J. R. P. Gupta

A new algorithm-based type-2 fuzzy controller for diabetic patient

In this paper, a new control algorithm is developed, which uses type-2 fuzzy sets for automatic insulin delivery rate. Type-2 fuzzy sets have been used to handle uncertainties in the rules due to the inability of type-1 fuzzy sets in such cases. As the stability of the model is also highly dependent on the learning of the system, we have used Lyapunov Stability (LS) in combination with Fuzzy Differential (FD) for learning. A measure of the performance of different controllers is taken by incorporating parametric uncertainty into the model. The effectiveness of the proposed controller is demonstrated on type I diabetes patient.

Diagonal recurrent neural network based adaptive control of nonlinear dynamical systems using lyapunov stability criterion

In this paper adaptive control of nonlinear dynamical systems using diagonal recurrent neural network (DRNN) is proposed. The structure of DRNN is a modification of fully connected recurrent neural network (FCRNN). Presence of self-recurrent neurons in the hidden layer of DRNN gives it an ability to capture the dynamic behaviour of the nonlinear plant under consideration (to be controlled). To ensure stability, update rules are developed using lyapunov stability criterion. These rules are then used for adjusting the various parameters of DRNN. The responses of plants obtained with DRNN are compared with those obtained when multi-layer feed forward neural network (MLFFNN) is used as a controller. Also, in example 4, FCRNN is also investigated and compared with DRNN and MLFFNN. Robustness of the proposed control scheme is also tested against parameter variations and disturbance signals. Four simulation examples including one-link robotic manipulator and inverted pendulum are considered on which the proposed controller is applied. The results so obtained show the superiority of DRNN over MLFFNN as a controller.

Online modeling and adaptive control of robotic manipulators using Gaussian radial basis function networks

Radial basis function network (RBFN) is used in this paper for predefined trajectory control of both one-link and two-link robotic manipulators. The updating equations for the RBFN parameters were derived using the gradient descent principle. The other advantage of using this principle is that it shows the clustering effect in distributing the radial centres. To increase the complexity, the dynamics of robotic manipulator is assumed to be unknown, and hence, simultaneous control and identification steps were performed using the RBFNs. The performance of the RBFN is compared with the multilayer feed-forward neural network (MLFFNN) in terms of mean square error, tolerance to disturbance and parameter variations in the system. The efficacy of RBFN as a controller and identification tool is verified by performing the simulation study, and the results obtained reveal the superior performance of RBFN over MLFFNN in both identification and control aspects for one-link and two-link robotic manipulators.

Modeling and adaptive control of nonlinear dynamical systems using radial basis function network

In this paper, the use of radial basis function network (RBFN) for simultaneous online identification and indirect adaptive control of nonlinear dynamical systems is demonstrated. The motivation of using RBFN comes from the simplicity of its structure and simpler mathematical formulation, which gives it an advantage over multi-layer feed-forward neural network (MLFFNN). Since most processes are nonlinear, the use of conventional proportional-integral-derivative controller is not useful. Most of the time plant’s dynamics information is not available. This creates another limitation on the use of conventional control techniques, which works only if plant’s dynamics information is available. The proposed controller is tested for parameter variations and disturbance effects. Simulation results showed that RBFN is able to capture the unknown dynamics as well as simultaneously able to adaptively control the plant. It is also found to compensate the effects of parameter variations and disturbances. The comparative analysis is also done with MLFFNN in each simulation example, and it is found that performance of RBFN is better than that of MLFFNN.

A novel shape based batching and prediction approach for sunspot data using HMMs and ANNs

This paper introduces a novel approach which uses a Hidden Markov Model (HMM) based Artificial Neural Networks (ANN) for prediction of systems that are non deterministic, dynamical and chaotic in nature. The HMM is used for shape based batch creation of training data, which is then processed one batch at a time by an ANN. The weights and Learning Rate of the ANN are tweaked to predict the correct output for an input dataset. The novel Prediction method used here exploits the Pattern Identification prowess of the HMM for batch selection and the ANNs of each batch to predict the output of the system. The Standard application of the Sun-Spot Data (SSD) was used for testing the competence of this method.

