Hybrid fuzzy predictive control based on genetic algorithms for the temperature control of a batch reactor (original) (raw)
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IFAC Proceedings Volumes, 2008
The paper deals with model predictive control (MPC) of nonlinear hybrid systems with discrete inputs. It is often required to take into account the hybrid and/or nonlinear nature of real systems, therefore, a hybrid fuzzy model is used for MPC in the paper. Two approaches that are suitable for MPC of nonlinear hybrid systems with discrete inputs are compared on a batch reactor example: a branch & bound and a genetic algorithm. We have established that both algorithms are suitable for controlling such systems. The main advantages of the genetic algorithm are boundedness of computational time in one step and whole computation-efficiency, whereas the main drawbacks are its inherent sub-optimality and the need for suitably tuned parameters. On the other hand, the branch & bound approach does not require parameter tuning and using a suitable cost function provides optimal results in considerably less time than an explicit enumeration method.
Hybrid fuzzy predictive control of a batch reactor using a branch and bound and a genetic algorithm
2008
The paper deals with model predictive control (MPC) of nonlinear hybrid systems with discrete inputs. It is often required to take into account the hybrid and/or nonlinear nature of real systems, therefore, a hybrid fuzzy model is used for MPC in the paper. Two approaches that are suitable for MPC of nonlinear hybrid systems with discrete inputs are compared on a batch reactor example: a branch & bound and a genetic algorithm. We have established that both algorithms are suitable for controlling such systems. The main advantages of the genetic algorithm are boundedness of computational time in one step and whole computation-efficiency, whereas the main drawbacks are its inherent sub-optimality and the need for suitably tuned parameters. On the other hand, the branch & bound approach does not require parameter tuning and using a suitable cost function provides optimal results in considerably less time than an explicit enumeration method.
Fuzzy-model-based hybrid predictive control
Isa Transactions, 2009
In this paper we present a method of hybrid predictive control (HPC) based on a fuzzy model. The identification methodology for a nonlinear system with discrete state-space variables based on combining fuzzy clustering and principal component analysis is proposed. The fuzzy model is used for HPC design, where the optimization problem is solved by the use of genetic algorithms (GAs). An illustrative experiment on a hybrid tank system is conducted to demonstrate the benefits of the proposed approach.
Application of Predictive Control to a Batch Reactor
2003
The REPSOL company had in mind the improvement of the control on one of their chemical reactors. A feasibility study for the implementation of an Advanced Control technique (Predictive Control for temperature control for chemical Reactors PCR) for a batch reactor for Polyols production has been performed. The proposed technique PCR is based on a dynamic model of the unit which makes the prediction of the process variables behaviour. That behaviour is specified in terms of closed loop time response through a desired future trajectory. The control system shows a substantial improvement in the reactor temperature controllability and a notorious elimination of the competition between the cooling and heating actuators. The improvement is partially explained by the distinction made by the model and the control modules between the heating and the cooling dynamic effects which are usually quite different. Among the origins of the benefits obtained by such a model based predictive control, e...
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Fuzzy control and predictive control techniques are two modern control strategies that have been accepted by the industry to solve complex problems. Everyday the industry demands control strategies that can deliver better performance for several operating points and these requirements have motivated the development of the theory of Nonlinear Model Predictive Control. This type of controllers can be implemented using fuzzy models. The present paper presents 4 algorithms to construct the controllers. The comparison between the algorithms includes complexity, computational load, model representation, quality of the solution. The controllers are compared using a model of a chemical process (Continuous Stirred Tank Reactor).
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Fuzzy control systems and predictive control techniques has been widely accepted for industrial applications. Fuzzy modeling has received particular attention with in the industry and the nance sectors due to the qualitative insight about the behaviour of the modeled process. Predictive control had been focused on linear systems and time variant linear systems (Adaptive Predictive Control). These adaptive techniques demand slow changes on the parameters to achieve a good performance. This situation generates the necessity of Predictive Control Techniques based on Non-linear Models. This paper presents an algorithm for multivariable predictive control using fuzzy models. The algorithm converts the non-linear program needed to solve the optimal control problem for nonlinear plants, into a quadratic program with a fast solution. The e ectiveness of the algorithm is demostrated by a simulation of a steam generating unit used for electricity generation.
2010 5th IEEE Conference on Industrial Electronics and Applications, 2010
A switching scheme based on Takagi-Sugeno fuzzy inference system is proposed in this paper to address the problems of poor transient response, rapid oscillation of plant output and long duration of switching time associated with the multiple model predictive control (MMPC) based on hard switching. A set of piecewise linear models is used to represent the system under consideration at different operating regimes. Corresponding MPC local controller is developed for each model. At each instant, the fuzzy switching system selects the appropriate model/controller pair for the system. The proposed MMPC strategy is applied to a coagulation chemical dosing unit for water purification plants. Simulation results of the proposed control scheme are promising and positive.
Chemical Product and Process Modeling, 2008
The processes that take place in industrial reactors are mainly highly nonlinear and, due to this, their controls have become a major factor in determining the aspect of process safety, quality, and productivity of these systems. For this reason, we have designed an inexpensive, practical reactor on a pilot plant scale to conduct such online control studies where the heat of reaction in the reactor is simulated by injecting steam into the reactor system. This paper highlights the development of the pilot plant with its software development to implement advanced control strategies using artificial intelligence approaches such as fuzzy logic and genetic algorithm. The online implementation results conclude that the Genetic Algorithm Model Based Controller (GAMBC) gave similar performance with the Fuzzy Logic Controller (FLC) for the set point tracking studies but in the load disturbance rejection studies, it was found that the FLC performed better. The results obtained show the useful...