N. Sivakumaran

Modeling and prediction of machining quality in CNC turning process using intelligent hybrid decision making tools

Decision-making process in manufacturing environment is increasingly difficult due to the rapid changes in design and demand of quality products. To make decision making process (selection of machining parameters) online, effective and efficient artificial intelligent tools like neural networks are being attempted. This paper proposes the development of neural network models for prediction of machining parameters in CNC turning process. Experiments are designed based on Taguchis Design of Experiments (DoE) and conducted with cutting speed, feed rate, depth of cut and nose radius as the process parameters and surface roughness and power consumption as objectives. Results from experiments are used to train the developed neuro based hybrid models. Among the developed models, performance of neural network model trained with particle swarm optimization model is superior in terms of computational speed and accuracy. Developed models are validated and reported. Signal-to-noise (S/N) ratios of responses are calculated to identify the influences of process parameters using analysis of variance (ANOVA) analysis. The developed model can be used in automotive industries for deciding the machining parameters to attain quality with minimum power consumption and hence maximum productivity.

Design of self tuning fuzzy controllers for nonlinear systems

Nonlinearities present in the systems make their controller design a non-trivial task. The difficulty further increases in case of multi-input-multi-output (MIMO) systems with increased number of variables and interactions between them. In this paper, fuzzy based intelligent control schemes are designed for control of nonlinear single-input-single-output (SISO) and MIMO systems. The comparative study of the designed self tuning fuzzy controller with a standard Takagi-Sugeno (TS) fuzzy controller is discussed with application to a shell and tube heat exchanger (nonlinear SISO system) and a coupled two tank system (nonlinear MIMO system). Online tuning of the membership functions and control rules of fuzzy controller is carried out using simulated annealing (SA) to obtain improved performance by minimizing the error function. Experimental results demonstrate the effectiveness of the control scheme.

IMC based Fractional order PID controller

This article presents the design of Internal Model Controller (IMC) based Fractional Order Two-Degrees-of-Freedom controller with the desired bandwidth specification and the robustness of the designed controller is briefly analyzed. For the conventional IMC based PID controller, One-Degree of Freedom is sufficient to meet the desired bandwidth specification for both Servo and regulatory performances. These features of the IMC based PID controller is combined with fractional order controller, as the fractional calculus has made a major impact in the control research community. It has been analytically and mathematically proved that One-Degree of Freedom IMC based fractional order controller tuned for the disturbance rejection will not meet the nominal specification for the set point tracking. To overcome this drawback, IMC based fractional order Two-Degree-of-Freedom controller is reported in this article.

Grey wolf optimization based parameter selection for support vector machines

Purpose The purpose of this paper is to tune support vector machine (SVM) classifier using grey wolf optimizer (GWO). Design/methodology/approach The schema of the work aims at extracting the features from the collected data followed by a SVM classifier and metaheuristic optimization to tune the classifier parameters. Findings The optimal tuning of classifier parameters lowers errors due to manual elucidation and decreases the risk in human perceptions and repeated visual dignosis. Originality/value A novel, GWO based tuning algorithm is used for SVM classifier, which is implemented in classifying the complex and nonlinear biomedical signals like intracranial electroencephalogram.

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.

Swing Up and Stabilization Control of a Rotary Inverted Pendulum

Abstract The control of a Rotary Inverted Pendulum (RIP) is a well-known and a challenging problem that serves as a popular benchmark in modern control system studies. The task is to design controllers which drives the pendulum from its hanging-down position to the upright position and then hold it there. The swing up is achieved using an energy based controller. In energy based control the pendulum is controlled in such a way that its energy is driven towards a value equal to the steady-state upright position. Then a mode controller switches between the swing-up controller and stabilizing controller near the upright position. For stabilization control, two control techniques are analyzed. Firstly, a sliding mode controller (SMC) is designed to stabilize the pendulum. Secondly, a state feedback controller is designed that would maintain the pendulum upright and handle disturbances up to a certain point. The state feedback controller is designed using the linear quadratic regulator (LQR). The responses of the LQR controller and SMC controller are compared in simulation.

Neural Model Predictive Controller for Multivariable Process

In this paper, control of a non-minimal quadruple tank process, which is non linear and multivariable is reported. A nonlinear Model Predictive Controller (NMPC) is developed using a Recurrent Neural Network (RNN) as a predictor. The process data is obtained from the laboratory scale experimental setup, which is used in training the RNN. The network trained is used in controlling the quadruple tank, solving the least square optimization problem with a quadratic performance objective. The control system is implemented in real-time on a laboratory scale plant using dSPACE interface card and Matlab software. The quality of controller using NMPC is compared with dynamic matrix control (DMC) for reference tracking and external disturbance rejection.

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.

Design and implementation of Model Predictive Controller for Type-I Diabetics

Tight glucose control is most essential in diabetic management. An approach is developed based on Model Predictive Controller (MPC) to deliver insulin continuously to the patient through a closed-loop system. A real-time system is developed using Glucose sensor and 89c51 microcontroller. The system is tested both in simulation and in real time. A conventional Proportional-Integral-Derivative (PID) controller is designed and the performance is compared with that of MPC controller.

Stabilization Using Fractional-Order PID α Controllers for First Order Time Delay System

This brief note considers the problem of stabilizing a first-order plant with dead-time using a fractional order Proportional-Integral-Derivative (PIDα) controller. Fractional order controller provides good stability margin than the conventional PID controller. Here, the upper bound of K_d value is estimated first for all permissible value of α which is followed by computing the value of K_c. Further, the graphic results have been provided to clearly show the range of stabilizing PIDα parameters [α, K_c, K_i and K_d]. An example illustrates the applicability of the procedure.