Management of Distress During Climacteric Years by Homeopathic Therapy

OBJECTIVES The purpose of this study was to ascertain the usefulness of homeopathic therapy in the management of distressing symptoms encountered during climacteric years in women (primary objective) and also the changes brought about in the levels of follicle-stimulating hormone (FSH) and lipid profile in these women after homeopathic treatment (secondary objective). MATERIALS AND METHODS An open, multicenter, prospective, observational study was carried out to ascertain the usefulness of homeopathic treatment in distress during climacteric years (DDCY). Patients were enrolled from the general outpatient department of the six Institutes/Units of Central Council for Research in Homoeopathy (CCRH) and were required to complete a follow-up period of 1 year as per the protocol designed by the CCRH. A uniform questionnaire assessing 15 predefined symptoms of menopause was adopted, with assessment of each symptom at every visit. Levels of serum FSH and lipid profile were monitored at entry and at completion. Effect size of the study was also calculated. CARA Software was used for repertorization of the presenting symptoms of menopause along with the characteristic attributes of each patient to arrive at a simillimum. The selected medicine was prescribed in a single dose as per the homeopathic principles. The assessment of the results was made through statistical analysis using the Wilcoxon signed rank test on Statistical Package for Social Sciences (SPSS) comparing symptom score at entry and completion of 1 year of treatment and t test for analyzing improvement in laboratory findings. RESULTS Homeopathic therapy was found to be useful in relieving menopausal distressing symptoms such as hot flashes, night sweats, anxiety, palpitation, depression, insomnia, and so on. Influence on serum levels of FSH, high-density lipoprotein, and low-density lipoprotein was not significant but serum levels of cholesterol, triglycerides, and very-low-density lipoprotein decreased significantly. Effect size of the study was found to be large. The medicines found to be most frequently indicated and useful were Sepia, Lachesis, Calcarea carb., Lycopodium, and Sulphur. CONCLUSIONS This study proves the usefulness of homeopathic medicines in relieving DDCY.

Diagonal recurrent neural network based identification of nonlinear dynamical systems with Lyapunov stability based adaptive learning rates

Abstract This paper proposes a diagonal recurrent neural network (DRNN) based identification model for approximating the unknown dynamics of the nonlinear plants. The proposed model offers deeper memory and a simpler structure. Thereafter, we have developed a dynamic back-propagation learning algorithm for tuning the parameters of DRNN. Further, to guarantee the faster convergence and stability of the overall system, dynamic (adaptive) learning rates are developed in the sense of Lyapunov stability method. The proposed scheme is also compared with multi-layer feed forward neural network (MLFFNN) and radial basis function network (RBFN) based identification models. Numerical experiments reveal that DRNN has performed much better in approximating the dynamics of the plant and have also shown more robustness toward system uncertainties.

Lyapunov stability-based control and identification of nonlinear dynamical systems using adaptive dynamic programming

This paper presents a novel control and identification scheme based on adaptive dynamic programming for nonlinear dynamical systems. The aim of control in this paper is to make output of the plant to follow the desired reference trajectory. The dynamics of plants are assumed to be unknown, and to tackle the problem of unknown plant’s dynamics, parameter variations and disturbance signal effects, a separate neural network-based identification model is set up which will work in parallel to the plant and the control scheme. Weights update equations of all neural networks present in the proposed scheme are derived using both gradient descent (GD) and Lyapunov stability (LS) criterion methods. Stability proof of LS-based algorithm is also given. Weight update equations derived using LS criterion ensure the global stability of the system, whereas those obtained through GD principle do not. Further, adaptive learning rate is employed in weight update equation instead of constant one in order to have fast learning of weight vectors. Also, LS- and GD-based weight update equations are also tested against parameter variation and disturbance signal. Three nonlinear dynamical systems (of different complexity) including the forced rigid pendulum trajectory control are used in this paper on which the proposed scheme is applied. The results obtained with LS method are found more accurate than those obtained with the GD-based method.

Artificial Neural Network based PID controller for online control of dynamical systems

For online control of various dynamical systems, an adaptive artificial neural network (ANN) based proportional integral derivative (PID) controller is developed. For linear time invariant processes, conventional PID controller is suitable but they have limitations when they are required to control the plants having high non linearity or their parameters are changing with the time. In order to find the parameters of PID controller, information regarding the dynamics of the plant is essential. If perturbation occurs in plant parameter(s) then PID controller may work only if these changes are not severe. But most plants are either non linear or their parameters changes with time and this demands for a use of more robust type of controller and ANN is a suitable candidate. To use the power of PID controller and ANN, ANN based PID controller is proposed in this paper. The benefit of this combination is that it utilizes the simplicity of PID controller mathematical formula and uses the ANN powerful capability to handle parameter variations and non linearity.

Modeling and control of one-link robotic manipulator using neural network based PID controller

The dynamics of one-link robotic manipulator is complex and non linear and hence, cannot be easily controlled by conventional PID controller. The severity of the problem further increases when the plants mathematical model is unknown or partially known which makes the use of PID control more difficult because it requires the dynamics of the system for tuning its parameters. Even if the dynamics are known, the parameters of PID controller are required to be retuned when external disturbance signals and/or parameter variations occurs in the system. In this paper, the PID controller is implemented using a multilayer feed forward neural network (MLFFNN) for the desired trajectory tracking control of one-link robotic manipulator (plant). To make the controller adaptive, the dynamics of plant is assumed to be unknown and hence, a separate multilayer feed forward neural network identification model is used which will approximate the plants dynamics and operate simultaneously with the controller. The other benefits of using an identification model is that it can adjust its own parameters to reflect the effects of the disturbance signal and parameter variations on the system and provides this information to the controller which then makes necessary adjustment to its output to compensate these effects. Simulation results shows that MLFFNN based PID controller is able to control the plant and provides the desired trajectory in the presence of parameter variations and disturbance signal.