Water Bath Temperature Control System Research Articles

Waterbath Temperature Control System with Fuzzy Logic

Waterbath Temperature Control System with Fuzzy Logic

Effect of Rotary Evaporator Water Bath Temperature on Recovery Rate of Phthalate Esters.

To clarify the influence of water bath temperature on the decompression and concentration efficiency of a rotary evaporator, accurate control of the water bath temperature is required. Therefore, the water bath temperature control system of a rotary evaporator was studied using the recovery rate of phthalate esters (PAEs) as the indicator. The results showed that when the temperature of the rotary evaporator was set at 40°C, the actual temperature range of the concentrated liquid would change from 26-41 to 39-41°C. By modifying the water bath temperature control system, the recovery rate of PAEs could be increased from 72.4% to 85.6% to 93.2%-98.5%. This test indicates that temperature has a great influence on vacuum concentration, and the recovery rate of PAEs can be improved by modifying the water bath temperature control system used in the rotary evaporator.

Application of Intelligent Track Guiding Control Algorithm on the Temperature Control of the Water Bath of a Specific Container

Aiming at the water bath temperature control system of great inertia, pure time-delay, non-linear and time changeable characteristics, a kind of simple and practical control algorithm is put forward in this paper：ITGC (Intelligent Track Guiding Control). ITGC and PID control system was carried out respectively step response test and the disturbance test in the MATLAB/SIMULINK simulation platform; And can be successfully applied in practical projects. Studies have shown that: ITGC control on the overshoot and adjustment time are better than the conventional PID method, the control scheme has stronger robustness and anti-jamming capability under various operating conditions and an increase in external disturbance case. Successfully resolved the difficulty of time-varying parameters and large lag in the temperature control system.

Comparison of ANN Controller and PID Controller for Industrial Water Bath Temperature Control System using MATLAB Environment

Artificial Neural Network is an effective tool for highly nonlinear system. With the advent of high-speed computer system, there is more increased interest in the study of nonlinear system. Neuro control algorithm is mostly implemented for the application to robotic systems and also some development has occurred in process control systems. Process Control systems are often nonlinear and difficult to control accurately. Their dynamic models are more difficult to derive than those used in aerospace or robotic control, and they tend to change in an unpredictable way. This paper gives an example where a multilayered feed forward back propagation neural network is trained offline to perform as a controller for a temperature control system with no a priori knowledge regarding its dynamics. The inverse dynamics model is developed by applying a variety of input vectors to the neural network. The performance of neural network based on these input vectors is observed by configuring it directly to control the process. In this paper, we have compared the performance of PID controller with ANN [1] based upon Set point change, Effect of load disturbances and Processes with variable dead time. The result shows that ANN outperforms the PID controller.

Design of Water Bath Temperature Control System Based on DS18B20

The features and working principle of single-wire digital temperature sensor DS18B20 are introduced. Through applying single chip microcontroller AT89C51, the water bath temperature control system is designed based on DS18B20. The paper also gives the structure of the hardware device, main design of software and the C51 program of DS18B20. In the end, the experimental result shows that the DS18B20 system has a higher accuracy.

Fuzzy systems design by clustering-aided ant colony optimization for plant control

This paper proposes fuzzy systems design by clustering-aided ant colony optimization (ACO) algorithm (CACO). The objective of CACO is to improve both the design efficiency of fuzzy systems and its performance. In CACO, structure of a fuzzy system, including the number of rules and fuzzy sets in each input variable, is created on-line by a newly proposed fuzzy clustering. In contrast to conventional grid-type partition, the antecedent part of a fuzzy system is flexibly partitioned, and the phenomenon of highly overlapped fuzzy sets is avoided. Once a new rule is generated, the consequence is selected from a list of candidate control actions by ACO. In ACO, the route of an ant is regarded as a combination of consequent actions selected from every rule. A pheromone matrix among all candidate actions is constructed and an on-line learning algorithm for heuristic value update is proposed. Searching for the best one among all consequence combinations involves using the pheromone matrix and heuristic values. To verify the performance of CACO on fuzzy systems design, simulations on nonlinear system control, water bath temperature control and chaotic system control are performed. Simulations on these problems and comparisons with other algorithms have demonstrated the performance of CACO.

