Robust multivariable predictive control: how can it be applied to industrial test stands ?

To cope with recent technological evolutions of air conditioning systems for aircraft, the French Aeronautical Test Center built a new test stand for certification at ground level. The constraints specified by the industrial users of the process seemed antagonistic for many reasons. First, the controller had to be implemented on an industrial automaton, not adaptable to modern algorithms. Then the specified dynamic performances were very demanding, especially taking into account the wide operating ranges of the process. Finally, the proposed controller had to be easy for nonspecialist users to handle. Thus, the control design and implementation steps had to be conducted considering both theoretical and technical aspects. This finally led to the development of a new multivariable predictive controller, called alpha-MPC, whose main characteristic is the introduction of an extra tuning parameter alpha that has enhanced the overall control robustness. In particular, the H1-norm of the sensitivity functions can be significantly reduced by tuning this single new parameter. It turns out to be a simple but efficient way to improve the robustness of the initial algorithm. The other classical tuning parameters are still physically meaningful, as is usual with predictive techniques. The initial results are very promising and this controller has already been adopted by the industrial users as the basis of the control part for future developments of the test stand

Learning-based predictive control for linear systems: a unitary approach

A comprehensive approach addressing identification and control for learningbased Model Predictive Control (MPC) for linear systems is presented. The design technique yields a data-driven MPC law, based on a dataset collected from the working plant. The method is indirect, i.e. it relies on a model learning phase and a model-based control design one, devised in an integrated manner. In the model learning phase, a twofold outcome is achieved: first, different optimal p-steps ahead prediction models are obtained, to be used in the MPC cost function; secondly, a perturbed state-space model is derived, to be used for robust constraint satisfaction. Resorting to Set Membership techniques, a characterization of the bounded model uncertainties is obtained, which is a key feature for a successful application of the robust control algorithm. In the control design phase, a robust MPC law is proposed, able to track piece-wise constant reference signals, with guaranteed recursive feasibility and convergence properties. The controller embeds multistep predictors in the cost function, it ensures robust constraints satisfaction thanks to the learnt uncertainty model, and it can deal with possibly unfeasible reference values. The proposed approach is finally tested in a numerical example

Control optimization, stabilization and computer algorithms for aircraft applications

The analysis and design of complex multivariable reliable control systems are considered. High performance and fault tolerant aircraft systems are the objectives. A preliminary feasibility study of the design of a lateral control system for a VTOL aircraft that is to land on a DD963 class destroyer under high sea state conditions is provided. Progress in the following areas is summarized: (1) VTOL control system design studies; (2) robust multivariable control system synthesis; (3) adaptive control systems; (4) failure detection algorithms; and (5) fault tolerant optimal control theory

Advanced Concept Modeling

Advanced Concepts Modeling software validation, analysis, and design. This was a National Institute of Aerospace contract with a lot of pieces. Efforts ranged from software development and validation for structures and aerodynamics, through flight control development, and aeropropulsive analysis, to UAV piloting services

Damping controller design for FACTS devices in power systems using novel control techniques

Power systems are under increasing stress as deregulation introduces several new economic objectives for operation. Since power systems are being operated close to their limits, weak connections, unexpected events, hidden failures in protection system, human errors, and a host of other factors may cause a system to lose stability and even lead to catastrophic failure. Therefore, the need for improved system damping in a wider operating range is gaining more attention. Among the available damping control methods, each approach has advantages and disadvantages in different systems. The effectiveness of damping control depends on the devices chosen, the system modal feature, and the applied controller design method;In the literature, many approaches have been proposed to undertake this task. However, some of these approaches only take a fixed operating point into consideration without describing the changing uncertainty in varying system conditions; computational effort. Furthermore, no systematic comparison of controller design methods has been conducted with regard to different system profiles. Attention has been drawn to the enhanced susceptibility to inter-area oscillations between groups of machines under large others require a great deal of variation of system operating conditions. The linear parameter varying (LPV) approach, which has been widely studied in the literature, provides a potential method for capturing the varying system condition precisely without formulation of system uncertainty. However, in some cases no solution can be achieved if the system variation is too large using the traditional LPV approach. Also, sometimes the system structure imposes limitations in the achievable damping performance. In general, there is a critical need for a cost-effective control strategy applicable to different systems from an economic point of view;In this dissertation, a comprehensive comparison among controller design methods has been conducted to study the damping effectiveness of different FACTS devices. Based on these, a robust regional pole-placement method is applied in a TCSC damping controller design in a 4-machine system; an interpolated LPV approach is proposed and applied to designing a SVC damping controller in the IEEE 50-machine system; finally with the advantage of an additional feedback signal, limitations in achieving satisfactory damping performance can be relieved using a two-input single-output (TISO) damping controller for a TCSC in the IEEE 50-machine system