R.N. Silva

ADAPTIVE CONTROL OF A SOLAR ENERGY PLANT: EXPLOITING ACCESSIBLE DISTURBANCES

This paper deals with temperature control in an industrial-scale distributed collector solar field. The need for adaptive control arises from the highly time-varying behaviour of both the plant dynamics and the solar radiation effects in the collector field due to atmospheric changes. The control problem consists of keeping constant the field outlet oil temperature by acting on the oil flow. The manipulated variable is chosen so as to minimize a receding horizon quadratic cost. The underlying control law is of model-based predictive control type, adaptively implemented by a MUSMAR algorithm, modified so as to exploit the information conveyed by the accessible disturbances. Experimental results are reported showing the significance on the achievable performance of the feedforward term depending on the accessible disturbances.

Reduced complexity adaptive nonlinear control of a distributed collector solar field

Abstract An approach to the control of a distributed collector solar field relying on feedback linearization, Lyapunov based adaptation and a simplified plant model is presented. The control objective consists of manipulating the oil flow so that the outlet oil temperature is regulated around a given setpoint. For dealing with plant nonlinearities and external disturbances, a nonlinear transformation is performed on the accessible variables such that the transformed system behaves as an integrator, to which linear control techniques are then applied. Since the transformation depends on an unknown parameter, an adaptation law is designed so as to minimize a Lyapunov function for the whole systems state. For the sake of control synthesis a simplified plant model which retains the bilinear nonlinearity is employed. The resulting control law has the same control structure of the one yielding exact input-output linearization but assumes a different placement of a temperature sensor. In order to justify this procedure, plant internal dynamics is studied. Experimental results obtained in the actual field are presented.

Adaptive regulation of super-heated steam temperature: a case study in an industrial boiler

Abstract This paper describes the application of the MUSMAR predictive adaptive controller to the regulation of super heated steam temperature in a commercial boiler. The boiler considered produces 150 t / h of steam at maximum load, used both for electric energy production in a turbine and industrial use. The combination of predictive and adaptive techniques, relying on multiple models redundantly estimated, allows a continuous adjustment of the controller tuning for tracking plant dynamics variations. This paper describes experiments actually performed on the plant with adaptive predictive control, in particular in the presence of load changes. A reduction of steam temperature fluctuations with respect to an optimized cascade of PI controllers is observed.

Cascade control of a distributed collector solar field

Abstract This paper reports experimental results on the cascade control of a distributed collector solar field. The control problem consists of keeping constant the field outlet oil temperature by acting on the circulating oil flow used for heat transfer. In the inner loop an adaptive model based predictive controller exploiting the information conveyed by accessible disturbances (radiation changes and inlet oil temperature) is used, while in the outer loop a PID is employed. The need for adaptive control arises from the time varying behaviour of the plant. Due to the generality of the methods employed, the experience reported is relevant to a wide class of industrial processes.

Time scaling internal state predictive control of a solar plant

The control of a distributed collector solar field is addressed in this work, exploiting the plants transport characteristic. The plant is modeled by a hyperbolic type partial differential equation (PDE) where the transport speed is the manipulated flow, i.e. the controller output. The model has an external distributed source, which is the solar radiation captured along the collector, approximated to depend only of time. From the solution of the PDE, a linear discrete state space model is obtained by using time-scaling and the redefinition of the control input. This method allows overcoming the dependency of the time constants with the operating point. A model-based predictive adaptive controller is derived with the internal temperature distribution estimated with a state observer. Experimental results at the solar power plant are presented, illustrating the advantages of the approach under consideration.

A dual approach to start-up of an adaptive predictive controller

This paper is concerned with the smoothing of start-up adaptation transients when controlling a priori unknown plants with an adaptive predictive control algorithm. The MUSMAR algorithm embodying an adaptive predictive controller relying on multiple models is considered. The approach followed consists of a dual modification of MUSMAR, including a bicriterial optimization which takes into account both the control objective and the parameter estimation criteria. The advantages of the proposed algorithm in what concerns reduction of the start-up adaptation transient are illustrated by experimental results obtained for the temperature control of a distributed solar collector energy plant.

A methodology for impact evaluation of alternative control strategies in a large-scale power plant

Abstract This work is concerned with a methodology for impact evaluation of alternative industrial process-control strategies applied to the steam generator of a 125 MW fuel-fired thermoelectric power-plant unit. The methodology employed is described. It relies on a “systems” approach and embodies nonlinear plant modelling and simulation. The technique used to build the model is presented, as well as some comparisons between different control structures based on classical and predictive/adaptive control strategies. A case study is presented which is distinguished by the availability of a rich set of experimental data, allowing detailed model tuning and validation. The methodology followed forms a paradigm which may be applied to other types of industrial process.

Fault diagnosis based on black-box models with application to a liquid-level system

This paper proposes an on-line robust approach to fault detection and isolation (FDI) of dynamic systems. This FDI approach is based on black-box models: artificial neural networks (ANNs) and the autoregressive with exogenous input (ARX) models. ANNs are used as observers and pattern classifiers, and adaptive ARX models are used as observers. The generalized likelihood ratio (GLR) algorithm is used for change detection. Process faults are considered, and the robust FDI problem is also addressed. The approach is applied to a laboratory set-up tank system under closed-loop control.

Controlling Distributed Hyperbolic Plants with Adaptive Nonlinear Model Predictive Control

A number of plants of technological interest include transport phenomena in which mass, or energy, or both, flow along one space dimension, with or without reactions taking place, but with neglected dispersion. This type of processes are described by hyperbolic partial differential equations [4] and is receiving an increasing attention in what concerns the application of Predictive Control [6]. Two examples considered are distributed collector solar fields [3, 10] and tubular bioreactors [5]. In both cases the manipulated variable is assumed to be the flow. For lack of space, only the first example is considered hereafter.