How to optimize the TS‐fuzzy knowledge base to achieve desired performances: Accuracy and robustness

AbstractDesigning an effective criterion/learning to find the best rule and optimal structure is a major problem in the design process of fuzzy neural controller. In this paper, we introduce a new robust model of Takagi Sugeno fuzzy logic controller. A hybrid learning algorithm, called hybrid approach to fuzzy supervised learning (HAFSL), which combines the genetic algorithm (GA) and gradient descent technique (GD) is proposed for constructing an efficient and robust fuzzy neural network controller (FNNC). Two phases of design and learning process are presented in this work. A GA is used for finding near optimal structure/parameters of the FNNC that minimizes the number of rules (initialization procedure). The second stage of learning algorithm uses the backpropagation algorithm based on GD method to fine tune the consequent parameters of the controller. The genes of chromosome are arranged into two parts, the first part contains the control genes (the certainty factors) and the second part contains the parameters genes that representing the fuzzy knowledge base. The effectiveness of this chromosome formulation enables the fuzzy sets and rules to be optimally reduced. The performances of the HAFSL are compared to these found by the traditional PI with genetic optimization (GA‐PI). Simulations demonstrate that the proposed HAFSL and GA‐PI algorithms have good generalization capabilities and robustness on the water bath temperature control system. Copyright © 2007 John Wiley & Sons, Ltd.

Modified Functional Link ANN based Controller for Thermal Processes

In this paper, Modified Functional Link ANN (M-FLANN) based controller has been designed and implemented on simulated water bath temperature control system. The performance of this controller has been compared with Multilayer Perceptron (MLP), Direct Linear Feed-through ANN (DLFANN), Functional Link ANN (FLANN) and Fuzzy Logic (FLC) and proportional-integral-derivative (PID) controllers on the same process. Their performances have been evaluated under identical conditions with respect to set-point regulation, effect of unknown disturbances and variable lag times. It has been found that the M-FLANN controller has better performance compared to other controllers. The results have been further confirmed by implementing and comparing the four ANN controllers on Continually Stirred Tank Heater (CSTH); a multi-input multi-output (MIMO) process.

THE KNOWLEDGE-BASED FUZZY RULES EMULATED NETWORK AND ITS APPLICATIONS ON DIRECT ADAPTIVE ON NONLINEAR CONTROL SYSTEMS

This paper proposes an adaptive network architecture, which can emulate the human knowledge as the fuzzy logic rule, and its applications as the controller for nonlinear systems. The structure of this proposed network, multi-input Fuzzy Rule Emulated Network or FREN, is derived based on human knowledge in the form of fuzzy IF-THEN rules. The initial setting of its parameters can be intuitively chosen from expert's experience. During the learning phase based on the gradient search, the learning rate can be adapted itself to remain the stability with the Lyapunov method. The performance of our network is presented by using this network as controller for the single invert pendulum plant and the water bath temperature control system. The comparison results with other conventional control algorithms such as artificial neural networks and PID controllers can be illustrated in each example.

Efficient reinforcement learning through dynamic symbiotic evolution for TSK-type fuzzy controller design

In this paper, efficient reinforcement learning through dynamic-form symbiotic evolution (DSE) is proposed for solving nonlinear control problems. Compared with traditional symbiotic evolution, DSE uses the sequential search-based dynamic evolution (SSDE) method to generate an initial population and to determine dynamic mutation points. Therefore, better chromosomes will be initially generated while better mutation points will be determined for performing dynamic mutation. The proposed DSE design method was applied to different control systems, including the cart-pole balancing system and the water bath temperature control system, and control problems were simulated on these systems. The proposed DSE method was verified to be efficient and superior for solving these control problems and from comparisons with some traditional genetic algorithms.

A GA-based neural fuzzy system for temperature control

This paper addresses the structure and an associated learning algorithm of a feedforward multilayered connectionist network for realizing the basic elements and functions of a traditional fuzzy logic controller. The genetic algorithm-based neural fuzzy system (GA-NFS) is based on Takagi–Sugeno–Kang (TSK) type model possessing a neural network's learning ability. A hybrid learning algorithm is proposed for parameters learning. The proposed algorithm combines the genetic algorithm (GA) and the least-squares estimate (LSE) method to construct the GA-NFS. The genetic algorithm is used to tune membership functions at the precondition part of fuzzy rules while the LSE method is used to tune parameters at the consequent part of fuzzy rules. The performance of the GA-NFS is compared to that of the traditional PID controller and fuzzy logic controller on the water bath temperature control system.

