are considered into account simultaneously. The primary goal of this paper is to develop a transformed parameter-dependent control that makes the closed-loop system stochastically stable with H ∞ performance index γ and provides less conservative results against actuator saturation and faults. Based on the mode-dependent Lyapunov function, new sufficient conditions are obtained to ensure that the stochastic stability of S-MJLPV systems. Eventually, an example based on the turbofan-engine model is presented to demonstrate the efficacy of our proposed methods.