Mathematical control of complex systems

Copyright © 2013 ZidongWang et al.This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Performance Guarantee of a Class of Continuous LPV System with Restricted-Model-Based Control

This paper considers the problem of the robust stabilisation of a class of continuous Linear Parameter Varying (LPV) systems under specifications. In order to guarantee the stabilisation of the plant with very large parameter uncertainties or variations, an output derivative estimation controller is considered. The design of such controller that guarantee desired  induced gain performance is examined. Furthermore, a simple procedure for achieving the  norm performance is proved for any all-poles single-input/single-output second order plant. The proof of stability is based on the polytopic representation of the closed loop under Lyapunov conditions and system transformations. Finally, the effectiveness of the proposed method is verified via a numerical example

Output peak control of nonhomogeneous markov jump system with unit-energy disturbance

This paper considers output peak controller design for discrete nonhomogeneous Markov jump systems under unit-energy disturbance. The mode-dependent output peak feedback controller is designed to ensure that the resulting closed-loop system is stochastically stable and the peak of the output is within a specified range. Furthermore, the optimal energy-to-peak gain indices of the mode-dependent and the mode-independent state feedback controllers are evaluated and compared. A numerical example is presented to illustrate the applicability of the results obtained

Gain-Scheduled Fault Detection Filter For Discrete-time LPV Systems

The present work investigates a fault detection problem using a gain-scheduled filter for discrete-time Linear Parameter Varying systems. We assume that we cannot directly measure the scheduling parameter but, instead, it is estimated. On the one hand, this assumption imposes the challenge that the fault detection filter should perform properly even when using an inexact parameter. On the other, it avoids the burden associated with designing a complex estimation process for this parameter. We propose three design approaches: the ${\mathcal {H}_{2}}$ , ${\mathcal {H}_{\infty }}$ , and mixed ${\mathcal {H}_{2}} / {\mathcal {H}_{\infty }}$ gain-scheduled Fault Detection Filters designed via Linear Matrix Inequalities. We also provide numerical simulations to illustrate the applicability and performance of the proposed novel methods

Control and filtering of time-varying linear systems via parameter dependent Lyapunov functions

The main contribution of this dissertation is to propose conditions for linear filter and controller design, considering both robust and parameter dependent structures, for discrete time-varying systems. The controllers, or filters, are obtained through the solution of optimization problems, formulated in terms of bilinear matrix inequalities, using a method that alternates convex optimization problems described in terms of linear matrix inequalities. Both affine and multi-affine in different instants of time (path dependent) Lyapunov functions were used to obtain the design conditions, as well as extra variables introduced by the Finsler\u27s lemma. Design problems that take into account an H-infinity guaranteed cost were investigated, providing robustness with respect to unstructured uncertainties. Numerical simulations show the efficiency of the proposed methods in terms of H-infinity performance when compared with other strategies from the literature

Optimized state feedback regulation of 3DOF helicopter system via extremum seeking

In this paper, an optimized state feedback regulation of a 3 degree of freedom (DOF) helicopter is designed via extremum seeking (ES) technique. Multi-parameter ES is applied to optimize the tracking performance via tuning State Vector Feedback with Integration of the Control Error (SVFBICE). Discrete multivariable version of ES is developed to minimize a cost function that measures the performance of the controller. The cost function is a function of the error between the actual and desired axis positions. The controller parameters are updated online as the optimization takes place. This method significantly decreases the time in obtaining optimal controller parameters. Simulations were conducted for the online optimization under both fixed and varying operating conditions. The results demonstrate the usefulness of using ES for preserving the maximum attainable performance

D04.05 - Feasibility mock-ups of feedback schedulers

Control and computation co-design deals with the interaction between feedback control laws design and their implementation on a real execution resource. Control design is often carried out in the framework of continuous time, or under the assumption of ideal sampling with equidistant intervals and known delays. Implementation on a real-time execution platform introduces many timing uncertainties and distortions to the ideal timing scheme, e.g. due to variable computation durations, complex preemption patterns between concurrent activities, uncertain network induced communication delays or occasional data loss. Analyzing, prototyping, simulating and guaranteeing the safety of complex control systems are very challenging topics. Models are needed for the mechatronic continuous system, for the discrete controllers and diagnosers, and for network behavior. Real-time properties (task response times) and the network Quality of Service (QoS) influence the controlled system properties (Quality of Control, QoC). To reach effective and safe systems it is not enough to provide theoretic control laws and leave programmers and real-time systems engineers just do their best to implement the controllers. This report first describes, through the detailed design of a quadrotor drone controller, the main features of {\sc Orccad}, an integrated development environment aimed to bridge the gap between advanced control design and real-time implementation. Besides control design and implementation, a real-time (hardware-in-the-loop) simulation has been designed to assess the control design with a simulated target rather than with the real plant. Using this HIL structure, several experiments using flexible real-time control features are reported, namely Kalman filters subject to data loss, control under (m,k)-firm constraints, control with varying sampling rates and feedback scheduling using the MPC approach