Madan Gupta

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B. Harun Küçük

University of Pennsylvania

Ruth DeSouza

University of Melbourne

Christopher Witmore

Texas Tech University

Armando Marques-Guedes

UNL - New University of Lisbon

Roshan Chitrakar

Nepal College of Information Technology

Lev Manovich

Graduate Center of the City University of New York

Timothy Morton

Rice University

Jaydip Sen

Praxis Business School

Rossitza Goleva

New Bulgarian University

Annappa B

National Institute of Technology Karnataka,Surathkal

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Papers by Madan Gupta

Neuro-controller with dynamic learning and adaptation

Journal of Intelligent and Robotic Systems, 1993

As is known, many of the attributes of intelligent control in a biological process are due to the... more As is known, many of the attributes of intelligent control in a biological process are due to the interactions of billions of neurons. Changing the weights of neurons alter the behavior of the entire neural network. Learning in a neutral network is accomplished by adjusting the weights, typically to minimize some objective function, and storing these weights as the actual strengths of the interconnections. The authors believe, therefore, that a control technique designed on the principles of neural networks will exhibit a ‘learn-while-performing’ capability. In this paper such a neuro-controller, called the ‘Inverse-Dynamics Adaptive Control (IDAC)’, for a class of unknown linear plants with structural perturbations is presented. Algorithms necessary to implement the IDAC technique are derived in detail. Simulation results show that the IDAC scheme exhibits dynamic learning and adaptation capabilities in the control of unknown complex systems. Noa-priori knowledge of the process to be controlled is necessary for the implementation of this scheme. Furthermore, the plant parameter variations due to the structural or environmental perturbations may be investigated by studying the IDAC parameter trajectories.

A Recurrent Neural Network for Hierarchical Control of Interconnected Dynamic Systems

IEEE Transactions on Neural Networks, 2007

A recurrent neural network for the optimal control of a group of interconnected dynamic systems i... more A recurrent neural network for the optimal control of a group of interconnected dynamic systems is presented in this paper. On the basis of decomposition and coordination strategy for interconnected dynamic systems, the proposed neural network has a two-level hierarchical structure: several local optimization subnetworks at the lower level and one coordination subnetwork at the upper level. A goal-coordination method is used to coordinate the interactions between the subsystems. By nesting the dynamic equations of the subsystems into their corresponding local optimization subnetworks, the number of dimensions of the neural network can be reduced significantly. Furthermore, the subnetworks at both the lower and upper levels can work concurrently. Therefore, the computation efficiency, in comparison with the consecutive executions of numerical algorithms on digital computers, is increased dramatically. The proposed method is extended to the case where the control inputs of the subsystems are bounded. The stability analysis shows that the proposed neural network is asymptotically stable. Finally, an example is presented which demonstrates the satisfactory performance of the neural network.

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Fast neural learning and control of discrete-time nonlinear systems

IEEE Transactions on Systems, Man, and Cybernetics, 1995

The problem of learning control for a general class of discrete-time nonlinear systems is address... more The problem of learning control for a general class of discrete-time nonlinear systems is addressed in this paper using multilayered neural networks (M"s) with feedforward connections. A suitable extension of the concept of input-output linearization of discrete-time nonlinear systems is used to develop the control schemes for both output tracking and model reference control purposes. The ability of MNNs to model arbitrary nonlinear functions is incorporated to approximate the unknown nonlinear input-output relationship and its inverse using a new weight learning algorithm. In order to overcome the difficulties associated with simultaneous on-line identification and control in neural networks based adaptive control systems, the new learning control architectures are developed for both adaptive tracking and adaptive model reference control systems with on-line identification and control ability. The potentials of the proposed methods are demonstrated by simulation examples. 0018-9472/95$04.00 @ 1995 IEEE I

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Cooperative collision avoidance between multiple mobile robots

Journal of Robotic Systems, 2000

This paper presents a new collision avoidance technique, called cooperative collision avoidance, ... more This paper presents a new collision avoidance technique, called cooperative collision avoidance, for multiple mobile robots. The detection of the danger of collision between two mobile robots is discussed with respect to the geometric aspects of their paths as are cooperative collision avoidance behaviors. The direction control command and the velocity control command for the cooperative collision avoidance are then proposed. The avoidance technique is extended to cases in which the number of mobile robots is more than two. Furthermore, the conditions for collision avoidance are considered with respect to the navigation parameters and guidelines of designing the navigation parameters are obtained. The effectiveness of the proposed technique is demonstrated by means of numerical simulation and navigation experiments using two real mobile robots named Pioneer-1.

