Model-free norm-based fixed structure controller synthesis

This paper presents a method to perform model-free fixed structure controller synthesis. Based on frequency response data of the plant, the parameters of a predefined controller structure are optimized directly with respect to closed-loop performance specifications. As a result, no parametric plant model is required such that time consuming iterative identification-synthesis procedures can be omitted. A framework is presented to both assure stability and optimize closed-loop performance based on frequency response data. Furthermore, based upon these results, a cost-function is formulated that can be exploited to converge from a destabilizing- to the stabilizing controller parameter region. Both the stability guarantee and performance optimization procedures are combined in one optimization algorithm that is illustrated by means of an example

A Data-driven Approach to Robust Control of Multivariable Systems by Convex Optimization

The frequency-domain data of a multivariable system in different operating points is used to design a robust controller with respect to the measurement noise and multimodel uncertainty. The controller is fully parametrized in terms of matrix polynomial functions and can be formulated as a centralized, decentralized or distributed controller. All standard performance specifications like $H_2$, $H_\infty$ and loop shaping are considered in a unified framework for continuous- and discrete-time systems. The control problem is formulated as a convex-concave optimization problem and then convexified by linearization of the concave part around an initial controller. The performance criterion converges monotonically to a local optimal solution in an iterative algorithm. The effectiveness of the method is compared with fixed-structure controllers using non-smooth optimization and with full-order optimal controllers via simulation examples. Finally, the experimental data of a gyroscope is used to design a data-driven controller that is successfully applied on the real system

Robust control of longitudinal flight with handling qualities constraints

Classical flight control systems are still widely used in industry because of acquired experience and good understanding of their structure. Nevertheless, with more stringent constraints, it becomes difficult to easily fulfill all the criteria with this classical control laws. This article aims at showing that this problem can be solved by first designing a high order controller satisfying all the constraints, then by reducing and structuring it in order to make it look like a classical controller. Firstly, an H∞ synthesis is performed in order to get a robust controller versus mass and center of gravity variations, which will satisfy the handling qualities; then it will be reduced by using robust modal control techniques

Robust nonlinear control of vectored thrust aircraft

An interdisciplinary program in robust control for nonlinear systems with applications to a variety of engineering problems is outlined. Major emphasis will be placed on flight control, with both experimental and analytical studies. This program builds on recent new results in control theory for stability, stabilization, robust stability, robust performance, synthesis, and model reduction in a unified framework using Linear Fractional Transformations (LFT's), Linear Matrix Inequalities (LMI's), and the structured singular value micron. Most of these new advances have been accomplished by the Caltech controls group independently or in collaboration with researchers in other institutions. These recent results offer a new and remarkably unified framework for all aspects of robust control, but what is particularly important for this program is that they also have important implications for system identification and control of nonlinear systems. This combines well with Caltech's expertise in nonlinear control theory, both in geometric methods and methods for systems with constraints and saturations