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

In order to improve fuel efficiency, future aircraft will have reduced weight and increased wingspan. Such aircraft require active control systems to suppress ASE effects. ASE systems are often modeled in the grid based linear parameter-varying (LPV) framework, which captures the parameter varying dynamics. The controller is generally synthesized by solving Linear Matrix Inequalities (LMIs) for the LPV model. Selecting the grid density for such control synthesis approach requires special attention. On the one hand, a too coarse grid might not capture the parameter variation of the dynamics accurately enough. On the other hand, solving LMIs for too dense grid can lead to numerical issues and computational cost. This is usually relaxed by synthesizing the controller for a coarser grid and the stability and performance are verified for a denser grid. A possible remedy for this drawback of grid-based LPV models is polytopic LPV representation. In such case the LMIs need to be solved only for the vertex systems of the convex polytopic hull. Various types of convex polytopic models can be obtained by Tensor Product (TP) model transformation. The aim of the paper is to derive polytopic models for ASE vehicles and to apply these models for flutter suppression control design. The goal is to have a small number of vertex systems and sufficient accuracy while keeping the conservativeness of polytopic modeling low. The aircraft under consideration is the mini MUTT (Multi Utility Technology Testbed) vehicle. Based on the polytopic representation a stabilizing state feedback controller and observer is synthesized. The effectiveness of the resulting control design is verified through numerical simulations.

Polytopic LPV modeling and gain scheduling [formula omitted] control of ball screw with position- and load-dependent variable dynamics

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.

In this paper a novel Simultaneous Localization and Mapping (SLAM) is presented. Using the sound as the input signal, instead of the classical vision or laser systems, leads to a SSLAM (sound SLAM) with a new features, such as the use of a Linear Parameter Varying (LPV) Kalman filter rather than the classical Extended Kalman filter. The other novelty is the modeling of sound reverberation using LPV models. The work is an extension under development from previous research group's works. The experimental partial results and the theoretical developments encourage authors to follow this unexplored line of SSLAM.

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.

Polytopic LPV modeling and gain-scheduled switching control for a flexible air-breathing hypersonic vehicle

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.

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.

Optimal experimental design for LPV identification using a local approach

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.

Robust control of gust-induced vibration of highly flexible aircraft

A TP-LPV-LMI based control for Tumor Growth Inhibition

A model order reduction method is proposed for models of aeroservoelastic vehicles in the linear parameter-varying (LPV) systems framework, based on state space interpolation of modal forms. The dynamic order of such models is usually too large for control synthesis and implementation since they combine rigid body dynamics, structural dynamics and unsteady aerodynamics. Thus, model order reduction is necessary. For linear timeinvariant (LTI) models, order reduction is often based on balanced realizations. For LPV models, this requires the solution of a large set of linear matrix inequalities (LMIs), leading to numerical issues and high computational cost. The proposed approach is to use well developed and numerically stable LTI techniques for reducing the LPV model locally and then to transform the resulting collection of systems into a consistent modal representation suitable for interpolation. The method is demonstrated on an LPV model of the body freedom flutter vehicle, reducing the number of states from 148 to 15. The accuracy of the reduced order model (ROM) is confirmed by evaluating the ν-gap metric with respect to the full order model and by comparison to another ROM obtained by state-of-the-art LPV balanced truncation techniques.

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.

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