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PISA - A New Noninvasive Method for Early Detection and Quantification of Ischemia in Hypertensive Patients

Angiology, 1985

Hypertension if untreated, is known to produce cardiac damage. Cardiac complications result from ... more Hypertension if untreated, is known to produce cardiac damage. Cardiac complications result from the effects of hypertension on the myocardium and/or to accelerated atherosclerotic changes in the coronary arteries. Presently availa ble diagnostic tools are able to detect cardiac complications only when they are relatively at an advanced stage. Early detection of cardiac complications would have the advantage of early

Adaptive navigation of mobile robots with obstacle avoidance

IEEE Transactions on Robotics and Automation, 1997

A local navigation technique with obstacle avoidance, called adaptive navigation, is proposed for... more A local navigation technique with obstacle avoidance, called adaptive navigation, is proposed for mobile robots in which the dynamics of the robot are taken into consideration. The only information needed about the local environment is the distance between the robot and the obstacles in three specified directions. The navigation law is a first-order differential equation and navigation to the goal and obstacle avoidance are achieved by switching the direction angle of the robot. The effectiveness of the technique is demonstrated by means of simulation examples

Absolute stability conditions for discrete-time recurrent neural networks

IEEE Transactions on Neural Networks, 1994

An analysis of the absolute stability for a general class of discrete-time recurrent neural netwo... more An analysis of the absolute stability for a general class of discrete-time recurrent neural networks (RNN&#39;s) is presented. A discrete-time model of RNN&#39;s is represented by a set of nonlinear difference equations. Some sufficient conditions for the absolute stability are derived using Ostrowski&#39;s theorem and the similarity transformation approach. For a given RNN model, these conditions are determined by the synaptic weight matrix of the network. The results reported in this paper need fewer constraints on the weight matrix and the model than in previously published studies.

Approximation of discrete-time state-space trajectories using dynamic recurrent neural networks

IEEE Transactions on Automatic Control, 1995

Page 1. I266 IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL. 40, NO. 7, JULY 1995 Approximation of D... more

Stable dynamic backpropagation learning in recurrent neural networks

IEEE Transactions on Neural Networks, 1999

The conventional dynamic backpropagation (DBP) algorithm proposed by Pineda does not necessarily ... more The conventional dynamic backpropagation (DBP) algorithm proposed by Pineda does not necessarily imply the stability of the dynamic neural model in the sense of Lyapunov during a dynamic weight learning process. A difficulty with the DBP learning process is thus associated with the stability of the equilibrium points which have to be checked by simulating the set of dynamic equations, or else by verifying the stability conditions, after the learning has been completed. To avoid unstable phenomenon during the learning process, two new learning schemes, called the multiplier and constrained learning rate algorithms, are proposed in this paper to provide stable adaptive updating processes for both the synaptic and somatic parameters of the network. Based on the explicit stability conditions, in the multiplier method these conditions are introduced into the iterative error index, and the new updating formulations contain a set of inequality constraints. In the constrained learning rate algorithm, the learning rate is updated at each iterative instant by an equation derived using the stability conditions. With these stable DBP algorithms, any analog target pattern may be implemented by a steady output vector which is a nonlinear vector function of the stable equilibrium point. The applicability of the approaches presented is illustrated through both analog and binary pattern storage examples.

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Neuro-controller with dynamic learning and adaptation

Journal of Intelligent and Robotic Systems, 1993