P. Schroder

On-line evolution of robust control systems: an industrial active magnetic bearing application ☆

Abstract An electrical pump running on active magnetic bearings (AMBs) is described.To overcome the difficulty of obtaining an accurate model of the system, the control system is designed on-line. An optimiser modifies controller parameters running on the controller (a digital signal processor) in real time and the resulting sensor data is recorded and processed. On-line optimisation of the controller is achieved from within a commercial CACSD software package using H 2 and H ∞ measures of tracking as design objectives. A multiobjective genetic algorithm is used to drive the optimisation for the AMB system. The resulting controller is compared with that of a manually tuned controller and significant improvements in performance and robustness are observed.

Multi-objective optimization approach to the ALSTOM gasifier problem

Abstract A control system design procedure based on the optimization of multiple objectives is used to realize the control design specifications of the linear gasification plant models. A multi-objective genetic algorithm (MOGA) is used in conjunction with an H∞ loop-shaping design procedure (LSDP) in order to satisfy the requirements of this critical system. The H∞ LSDP is used to guarantee the stability and robustness of the controller while its associated weighting matrix parameters are selected using the multi-objective search method in order to achieve performance requirements. A controller emerges which is stable but unable to completely meet some of the control objectives. Despite this shortcoming, the study is an excellent vehicle for introduction to an effective H∞ loop-shaping procedure. Further work, beyond the scope of this challenge has subsequently produced an improved controller design.

Fault tolerant control of active magnetic bearings

A nonlinear model of a Rolls-Royce turbomachines rotor supported by active magnetic bearings (AMBs) is presented. The model has the capability to model magnetic bearing systems of a variety of different configurations. A multiobjective genetic algorithm (MOGA) is used to design PID controllers for the AMB system with five different bearing configurations. A centralised fault compensation scheme is demonstrated and shown to perform more effectively than a decentralised control system without such a scheme. The fault tolerance characteristics of the five different bearing structures are compared under both decentralised and centralised control.

On-Line Genetic Auto-Tuning of PID Controllers for an Active Magnetic Bearing Application

Abstract A prototype large electrical machine running on active magnetic bearings is described. This rig is controlled by a digital signal processor connected by a custom interface to MATLAB/Simulink hosted by a PC. The on-line tuning of a PID controller is set up as an optimisation problem from MATLAB and a multiobjective genetic algorithm is used to drive the optimisation. The results of an optimisation are presented and analysed.

An environment for on-line multiobjective tuning of controllers

Abstract A MATLAB-based rapid controller prototyping and development system for on-line tuning is presented. This is capable of automatically generating executable code for a digital signal processor from a Simulink specification of a controller and also has a real-time parameter adjustment and data logging facility. An optimisation problem can therefore be formulated in MATLAB with the controller parameters as decision variables and direct measures of the controllers performance as optimisation objectives. A multiobjective genetic algorithm is used as an optimisation engine to perform on-line tuning for the controller of an active magnetic bearing system. The optimisation objective function is based on on-line H ∞ and H 2 , measures of controller performance.