Gain‐scheduled H∞ filtering of parameter‐varying systems

This paper deals with the gain‐scheduled H_∞ filtering problem for a class of parameter‐varying systems. A sufficient condition for the existence of a gain‐scheduled filter, which guarantees the asymptotic stability with an H_∞ noise attenuation level bound for the filtering error system, is given in terms of a finite number of linear matrix inequalities (LMIs). The filter is designed to be parameter‐varying and have a nonlinear fractional transformation structure. A numerical example is presented to demonstrate the application of the proposed method. Copyright © 2006 John Wiley & Sons, Ltd.     

Conservatism reduction for linear parameter‐varying control design facing inexact scheduling parameters illustrated on flight tests

This article addresses the design problem of linear parameter‐varying (LPV) output feedback controllers that depend on inexact scheduling parameters for LPV systems. This problem has already been tackled and several methods have been proposed by overbounding the discrepancies between the actual scheduling parameters and the provided ones in the derivation of controller design condition. However, all methods in literature have conservatism in the overbounding, which is the main issue addressed in this article. We therefore propose a new overbounding for the discrepancies with the reverse use of Elimination lemma, which introduces no conservatism in theory. The new method is formulated in terms of bilinear matrix inequality, which is not tractable compared with linear matrix inequality, thus a practical design procedure composed of line search and iterative algorithm is shown. The effectiveness of our method is illustrated by an application to flight controller design for the lateral‐directional motions of a research airplane MuPAL‐α and the consequently conducted flight tests.     

Gain‐scheduled ℋ︁2 controller synthesis for linear parameter varying systems via parameter‐dependent Lyapunov functions

This paper deals with the problem of gain‐scheduled ??₂ control for linear parameter‐varying systems. The system state–space model matrices are affinely parameterized and the admissible values of the parameters and their rate of variation are supposed to belong to a given convex bounded polyhedral domain. Based on a parameter‐dependent Lyapunov function, a linear matrix inequality methodology is proposed for designing a gain‐scheduled state feedback ??₂ controller, where the feedback gain is a matrix fraction of polynomial matrices with quadratic dependence on the scheduling parameters. Copyright © 2005 John Wiley & Sons, Ltd.     

Analysis of linear parameter‐varying systems using a non‐smooth dissipative systems framework

This paper presents new finite‐dimensional linear matrix inequality formulations for several important analysis problems of linear parameter‐varying systems. The approach is based on a non‐smooth dissipative systems framework using a continuous, piecewise‐affine parameter‐dependent Lyapunov function. The new analysis approach yields guaranteed and less conservative results than previously published techniques that are based on affine parameter‐dependent Lyapunov functions or Luré–Postnikov Lyapunov functions. Conservatism is reduced in this new approach because the analysis uses a more general class of parameter‐dependent Lyapunov functions (PDLFs). The new approach also provides an explicit trade‐off between conservatism and computational effort of the analysis technique. Note that, in contrast to the gridding approach typically used to develop a computationally feasible algorithm, this proposed approach guarantees the analysis results. This paper also shows that the numerical results of our approach, while computationally intensive, could be used to develop many new insights into the selection of PDLFs in the other analysis techniques. Copyright © 2002 John Wiley & Sons, Ltd.     

A less conservative approach to handle time‐varying parameters in discrete‐time linear parameter‐varying systems with applications in networked control systems

This article proposes a new strategy to deal with linear parameter‐varying discrete‐time systems, whose time‐varying parameters can be written as solutions (such as exponential, trigonometric, or periodic function) of a linear difference equation (DE). The novelty is to explicitly exploit the precise knowledge of the function describing the time‐varying parameter by incorporating the associated DE in the conditions, providing less conservative results when compared with conventional approaches based on bounded or arbitrary rates of variation. The advantage of the method comes from the fact that, differently from the available methods, the pointwise stability for the whole domain of the time‐varying parameters is not a necessary condition to obtain feasible solutions. The applicability and benefits of the proposed technique are investigated in terms of numerical examples concerning robust stability analysis,  filtering, and  state‐feedback control. As a final contribution, the problem of time‐varying sampling periods in the context of networked control systems is investigated using the proposed strategy. A numerical example based on a practical application is presented to illustrate the superiority of the approach when compared to methods from the literature based on matrix exponential computation.     

