T. K. Radhakrishnan

Soft sensor based composition estimation and controller design for an ideal reactive distillation column

In this research work, the authors have presented the design and implementation of a recurrent neural network (RNN) based inferential state estimation scheme for an ideal reactive distillation column. Decentralized PI controllers are designed and implemented. The reactive distillation process is controlled by controlling the composition which has been estimated from the available temperature measurements using a type of RNN called Time Delayed Neural Network (TDNN). The performance of the RNN based state estimation scheme under both open loop and closed loop have been compared with a standard Extended Kalman filter (EKF) and a Feed forward Neural Network (FNN). The online training/correction has been done for both RNN and FNN schemes for every ten minutes whenever new un-trained measurements are available from a conventional composition analyzer. The performance of RNN shows better state estimation capability as compared to other state estimation schemes in terms of qualitative and quantitative performance indices.

Fiber-Optic pH Sensor

The new enhancement in the determination of pH using optical fiber system is described here. This work uses the membrane made of cellulose acetate membrane for reagent immobilization and congo red (pK a 3.7) and neutral red (pK a 7.2) as pH indicators. An effective covalent chemical binding procedure is used to immobilize the indicators. The response time, reversibility, linear range, reproducibility, and long-term stability of fiber optic sensor with congo red as well as neutral red have been determined. The linear range measured for the sensor based on the congo red and neutral red is 4.2–6.3 and 4.1–9.0, respectively. The response time of sensor membrane is measured by varying the substance pH values between 11.0 and 2.0.

Model Based IMC Controller for Processes with Dead Time

Abstract A system with varying transportation lags has been experimentally studied for modeling. Modeling is performed using a step test. The tracer is sodium chloride solution whose conductivity is measured using an online conductivity analyzer. Based on the step response, the model parameters are determined and the lag processes are represented by a first order plus dead time (FOPDT) model. For the models developed, an internal model control (IMC) scheme is designed. Performance comparison, based on rise time, settling time, and overshoot, is done among the designed IMC controllers, conventional PID controllers, and Smith Predictor controllers. The present study depicts that IMC controllers outperform PID and Smith Predictor controllers.

Adaptive Enhanced Genetic Algorithm-Based Proportional Integral Controller Tuning for pH Process

Abstract The control of a pH process is a challenging task. For the pH process, Proportional Integral (PI) control has been successfully used for many years. Tuning of the PI controller is necessary for the satisfactory operation of the system. This paper presents an Enhanced Genetic Algorithm (EGA) for the tuning of PI controllers. To avoid the premature convergence and to reduce the computation time, advanced genetic operators have been used in the proposed GA approach. The proposed GA tuned PI controller is implemented in the experimental pH process. The performance of the controller with Enhanced GA tuning is compared with a traditional Ziegler Nichols tuning and Internal Model control for various set point and trajectory responses of the pH process. The experimental results show that the proposed GA is very much capable to adapt the controller to dynamic plant characteristics changes in pH process.

RECURRENT NEURO FUZZY AND FUZZY NEURAL HYBRID NETWORKS: A REVIEW

An attempt is made to provide a comprehensive survey of the current trends in hybrid recurrent structures of fuzzy logic (FL) and neural networks (NN) for solving temporal problems. Their applications extending to universal approximations are also discussed with available reported literature on recurrent neuro fuzzy networks (RNFN) and recurrent fuzzy neural networks (RFNN).

Model Based Tuning of Humidifying Processes with Transportation Lag

Abstract A humidifying system with varying transportation lag was studied experimentally. Various models were tried and the system fitted a first order plus dead time model with an error of ±5%. The humidity was measured using an on‐line Yokogawa hygrometer. From the model parameters, various controllers, such as PI, Smith predictor, IMC, and IMC PID were analyzed using Matlab. The closed loop performance was studied for both regulator and servo problems. Based on rise time, settling time, and overshoot, the present study concludes that the IMC controller is best suited for this process.

Identification and Control of Bioreactor using Recurrent Networks

Abstract A nonlinear model predictive control (NMPC) strategy based on recurrent neural networks (RNN) is proposed for a single‐input single‐output system (SISO) to control the uncertain nonlinear process. The automatic configuration and modeling of the networks is carried out using a recurrent Elman network using back propagation through time (BPTT) with MATLAB. Identification of the process is performed with a RNN based nonlinear autoregressive with exogenous input (NARX) model and the incorporation of the developed model in the formulation of NMPC is presented. Further, the results of the NMPC is compared with a well tuned IMC based PI controller, which shows a better performance based on the rise time and settling time of the proposed NMPC scheme for the control of an unstable bioreactor.

Control System Design for a Neutralization Process using Block Oriented Models

Abstract This paper focuses on the development of a non‐linear controller for a neutralization process. Block oriented models, namely the Wiener and Hammerstein model structures, are used for the controller design. A neural network architecture that has the capability to model the steady state behavior of a complex non‐linear process is developed. The dynamic behavior is modeled with a linear model. The pH process considered in this study exhibits drastic changes in the gain, even over a small operating range. In this study, the performance of controllers designed using Weiner and Hammerstein models are compared with a PI controller for servo and regulatory changes. The comparison results based on integral square error (ISE) values shows that the Weiner model based controller is suitable for a pH process.

A comparative study of neuro fuzzy and recurrent neuro fuzzy model-based controllers for real-time industrial processes

Nonlinearities in system dynamics and the multivariable nature of processes offer a stiff challenge in designing predictive controllers that improve process performance in industries. This investigation presents a recurrent neuro fuzzy network (RNFN) model for a nonlinear multivariable system in process industries and a methodology to design model-predictive controllers (MPCs) using the proposed model. The RNFN model combines the learning features of artificial neural networks with human cognition capabilities of fuzzy systems. Therefore, RNFN leads to a modelling framework that has the ability not only to learn the model parameters, but also makes decision on operating region of the nonlinear model depending on the input–output data. Furthermore, the recurrent structure and the introduction of a memory unit between the fuzzy inference and fuzzification layer enhance the prediction capability due to the use of past input–output data, making the model more suitable for designing predictive controllers. Next, the MPC design methodology that exploits the advantages of the RNFN model to optimize the control moves is presented. The proposed MPC uses the gradient descent algorithm to minimize the control moves as against the traditional state-space approaches that require complex computations and solvers. Therefore, implementing the proposed MPC in embedded hardware becomes easier. The proposed modelling framework and the MPC design methodology are illustrated using experiments on a laboratory-scale quadruple tank. Our experiments show that the proposed RNFN-based MPC performs better than the neuro fuzzy network-based MPC for both servo and regulatory responses.

Adaptive Control of Neutralization Process Using Recurrent Neural Networks

Abstract This paper presents Recurrent neural Network (RNN) based adaptive control scheme for a pH neutralization process which is difficult to control due to its nonlinear dynamics with uncertainties. The proposed design comprises of both RNN estimator which adapts online and a RNN controller. Desired performance of the system is ensured by the parallel operation of both. The estimator weights are updated recursively by back propagation algorithm and controller weights are modified by steepest descent approach. Stability and convergence of proposed controller is guaranteed by Lyapunov stability analysis. Servo and regulatory performance of the system thus obtained by simulation is compared with a model based IMC controller. The RNN based controller is exhibits better performance as shown by the control simulation of a nonlinear pH neutralization process.