Gain Scheduled Output Feedback Control of Discrete-Time Networked Systems

Causal Gain-scheduled output feedback controllers using parameter-dependent Lyapunov Functions

Gain-scheduled output-feedback controllers using inexact scheduling parameters for continuous-time LPV systems

In this paper, a new control approach is proposed for the transient stabilization of a single-machine infinite-bus power system. The proposed method is based on an LPV (linear parameter varying) modelling of the nonlinear power system and gain-scheduled output feedback control. It is well-known that when large disturbances or a fault occurs, the nonlinearity inherent in power systems can no longer be ignored. The proposed method can handle the nonlinear model directly. First, we show that the nonlinear model can be transformed equivalently into an LPV system with the rotor angle as the scheduling parameter. Then, a gain-scheduled output feedback controller is designed based on robust pole placement and L2-gain minimization. Simulation results verify that the proposed method is better than well-tuned conventional PSS control.

This paper addresses the design problem of Gain-Scheduled Output-Feedback (GSOF) controllers for discretetime Linear Parameter-Varying (LPV) systems via Parameter-Dependent Lyapunov Functions (PDLFs). In our problem setting, it is supposed that scheduling parameters are provided with some uncertainties. In this practical problem setting, we give a sufficient condition for designing GSOF controllers exploiting the uncertain scheduling parameters which minimize the induced l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm in terms of Parameter-Dependent Bilinear Matrix Inequalities (PDBMIs). Two algorithms for solving the PDBMIs, i.e. line search and iterative algorithms, are shown. Under the ideal situation, i.e. exact values of scheduling parameters are available, our method then recovers the conventional design method via PDLFs.

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In this paper, we consider the fault Detection and isolation (FDI) problem for faulty linear parameter-varying (LPV) systems subject to disturbances and propose a observer-based solution by using multiobjective optimization techniques. To simply the design process, a general faulty LPV system will be constructed from standard LPV description by converting actuator/system component faults into sensor faults at first. Then a bank of LPV FDI filters will be designed to identify each fault. Each FDI filter could generate a residual signal to track individual fault with minimum error and suppressing the effects of disturbances and other fault signals. The design of FDI filters will be formulated as multiobjective optimization problems in terms of linear matrix inequalities (LMIs) and can be solved efficiently. Two numerical examples are also presented to demonstrate the proposed fault detection and isolation approach on both LPV and LTI systems.

This paper discusses H-infinity state feedback control for a networked control system with time-varying delays. Based on the free-weighing matrix method, a delay-dependent stability criterion satisfying a prescribed H-infinity norm bound is presented for an NCS with unknown, time-varying and bounded delays. And then, the criterion is transformed into sufficient conditions based on linear matrix inequalities for H-infinity control. The conditions thus obtained are also used to design an H-infinity state feedback controller. This design method is further extended to solve the design problem of robust H-infinity state feedback control. A numerical example demonstrates the validity of die method.

Markovian jumping systems have arisen naturally in the mathematical modeling of phenomena spanning disciplines in the social sciences, natural sciences, and engineering. This kind of stochastic dynamical systems can be employed to model the dynamics when parameters are subject to random abrupt changes due to sudden environment changes, subsystem switching, system noises, executor faults, and so forth. Much attention has been given to modeling, optimization, and real applications of such stochastic dynamical systems in the literature in recent years. As the advanced control and optimization will provide a basis for the design and application of such stochastic systems, these advanced techniques would result in substantial and sustainable benefits. The accepted papers in this special issue include stochastic stability, stabilization, stochastic control optimization, system modeling and identification methods, predictive control, signal processing, robust filtering, multiagent systems, networked control systems, time-delayed systems, neural networks, the Takagi-Sugeno fuzzy systems, simulated annealing, and fault detection methods.

This paper investigates the guaranteed cost control problem for a class of linear parameter-varying (LPV) systems with Markovian switching under partly known transition rates. A key point of this work relaxes Markovian jump systems whose subsystems are described by linear time-invariant (LTI) dynamics to LPV systems with Markovian switching whose subsystems are described by LPV dynamics. Meanwhile, the special assumption of completely known transition rates is relaxed to more general case that transition rates are partly known. Moreover, the proposed control technique for the LPV systems with Markovian switching is used to solve the guaranteed cost control problem of turbofan-engine. Firstly, a guaranteed cost controller is designed, and the design problem can be reduced to a set of linear matrix inequalities (LMIs) feasibility problem. Then, a turbofan-engine example is given to demonstrate the applicability of the main results.

Stability analysis and stabilization of LPV systems with jumps and (piecewise) differentiable parameters using continuous and sampled-data controllers

This paper develops the stability analysis and delay-dependent \(\mathcal {H}_{\infty }\) control synthesis for linear parameter-varying (LPV) systems with time-varying state delays. On the basis of the Finsler’s lemma, sufficient conditions on \(\mathcal {H}_{\infty }\) performance analysis are formulated in terms of parameterized linear matrix inequalities. The interesting annihilator matrix is constituted by time-varying parameters of LPV systems to reduce the conservatism. A numerical example is presented to confirm the efficiency of the proposed method.

In recent years, automatic crane device is used in scenes of handling of objects at warehouse. Stacker crane is an example of automatic crane device. It has a Traveling body, a Forking mechanism and a Lifting mechanism. A Traveling body makes a Lifting body move along the horizontal axis. A Lifting mechanism makes a Forking mechanism move along vertical axis. It is able to carry a load into rack or takes out a load from the rack by using Forking mechanism. But, in action of Forking mechanism, vibration of Forking mechanism and variation of characteristic frequency of Forking mechanism causes several problems. In this study, the stacker crane is described in LPV (Linear Parameter Varying) system. And, using DMM (Dual Model Matching) method provides the LPV controller that can adjust plant that explained by LPV system. The results of simulation show the effectiveness of LPV controller when DMM controlled is applied.

Linear parameter-varying (LPV) systems are usually described in either state-space or input-output form. When analyzing system equivalence between different representations it appears that the time-shifted versions of the scheduling signal (dynamic dependence) need to be taken into account. Therefore, representations used previously to define and specify LPV systems are not equal in terms of dynamics. In order to construct a parametrization-free description of LPV systems that overcomes these difficulties, a behavioral approach is introduced that serves as a basis for specifying system theoretic properties. LPV systems are defined as the collection of trajectories of system variables (like inputs and outputs) and scheduling variables. LPV kernel, input-output, and state-space system representations are introduced with appropriate equivalence transformations.

Linear Parameter Varying (LPV) system shows preferable performance in dealing with NonLinear (NL) systems and systems with time-varying parameters, based on a kind of wheel-legged robot with two-wheel and balance as Inverted Pendulum, this paper proposes a gain scheduling control method for balancing using LPV system on Equivalent model with Center Of Mass (COM). In order to ensure the stability and responsive of the system with possible values for all variable parameters, Higher Order Singular Value Decomposition (HOSVD) is used for constructing polytopic LPV system, then the robust controller is designed for the vertex systems forthputting Linear Matrix Inequality (LMI) approach, and the gain scheduling controller for global system is obtained by convex combination of vertex LTI systems. The simulation results show that the system can achieve the expected indicators.

Input Estimation from Sparse Measurements in LPV Systems and Isotopic Ratios in Polar Firns