Switching linear parameter‐varying control with improved local performance and optimized switching surfaces

This paper presents a novel approach to designing switching linear parameter‐varying (SLPV) controllers with improved local performance and an algorithm for optimizing switching surfaces to further improve the performance of the SLPV controllers. The design approach utilizes the weighted average of the local L₂‐gain bounds (representing the local performance) as the cost function to be minimized, whereas the maximum of the local L₂‐gain bounds (representing the worst‐case performance over all subsets) is bounded with a tuning parameter. The tuning parameter is useful for taking the trade‐off between the local performance and the worst‐case performance. An algorithm based on the particle swarm optimization is introduced to optimize the switching surfaces of an SLPV controller. The efficacy of the proposed SLPV controller design approach and switching surface optimization algorithm is demonstrated on both a numerical example and a physical example of air‐fuel ratio control of an automotive engine.     

Gain‐scheduled leader‐follower tracking control for interconnected parameter varying systems

This paper considers the gain‐scheduled leader‐follower tracking control problem for a parameter varying complex interconnected system with directed communication topology and uncertain norm‐bounded coupling between the agents. A gain‐scheduled consensus‐type control protocol is proposed and a sufficient condition is obtained, which guarantees a suboptimal bound on the system tracking performance under this protocol. An interpolation technique is used to obtain a protocol schedule, which is continuous in the scheduling parameter. The effectiveness of the proposed method is demonstrated using a simulation example. Copyright © 2015 John Wiley & Sons, Ltd.     

A predictive control strategy for norm‐bounded LPV discrete‐time systems with bounded rates of parameter change

A novel predictive control strategy for input‐saturated norm‐bounded linear parameter varying discrete‐time systems is proposed. The solution is computed by minimizing an upper bound to the ‘worst‐case’ infinite horizon quadratic cost under the constraint of steering the future state evolutions, emanating from the current state, into a feasible and positive invariant set. It will be shown that the ‘size’ of this terminal set depends on the rate of change of the scheduling parameter, which is assumed to be bounded and measurable. Copyright © 2007 John Wiley & Sons, Ltd.     

Integrated fault detection system design for linear discrete time‐varying systems with bounded power disturbances

This paper investigates the problem of an integrated fault detection system design for linear discrete time‐varying systems with bounded power disturbances. In the integrated design of residual generator and evaluator, an approximated energy constraint is first imposed on the bounded power disturbances, and then selected by solving a min–max problem to achieve minimal‐size set of undetectable faults under the condition of zero false alarms. To tackle the problem that the computational burden involved in solving the min–max optimization grows with time, the moving horizon method is proposed. The proposed approach in this paper has two advantages: (i) the approximated energy constraint on bounded power disturbances is explicitly selected as a min–max solution in the integrated design to improve fault detection rate; by contrast, when directly applying any existing fault detection method to the case of bounded power disturbances, a predefined approximated energy constraint is implicitly introduced without considering fault detection performance; (ii) the design objective of the proposed approach can choose to consider faults only in the recent time horizon rather than faults in the complete time horizon; this strategy enhances detection performance of recent faults and benefits early fault detection, but has not been considered by existing fault detection methods. Copyright © 2012 John Wiley & Sons, Ltd.     

Dynamic controllers for local input‐to‐state stabilization of discrete‐time linear parameter‐varying systems with delay and saturating actuators

We address the design of dynamic parameter‐dependent controllers with antiwindup action to locally stabilize in the input‐to‐state sense a class of discrete‐time linear parameter‐varying (LPV) systems. Such a class consists of systems with delayed state, saturating actuators, and subject to energy bounded disturbances. Moreover, the interval time‐varying delay can have a limited variation rate between two consecutive instants allowing to achieve less conservative design conditions. Differently from other conditions in the literature, the proposed convex synthesis methods allow to design dynamic controllers of different orders. Additionally, the user can choose to feed back only the current output of the system or its delayed ones. Thanks to the embedded (parameter dependent) antiwindup action, it is possible, for instance, to enlarge the region of admissible initial conditions or the maximum admissible disturbance energy. To illustrate the efficiency of our approach, we present numerical examples to compare with other methods from the literature.     

