Multi-input Multi-output Control Design Research Articles

Robust MIMO Controller Design for VSC-Based Microgrids: Sequential Loop Closing Concept and Quantitative Feedback Theory

Like other multi-input multi-output (MIMO) systems, adverse effects of interactions among control loops on the system performance is one of the most challenging issues in the control of microgrids (MGs). In this paper, a robust MIMO controller is proposed for the MGs to solve the problem of power sharing among its distributed generators systematically. To this end, the relative gain array (RGA) matrix is used to address the problem of input-output pairing, and adverse effects of interactions among the control loops are decreased by designing proper pre-compensators. The sequential loop closing technique is employed, and the quantitative feedback theory (QFT) is used to achieve robust stability, the robust tracking performance, and the desired level of disturbance attenuation for all the control loops. The simulation and experimental results reveal the desirable and acceptable performance of the proposed control strategy.

A quantitative feedback solution to the multivariable tracking error problem

SUMMARY This paper introduces a novel solution for the multi-input multi-output (MIMO) quantitative feedback theory control design problem with tracking error specifications. Looking for a minimum controller overdesign, the technique finds new controller quantitative feedback theory bounds based on necessary and sufficient conditions for the existence of suitable associated prefilter matrix elements. It improves previous approaches to the subject and includes (i) the possibility of a free selection of the nominal plant, (ii) a less conservative application of the Schwartz inequality to decisively reduce the potential controller overdesign, (iii) a methodology to design independently the elements of the prefilter matrix, and (iv) a scope of application to both sequential and nonsequential MIMO controller design methods. The benefits of the new control design technique are illustrated by means of two examples. The first one, a standard 2 × 2 MIMO problem, is provided for comparison purposes with previous approaches. The second example, included as a major control challenge, deals with a well-known demanding distillation column benchmark problem. Copyright © 2013 John Wiley & Sons, Ltd.

Active automotive engine vibration isolation using feedback control

Large frequency band feedback active automotive engine vibration isolation is considered. A MIMO (multi-input multi-output) controller design for an active engine suspension system has been performed making use of a virtual development environment for design, analysis, and co-simulation based closed-loop verification. Utilising relevant control object dynamic modelling, this design strategy provides a powerful opportunity to deal with various plant dynamics, such as structural flexibility and nonlinear characteristics where the main objective is to approach the actual physical characteristics for design and verification in early design phases where no prototypes are yet physically available. H 2 loop shaping technique proves to be powerful when achieving the desired frequency dependent loop gain while ensuring closed-loop stability. However, to achieve closed-loop stability two kinds of nonlinearities have to be taken into account. Those are nonlinear material characteristics of the engine mounts and large angular engine displacements. It is demonstrated how the adopted design strategy facilitates the investigation of the latter nonlinearity's impact on closed-loop characteristics. To deal with the nonlinearities, gain scheduling has been used.

Mimo Process Identification for Controller Design: Test Signals, Nominal Model and Error Bounds

Advanced MIMO (multi-input multi-output) control design techniques need accurate process models. Process identification is an effective way to do industrial process modelling. In this work, some problems in MIMO process identification are studied, where the purpose of identification is control system design. First a recent result on prediction error method identification will be introduced which is the theoretical basis of the work. Then a procedure of MIMO process identification is proposed which consists basically high order model estimation and model reduction. The method of identification is numerically simple and reliable. A special feature of the method is that it can not only estimate a nominal parametric model of the process, but also supply an upper bound of the model uncertainty (modelling errors) in the frequency domain. Input design can be solved easily in this approach. The identified nominal model and error bound can be readily used for robust control system analysis and design. An industrial application is presented to validate our method.

Design of autopilots for wingless missiles

A non-linear multi-input multi-output controller design technique is applied to control a wingless missile. It is readily demonstrated that this design is unique in the sense that it not only results in fast decoupled transient behaviour, but also that it can be extended simply to give safe control when one of the actuators is limited in rate or deflection.