M.D. Brown

Nonlinear Dynamic Matrix Control using Local Models

This paper proposes using a Local Model (LM) network representation of a nonlinear chemical process as a basis for nonlinear Dynamic Matrix Control (DMC). The LM network is composed of local linear ARX models and is trained using a hybrid learning approach. The nonlinear DMC uses step responses for different process operating points extracted from the LM network, rather than the single-step response model of linear DMC. Simulation studies of a pH neutralisation process indicate improve ments in both set point tracking and disturbance rejection.

MULTI-PROCESSOR ARCHITECTURE FOR IMPLEMENTATION OF AN AUTOMATIC VOLTAGE REGULATOR

Abstract The paper describes the design and implementation of a digital self-tuning automatic voltage regulator for excitation control of a synchronous generator. Practical aspects such as controller robustness and lack of sufficient processing power are discussed. The minimum sample intervals possible with conventional microprocessor technology fall short of specification by a factor of approximately four. A parallel architecture using INMOS transputers combined with an advanced VME bus-based measurement system provide an open-ended solution to this problem. The scheme is initially applied to a computer simulation of a three-phase turboalternator. A physical simulation is then used to test the long term reliability of the system under realistic conditions.

Transputer implementation of adaptive control for a turbogenerator system

Abstract The paper describes the design and implementation of digital self-tuning automatic voltage regulators for excitation control of a synchronous generator. Practical aspects such as controller robustness and processing power are discussed. Conventional microprocessor technology is unable to achieve the sampling periods required for industrial application. A parallel architecture using INMOS transputers combined with an advanced VME bus-based measurement system provide an open-ended solution to this problem, and enable the adaptive controllers to operate within the required 10 ms sample period. The scheme was initially evaluated on a computer simulation of a three-phase turboalternator. A laboratory model turbogenerator was then controlled to test the performance and reliability of the system under realistic conditions.

Nonlinear Internal Model Control using Local Model Networks

Abstract Local Model Networks represent a nonlinear dynamical system by a set of locally valid submodels across the operating range. Training such feedforward structures involves the combined estimation of the submodel parameters and those of the interpolation functions. The paper describes a new hybrid learning approach for local model networks that uses a combination of singular value decomposition and second order gradient optimization. A new nonlinear Internal Model Control scheme is proposed which has the important property that the controller can be derived analytically. Simulation studies of a pH neutralization process confirm the excellent modelling and control performance using the local model approach.

Parallel Self-Tuning Controllers for Turbogenerators

Abstract The paper describes the practical application of parallel processing for adaptive control of a laboratory turbogenerator system. A VME system with transputers is used to provide a flexible hardware structure, and to achieve fast sampling rates for multi-loop and multivariable self-tuning controllers. Experimental results show that improved control may be obtained, and confirm the robustness of the controllers

Parallel Adaptive Control for Turbogenerator Systems

Abstract This paper describes the design and implementation of digital self-tuning controllers for turbogenerator AVR and governor systems. Practical aspects such as controller robustness, lack of. sufficient processing power, and algorithm mapping issues are discussed. A parallel architecture using Inmos transputers combined with an advanced VME bus-based measurement system provides a solutIOn to these problems and offers the control systems designer the opportunity to implement complex parallel algorithms. Simulation results, involving both SISO and multi-loop self-tuning regulators on a network of T800 transputers, are presented together with implementation results on a laboratory scale microalternator and comparisons with a fixed-gain AVR.