An improved robust model predictive control for linear parameter‐varying input‐output models

This paper describes a new robust model predictive control (MPC) scheme to control the discrete‐time linear parameter‐varying input‐output models subject to input and output constraints. Closed‐loop asymptotic stability is guaranteed by including a quadratic terminal cost and an ellipsoidal terminal set, which are solved offline, for the underlying online MPC optimization problem. The main attractive feature of the proposed scheme in comparison with previously published results is that all offline computations are now based on the convex optimization problem, which significantly reduces conservatism and computational complexity. Moreover, the proposed scheme can handle a wider class of linear parameter‐varying input‐output models than those considered by previous schemes without increasing the complexity. For an illustration, the predictive control of a continuously stirred tank reactor is provided with the proposed method.     

Robust linear parameter‐varying control of induction motors

The paper deals with the motion control of an induction motor. Because the nonlinear state equations describing the dynamics of such a machine can be embedded into a linear model with the rotor speed ω as a varying parameter, advantage is taken of some recent results on the control of linear parameter‐varying systems, thus ensuring stability independently of how the varying parameter changes in time within a compact set. The adopted control structure consists of a fast inner electric loop that controls the stator currents and an outer mechanical loop that generates the torque acting on the motor shaft. Of crucial importance is the design of the internal model controller for the current loop. In particular, it is proved that an algebraically equivalent electric motor model admits a Lyapunov function that, together with its Lyapunov derivative, is independent of ω and of all motor parameters. This result allows us to find an upper bound on the norm of the Youla–Kucera parameter that ensures robust stability against speed measurement errors. Simulations carried out on a benchmark motor model show that the adopted control strategy performs well. Copyright © 2014 John Wiley & Sons, Ltd.     

Finite‐time control for LPV systems with parameter‐varying time delays and exogenous disturbances

In this paper, we consider the stability analysis and control synthesis of finite‐time boundedness problems for linear parameter‐varying (LPV) systems subject to parameter‐varying time delays and external disturbances. First, the concepts of uniform finite‐time stability and uniform finite‐time boundedness are introduced to LPV systems. Then, sufficient conditions, which guarantee LPV systems with parameter‐varying time delays finite‐time bounded, are presented by using parameter‐dependent Lyapunov–Krasovskii functionals and free‐weight matrix technologies. Moreover, on the basis of the results on the uniform finite‐time boundedness, the parameter‐dependent state feedback controllers are designed to finite‐time stabilize LPV systems. Both analysis and synthesis conditions are delay‐dependent, and they are formulated in terms of linear matrix inequalities by using efficient interior‐point algorithms. Finally, results obtained in simulation demonstrate the effectiveness of the proposed approach. Copyright © 2017 John Wiley & Sons, Ltd.     

Identification of hybrid and linear parameter‐varying models via piecewise affine regression using mixed integer programming

This article presents a two‐stage algorithm for piecewise affine (PWA) regression. In the first stage, a moving horizon strategy is employed to simultaneously estimate the model parameters and to classify the training data by solving a small‐size mixed‐integer quadratic programming problem. In the second stage, linear multicategory separation methods are used to partition the regressor space. The framework of PWA regression is adapted to the identification of PWA AutoRegressive with eXogenous input (PWARX) models as well as linear parameter‐varying (LPV) models. The performance of the proposed algorithm is demonstrated on an academic example and on two benchmark experimental case studies. The first experimental example concerns modeling the placement process in a pick‐and‐place machine, while the second one consists in the identification of an LPV model describing the input‐output relationship of an electronic bandpass filter with time‐varying resonant frequency.     

Gain scheduled control for discrete‐time systems depending on bounded rate parameters

In this paper we consider a linear, discrete‐time system depending multi‐affinely on uncertain, real time‐varying parameters. A new sufficient condition for the stability of this class of systems, in terms of a feasibility problem involving linear matrix inequalities (LMIs), is obtained under the hypothesis that a bound on the rate of variation of the parameters is known. This condition, obtained by the aid of parameter dependent Lyapunov functions, obviously turns out to be less restrictive than that one obtained via the classical quadratic stability (QS) approach, which guarantees stability in presence of arbitrary time‐varying parameters. An important point is that the methodology proposed in this paper may result in being less conservative than the classical QS approach even in the absence of an explicit bound on the parameters rate of variation. Concerning the synthesis context, the design of a gain scheduled compensator based on the above approach is also proposed. It is shown that, if a suitable LMI problem is feasible, the solution of such problem allows to design an output feedback gain scheduled dynamic compensator in a controller‐observer form stabilizing the class of systems which is dealt with. The stability conditions are then extended to take into account L₂ performance requirements. Some numerical examples are carried out to show the effectiveness and to investigate the computational burden required by the proposed approach. Copyright © 2005 John Wiley & Sons, Ltd.     

