Parameter-dependent controller design for electro-hydraulic servo system

This paper presents a nonparametric identification method for state-space linear parameter-varying (LPV) models using a modified support vector machine (SVM) approach. While most LPV identification schemes in the state-space form fall under the general category of parametric methods, regularization-based SVMs provide a viable alternative to model scheduling dependencies, without the need of specifying the dependency structure and with an attractive bias-variance trade-off. In this paper, a solution is proposed for nonparametric identification of LPV state-space models in terms of least-squares SVMs (LS-SVM) and is then extended in a way that the proposed estimation is robust to errors in the noise model estimation. The so-called instrumental variables (IV) method has been used in linear system identification for quite some time, and has recently seen its application in the identification of both nonlinear and LPV systems in the input-output (IO) form. The IV method reduces the bias in estimated LPV state-space models in case the noise model is not estimated properly or is unknown. In the proposed method of this paper, the attractive bias-variance trade-off properties of LS-SVMs are combined with statistical properties of IV-based methods to give robust estimates of the functional dependencies. Numerical examples are provided to compare the performances of the proposed IV-based technique with the LS-SVM-based LPV model identification methods.

Development of an autonomous fog computing platform using control-theoretic approach for robot-vision applications

International audience

Robust control for nonlinear systems with structured [formula omitted]2-gain bounded uncertainty

System or process identification is a mathematical modeling of systems (processes) from test or experimental data. Process models obtained from identification process can be used for operator training simulator, analysis and design of safety systems, and design of process and control systems. This paper presents the Linear Parameter Varying (LPV) model identification for Industrial boiler furnace. LPV model is the development of linear time invariant models of different operating conditions along the overall operating trajectory and interpolation of linear models. The LPV model is adopted by considering the fact that boiler furnace in the thermal power plant has several operating conditions. By assuming that on every operating condition there are parameters changes, the LPV model is suitable for covering all operating conditions. The boiler furnace is modeled as LPV systems with Linear transfer function model structure. Identification algorithm used in the identification process is Prediction error method. Data needed for identification is taken from first principle model of the process with sampling time of 1 second. The identification result is simulated and validated with the measured data. The simulation result shows better accuracy for Linear Parameter Varying model.

It is challenging to design a controller for a wind turbine system because of its nonlinear time-varying dynamics. To solve this issue, linear parameter-varying (LPV) control theory has been applied in the recent ten years to deal with nonlinear and wind-speed-dependent dynamics. Before applying the LPV control method, it is required to transform the nonlinear model of the system to an LPV model. The main objectives of this research are to model a 5-MW Reference Wind Turbine for Offshore System as an LPV system, develop a nonlinear wind turbine simulator using MATLAB/Simulink to validate the LPV model, and conduct a preliminary study on LPV control design to test the simulator, which will be used in the future research for applying different LPV control techniques, such as switching LPV control, LPV antiwindup control, etc.

A linear parameter-varying (LPV) model of the Benchmark Active Controls Technology (BACT) windtunnel model is developed to facilitate the synthesis and analysis of flutter suppression controllers. The BACT wind-tunnel model is a part of NASA Langley Research Center's Benchmark Models Program initiated to study aeroelastic phenomena and flutter suppression. The LPV model developed in this paper is a function of both Mach number and dynamic pressure and accurately represents the BACT wind-tunnel model over a large flight envelope. This LPV model makes it possible to rigorously investigate the robustness properties of flutter suppression controllers developed for the BACT wing. Furthermore, it enables the use of recent results in LPV control theory to develop robust, automatically gain-scheduled flutter suppression controllers.

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.

Optimal experimental design for LPV identification using a local approach

A linear parameter varying (LPV) flight dynamics model (FDM) is proposed to cater for atmospheric disturbance analysis in special flight conditions. A novel FDM which is capable of addressing the influence of turbulent wind is derived under the wind frame. An affine parameter dependent LPV model with wind effects is built based on function substitution method. The optimal solution for the decomposing function of the LPV FDM is obtained by genetic algorithm (GA). The analysis of dynamic response indicates that the genetic-optimized LPV FDM approximates the nonlinear FDM evidently, since it identifies the instantaneous dynamics and flight states varying in a wide range. The simulations of approach and landing against wind shear show that the genetic-optimized LPV FDM captures the instantaneous dynamic response when flying through turbulent wind, indicating that the LPV model can be further applied to turbulent wind special flight analysis and control law design.

Shape Memory Alloys (SMAs) actuators operate via a nonlinear and hysteretic relationship between input power and mechanical motion. This nonlinearity presents a serious challenge when developing methods for controlling these actuators. Because this hysteresis and nonlinearity is caused by the crystal phase transformation however, the SMA constitutive and kinetic models can be written in Linear Parameter Varying (LPV) form, with the partial derivative of crystal phase fraction with respect to temperature as the varying parameter. This allows a SMA system to be written in a state-space format where the coefficients in the state matrices vary as a function of the state variables, allowing for the application of powerful linear system analysis tools to this model without simplifying assumptions. This LPV model can then be used to create an estimator for the system, allowing for real-time approximations of the system states, including temperature and phase fraction. This paper presents the derivation of one such LPV model and explores its ability to accurately represent a physical SMA actuator system by comparison with an instrumented SMA muscle system.

Robust control of gust-induced vibration of highly flexible aircraft

Robust fault diagnosis and fault tolerant control for PEMFC system based on an augmented LPV observer

Direct identification of filters for linear parameter varying (LPV) systems is considered. In the literature on filter design, the system whose state has to be estimated is usually assumed known. However, in most applications, this assumption does not hold, and a two-step procedure is adopted: 1) an LPV model is identified from a set of noise-corrupted data; 2) on the basis of the identified model, an LPV Kalman filter is designed. In this paper, the idea of directly identifying the LPV filter from data is investigated. In previous works by the authors, it has been shown that the direct identification may be more convenient than the two-step design. In some of these works, optimal filter design techniques for time invariant systems have been developed. In the present paper, an approach for the direct identification of optimal filters for LPV systems is proposed. The approach is developed within a Set Membership framework and optimality refers to minimizing the worst-case estimation error.

This paper presents a Linear Parameter Varying (LPV) approach to model and control two-mass systems with backlash. The maximum amplitude of the backlash angle is assumed to be unknown and variable having no knowledge about the upper and lower bounds of it. Proper affine state space model together with the admissible variations of the LPV parameters is designed in order to realize a viable convex polytope. Utilizing H ∞ LPV lemmas and theories lead to a set of Linear Matrix Inequalities (LMIs). By solving these LMIs, appropriate scheduled state feedback gains are obtained. The designed robust control strategy can easily handle the variations of the backlash angle and load disturbance torque. A simulated two-mass backlash system verifies the efficiency of the designed control law.