Genetic reinforcement learning through symbiotic evolution for fuzzy controller design

An efficient genetic reinforcement learning algorithm for designing fuzzy controllers is proposed in this paper. The genetic algorithm (GA) adopted in this paper is based upon symbiotic evolution which, when applied to fuzzy controller design, complements the local mapping property of a fuzzy rule. Using this Symbiotic-Evolution-based Fuzzy Controller (SEFC) design method, the number of control trials, as well as consumed CPU time, are considerably reduced when compared to traditional GA-based fuzzy controller design methods and other types of genetic reinforcement learning schemes. Moreover, unlike traditional fuzzy controllers, which partition the input space into a grid, SEFC partitions the input space in a flexible way, thus creating fewer fuzzy rules. In SEFC, different types of fuzzy rules whose consequent parts are singletons, fuzzy sets, or linear equations (TSK-type fuzzy rules) are allowed. Further, the free parameters (e.g., centers and widths of membership functions) and fuzzy rules are all tuned automatically. For the TSK-type fuzzy rule especially, which put the proposed learning algorithm in use, only the significant input variables are selected to participate in the consequent of a rule. The proposed SEFC design method has been applied to different simulated control problems, including the cart-pole balancing system, a magnetic levitation system, and a water bath temperature control system. The proposed SEFC has been verified to be efficient and superior from these control problems, and from comparisons with some traditional GA-based fuzzy systems.

Water bath temperature control with a neural fuzzy inference network

Although multilayered backpropagation neural networks (BPNN) have demonstrated high potential in the nonconventional branch of adaptive control, its long training time usually discourages their applications in industry. Moreover, when they are trained on-line to adapt to plant variations, the overtuned phenomenon usually occurs. To overcome the weakness of the BPNN, we propose a neural fuzzy inference network (NFIN) in this paper suitable for adaptive control of practical plant systems in general, and for adaptive temperature control of a water bath system in particular. The NFIN is inherently a modified TSK (Takagi–Sugeno–Kang)-type fuzzy rule-based model possessing neural network's learning ability. In contrast to the general adaptive neural fuzzy networks, where the rules should be decided in advance before parameter learning is performed, there are no rules initially in the NFIN. The rules in the NFIN are created and adapted as on-line learning proceeds via simultaneous structure and parameter identification. The NFIN has been applied to a water bath temperature control system. As compared to the BPNN under the same training procedure, the control results show that not only can the NFIN greatly reduce the training time and avoid the overtuned phenomenon, but the NFIN also has perfect regulation ability.

Temperature control with a neural fuzzy inference network

Although multilayered backpropagation neural networks (BPNNs) have demonstrated high potential in adaptive control, their long training time usually discourages their applications in industry. Moreover, when they are trained online to adapt to plant variations, the over-tuned phenomenon usually occurs. To overcome the weakness of the BPNN, we propose a neural fuzzy inference network (NFIN) suitable for adaptive control of practical plant systems in general and for adaptive temperature control of a water bath system in particular. The NFIN is inherently a modified Takagi-Sugeno-Kang (TSK)-type fuzzy rule based model possessing a neural network's learning ability. In contrast to the general adaptive neural fuzzy networks, where the rules should be decided in advance before parameter learning is performed, there are no rules initially in the NFIN. The rules in the NFIN are created and adapted as online learning proceeds via simultaneous structure and parameter identification. The NFIN has been applied to a practical water bath temperature control system. As compared to the BPNN under the same training procedure, the simulated results show that not only can the NFIN greatly reduce the training time and avoid the over-tuned phenomenon, but the NFIN also has perfect regulation ability. The performance of the NFIN is compared to that of the traditional PID controller and fuzzy logic controller (FLC) on the water bath temperature control system. The three control schemes are compared, with respect to set point regulation, ramp-point tracking, and the influence of unknown impulse noise and large parameter variation in the temperature control system. The proposed NFIN scheme has the best control performance.

Temperature regulation with neural networks and alternative control schemes

In this article, we compare the neuro-control algorithm to three other control algorithms: fuzzy logic control, generalized predictive control, and proportional-plus-integral control. Each of these four algorithms is implemented on a water bath temperature control system. The four systems are compared through experimental studies under identical conditions with respect to set-point regulation, the effect of unknown load disturbances, large parameter variation, and variable deadtime in the system. It is found that the neurocontrol system compares well with the other three control systems and offers encouraging advantages. From the results of the experimental studies, however, the best characteristics of each of these different classes of control systems may be combined for realizing a more efficient and intelligent control scheme.

Fuzzy Logic Control

Although fuzzy logic theory was proposed about three decades ago, it is only until recently that fuzzy control technology has been successfully applied to many industrial systems and domestic appliances. Much of these developments is mainly due to a better understanding of the concept of fuzzy logic, its simplicity of implementation, and its feasibility in hardware development. This article intends to provide a simple but clear understanding on the concept of fuzzy logic and its application to control systems (therefore not intended for those who have already understood the basic principles of fuzzy logic control). A simulation example is given on the development and operation of a fuzzy logic controller for a multi-variable water bath temperature control system. C-pseudocodes are given in the Appendices to clarify the water bath fuzzy control algorithms.