Yancho Todorov

Volterra model predictive control of a lyophilization plant

Lyophilization plants are widely used by pharmaceutical industries to produce stable dried medications and important preparations. Since, a Lyophilization cycle involves a high energy demands it is needed to be used an improved control strategy in order to minimize the operating costs. This paper describes a method for designing a nonlinear model predictive controller to be used in a Lyophilization plant. The controller is based on a truncated fuzzy-neural Volterra predictive model and a simplified gradient optimization algorithm. The proposed approach is studied to control the product temperature in a Lyophilization plant. The efficiency of the proposed approach is tested and proved by simulation experiments.

Modeling of Chaotic Time Series by Interval Type-2 NEO-Fuzzy Neural Network

This paper describes the development of Interval Type-2 NEO-Fuzzy Neural Network for modeling of complex dynamics. The proposed network represents a parallel set of multiple zero order Sugeno type approximations, related only to their own input argument. The induced gradient based learning procedure, adjusts solely the consequent network parameters. To improve the robustness of the network and the possibilities for handling uncertainties, Type-2 Gaussian fuzzy sets are introduced into the network topology. The potentials of the proposed approach in modeling of Mackey-Glass and Rossler Chaotic time series are studied.

Implementations of a Hammerstein fuzzy-neural model for predictive control of a lyophilization plant

This paper describes two methodologies for implementation of Hammerstein model by using different input-output representations into model predictive control schemes. The model nonlinearity is easily approximated using a simple Takagi-Sugeno inference, while the linear parts are flexibly introduced. As optimization procedures for predictive control are used a standard gradient optimization method and an implementation of Hildreth Quadratic Programming. A comparison between the proposed control strategies is made by simulation experiments for control of nonlinear lyophilization plant.

MODEL PREDICTIVE CONTROL OF A LYOPHILIZATION PLANT: A SIMPLIFIED APPROACH USING WIENER AND HAMMERSTEIN SYSTEMS

Lyophilization process is widely used in pharmaceutical industries, preparing stable dried medications and important biopreparations, so they remain stable and easier to store at room temperature. Since a lyophilization cycle involves high energy demands, an improved control strategy has to be used in order to minimize the operating costs. This paper deals with the design methodology of nonlinear model predictive controllers for lyophilization plant. The controllers are based on fuzzy-neural predictive models and simplified gradient optimization algorithm. As predictive models, fuzzy-neural implementations of Hammerstein and Wiener-Hammerstein systems are used. Such structures provide fast and reliable system identification using small number of parameters which reduces the computational burden during the optimization procedure. The potential benefits of the proposed approaches are demonstrated by simulation experiments.

Fuzzy-neural model predictive control of a building heating system

Abstract This paper describes the development of a Model Predictive Controller with supervision control of a building heating system. A fuzzy–neural model and optimizing procedure as a part of a nonlinear predictive controller are utilized on-line to determine the future values of control actions based on dependence between outdoor and indoor temperatures. A learning algorithm for parameters in fuzzy-neural implementation of the predictive model is additionally applied. Simulation results with a model of a single room heating system demonstrate that a better system performance can be achieved in comparison to classical PID control.

Adaptive supervisory tuning of nonlinear model predictive controller for a heat exchanger

Abstract It is presented in this paper an adaptive predictive supervisory algorithm to the temperature control of a heating system with a heat exchanger. The nonlinear predictive control strategy is designed on the basis of a Takagi-Sugeno fuzzy-neural model and a simple optimization procedure. An additional supervisory level in the control system is introduced for adaptive tuning of a weighting factor in the predefined optimization criterion. Using the proposed algorithm a higher system performance can be achieved which leads to reduction of the energy consumption into the heating system. The proposed approach is studied by experimental simulations to control a temperature in the heating system.

State-space fuzzy-neural network for modeling of nonlinear dynamics

This paper describes a novel idea for designing a fuzzy-neural network for modeling of nonlinear system dynamics. The presented approach assumes a state-space representation in order to obtain a more compact form of the model, without statement of a great number of parameters needed to represent a nonlinear behavior. To increase the flexibility of the network, simple Takagi-Sugeno inferences are used to estimate the current system states, by a set of a multiple local linear state estimators. Afterwards, the output of the network is defined, as function of the current and estimated system parameters. A simple learning algorithm based on two step Gradient descent procedure to adjust the network parameters, is applied. The potentials of the proposed modeling network are demonstrated by simulation experiments to model an oscillating pendulum and a nonlinear drying plant.

Fuzzy-neural predictive control using Levenberg-Marquardt optimization approach

It is proposed in this paper a study on the influence of the Levenberg-Marquardt optimization approach for computation of the control actions in Nonlinear Model Predictive Controller. To predict the future plant behavior, a classical Takagi-Sugeno inference is used. A comparison by applying the Gradient descent and the Newton-Raphson optimization approaches is made. The efficiency of the proposed optimization strategies is demonstrated by experiments in MATLAB environment to control a Continuous Stirred Tank Reactor.

Input space selective fuzzification in intuitionistic semi fuzzy-neural network

In this paper, the influence of the selective fuzzification of the input space in Intuitionistic Semi-Fuzzy Neural Network (ISFNN) is investigated. The ISFNN represents a structure modification of the classical fuzzy-neural approach where selective fuzzification as a means to reduce the number of the generated fuzzy rules is proposed, thus expected to reduce the number of the associated learning parameters and to achieve a degree of computational simplicity. On the other hand, the potentials of the network are supplemented by intuitionistic fuzzy logic, in order to handle uncertain data variations. As a learning procedure for the proposed structure, a two-step gradient descent algorithm is employed. To investigate the influence of input space fuzzificaton, several test experiments in modeling of a two benchmark chaotic systems — Mackey-Glass and Rossler chaotic time series are made.

Intuitionistic Neo-Fuzzy predictive control

This paper presents an implicit predictive control strategy based on Intuitionistic Neo-Fuzzy predictor, as a first attempt to investigate the potentials of the intuitionistic fuzzy logic for the purpose of control applications. The proposed predictor represents a simple fuzzy-neural network as fusion from the concepts of the intuitionistic fuzzy logic, the neo-fuzzy neuron theory and the classical Takagi-Sugeno inference mechanism. The predictions are then coupled into generalized predictive control scheme where a standard quadratic control cost function is minimized over a set of predefined horizons. For simplicity, the considered process variables and the calculated output control sequence are iteratively bounded instead of explicitly constrained, in order to investigate the computational procedures related to implementation of an intuitionistic fuzzy logic. To investigate the potentials of the proposed predictive control approach, numerical experiments to control a Continuous Stirred Tank Reactor (CSTR) under uncertain conditions are studied.