Showing papers by "Santanu Chaudhury published in 1996"
TL;DR: A new extended Kalman filter (EKF) based network inversion scheme is proposed for trajectory tracking control of a two-link manipulator and comparison of the inversion algorithms showed the superior performance of the EKF based inversion schemes over others.
Abstract: This paper is concerned with the design of a neuro-adaptive trajectory tracking controller. The paper presents a new control scheme based on inversion of a feedforward neural model of a robot arm. The proposed control scheme requires two modules. The first module consists of an appropriate feedforward neural model of forward dynamics of the robot arm that continuously accounts for the changes in the robot dynamics. The second module implements an efficient network inversion algorithm that computes the control action by inverting the neural model. In this paper, a new extended Kalman filter (EKF) based network inversion scheme is proposed. The scheme is evaluated through comparison with two other schemes of network inversion: gradient search in input space and Lyapunov function approach. Using these three inversion schemes the proposed controller was implemented for trajectory tracking control of a two-link manipulator. Simulation results in all cases confirm the efficacy of control input prediction using network inversion. Comparison of the inversion algorithms in terms of tracking accuracy showed the superior performance of the EKF based inversion scheme over others.
48 citations
01 May 1996
TL;DR: The paper is concerned with the design of a hybrid controller structure, consisting of the adaptive control law and a neural-network-based learning scheme for adaptation of time-varying controller parameters, implemented using both MLN and RBF networks.
Abstract: The paper is concerned with the design of a hybrid controller structure, consisting of the adaptive control law and a neural-network-based learning scheme for adaptation of time-varying controller parameters. The target error vector for weight adaptation of the neural networks is derived using the Lyapunov-function approach. The global stability of the closed-loop feedback system is guaranteed, provided the structure of the robot-manipulator dynamics model is exact. Generalisation of the controller over the desired trajectory space has been established using an on-line weight-learning scheme. Model learning, using a priori knowledge of a robot arm model, has been shown to improve tracking accuracy. The proposed control scheme has been implemented using both MLN and RBF networks. Faster convergence, better generalisation and superior tracking accuracy have been achieved in the case of the RBF network.
37 citations
TL;DR: An abductive reasoning based inferencing engine for image interpretation that finds an acceptable and consistent explanation of the features detected in the image in terms of the objects known a priori.
Abstract: This paper describes an abductive reasoning based inferencing engine for image interpretation. The inferencing strategy finds an acceptable and consistent explanation of the features detected in the image in terms of the objects known a priori. The inferencing scheme assumes representation of the domain knowledge about the objects in terms of local and/or relational features. The inferencing system can be applied for different types of image interpretation problems like 2-D and 3-D object recognition, aerial image interpretation, etc. In this paper, we illustrate functioning of the system with the help of a 2-D object recognition problem.
2 citations
TL;DR: A query based learning algorithm is proposed in this paper to obtain a valid neural emulator of the robot manipulator using radial basis function networks and shows significant improvement in the neural model after retraining.
Abstract: A query based learning algorithm is proposed in this paper to obtain a valid neural emulator of the robot manipulator using radial basis function networks. This algorithm is centred around the conc...