Observer‐based synthesis of linear parameter‐varying mixed sensitivity controllers

This article presents a computationally efficient way of synthesizing linear parameter‐varying (LPV) controllers. It reviews the possibility of a separate observer and state feedback synthesis with guaranteed performance and shows that a standard mixed sensitivity problem can be solved in this way. The resultant output feedback controller consists of an LPV observer, augmented with dynamic filters to incorporate integral control and roll‐off properties, and an LPV state feedback gain. It is thus highly structured, which is beneficial for implementation. Moreover, it does not depend on scheduling parameter rates regardless of whether parameter‐dependent Lyapunov matrices are used during synthesis. A representative control design for active flutter suppression on an aeroelastic unmanned aircraft demonstrates the benefits of the proposed method in comparison with state‐of‐the‐art LPV output feedback synthesis.     

Gain‐scheduled dynamic output feedback control for discrete‐time LPV systems

This paper presents synthesis conditions for the design of gain‐scheduled dynamic output feedback controllers for discrete‐time linear parameter‐varying systems. The state‐space matrix representation of the plant and of the controller can have a homogeneous polynomial dependency of arbitrary degree on the scheduling parameter. As an immediate extension, conditions for the synthesis of a multiobjective ??_∞ and ??₂ gain‐scheduled dynamic feedback controller are also provided. The scheduling parameters vary inside a polytope and are assumed to be a priori unknown, but measured in real‐time. If bounds on the rate of parameter variation are known, they can be taken into account, providing less conservative results. The geometric properties of the uncertainty domain are exploited to derive finite sets of linear matrix inequalities based on the existence of a homogeneous polynomially parameter‐dependent Lyapunov function. An application of the control design to a realistic engineering problem illustrates the benefits of the proposed approach. Copyright © 2011 John Wiley & Sons, Ltd.     

ℋ︁∞ and l2–l∞ filtering for two‐dimensional linear parameter‐varying systems

In this paper, the ??_∞ and l₂–l_∞ filtering problem is investigated for two‐dimensional (2‐D) discrete‐time linear parameter‐varying (LPV) systems. Based on the well‐known Fornasini–Marchesini local state‐space (FMLSS) model, the mathematical model of 2‐D systems under consideration is established by incorporating the parameter‐varying phenomenon. The purpose of the problem addressed is to design full‐order ??_∞ and l₂–l_∞ filters such that the filtering error dynamics is asymptotic stable and the prescribed noise attenuation levels in ??_∞ and l₂–l_∞ senses can be achieved, respectively. Sufficient conditions are derived for existence of such filters in terms of parameterized linear matrix inequalities (PLMIs), and the corresponding filter synthesis problem is then transformed into a convex optimization problem that can be efficiently solved by using standard software packages. A simulation example is exploited to demonstrate the usefulness and effectiveness of the proposed design method. Copyright © 2007 John Wiley & Sons, Ltd.     

Exponential H∞ filtering for switched linear systems with interval time‐varying delay

This paper deals with the problem of exponential H_∞ filtering for a class of continuous‐time switched linear system with interval time‐varying delay. The time delay under consideration includes two cases: one is that the time delay is differentiable and bounded with a constant delay‐derivative bound, whereas the other is that the time delay is continuous and bounded. Switched linear filters are designed to ensure that the filtering error systems under switching signal with average dwell time are exponentially stable with a prescribed H_∞ noise attenuation level. Based on the free‐weighting matrix approach and the average dwell technology, delay‐dependent sufficient conditions for the existence of such a filter are derived and formulated in terms of linear matrix inequalities (LMIs). By solving that corresponding LMIs, the desired filter parameterized matrices and the minimal average dwell time are obtained. Finally, two numerical examples are presented to demonstrate the effectiveness of the developed results. Copyright © 2008 John Wiley & Sons, Ltd.