Faculty Publications
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Item Nonlinear system identification using memetic differential evolution trained neural networks(2011) Subudhi, B.; Jena, D.Several gradient-based approaches such as back propagation (BP) and Levenberg Marquardt (LM) methods have been developed for training the neural network (NN) based systems. But, for multimodal cost functions these procedures may lead to local minima, therefore, the evolutionary algorithms (EAs) based procedures are considered as promising alternatives. In this paper we focus on a memetic algorithm based approach for training the multilayer perceptron NN applied to nonlinear system identification. The proposed memetic algorithm is an alternative to gradient search methods, such as back-propagation and back-propagation with momentum which has inherent limitations of many local optima. Here we have proposed the identification of a nonlinear system using memetic differential evolution (DE) algorithm and compared the results with other six algorithms such as Back-propagation (BP), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), Genetic Algorithm Back-propagation (GABP), Particle Swarm Optimization combined with Back-propagation (PSOBP). In the proposed system identification scheme, we have exploited DE to be hybridized with the back propagation algorithm, i.e. differential evolution back-propagation (DEBP) where the local search BP algorithm is used as an operator to DE. These algorithms have been tested on a standard benchmark problem for nonlinear system identification to prove their efficacy. First examples shows the comparison of different algorithms which proves that the proposed DEBP is having better identification capability in comparison to other. In example 2 good behavior of the identification method is tested on an one degree of freedom (1DOF) experimental aerodynamic test rig, a twin rotor multi-input-multi-output system (TRMS), finally it is applied to Box and Jenkins Gas furnace benchmark identification problem and its efficacy has been tested through correlation analysis. © 2011 Elsevier B.V.Item Intelligent adaptive observer-based optimal control of overhead transmission line de-icing robot manipulator(Robotics Society of Japan ar@rsj.or.jp, 2016) Vijay, M.; Jena, D.[No abstract available]Item Backstepping terminal sliding mode control of robot manipulator using radial basis functional neural networks(Elsevier Ltd, 2018) Vijay, M.; Jena, D.This paper examines an observer-based backstepping terminal sliding mode controller (BTSMC) for 3 degrees of freedom overhead transmission line de-icing robot manipulator (OTDIRM). The control law for tracking of the OTDIRM is formulated by the combination of BTSMC and neural network (NN) based approximation. For the precise trajectory tracking performance and enhanced disturbance rejection, NN-based adaptive observer backstepping terminal sliding mode control (NNAOBTSMC) is developed. To obviate local minima problem, the weights of both NN observer and NN approximator are adjusted off-line using particle swarm optimization. The radial basis function neural network-based observer is used to estimate tracking position and velocity vectors of the OTDIRM. The stability of the proposed control methods is verified with the Lyapunov stability theorem. Finally, the robustness of the proposed NNAOBTSMC is checked against input disturbances and uncertainties. © 2017 Elsevier LtdItem Application of Reinforcement Learning-Based Adaptive PID Controller for Automatic Generation Control of Multi-Area Power System(Institute of Electrical and Electronics Engineers Inc., 2025) Muduli, R.; Jena, D.; Moger, T.This paper presents an application of an actor-critic reinforcement learning (RL) algorithm-based adaptive proportional-integral-derivative (PID) controller for automatic generation control of multi-area power systems. The proposed approach has several advantages over other deep RL algorithm-driven PID controllers, such as simplicity in structure, elimination of pre-learning requirements, and prior tuning of PID parameters. Online adaption of PID parameters is achieved through actor-critic policy. The proposed method implements a single radial basis function (RBF) based neural network for actor and critic networks. Three different case studies are demonstrated with proper illustration and analysis of the result to present the effectiveness and robustness of the proposed control strategy against various uncertainties. The outcomes of the proposed controller are compared with the conventional PID controller tuned by the Particle Swarm Optimization (PSO) algorithm. The results seem competent enough to maintain the frequency within an acceptable limit under various uncertainties. Note to Practitioners - This paper describes the application of an online adaptive PID controller for automatic generation control of power systems. The controller is designed using a model-free reinforcement learning algorithm, which enables it to control the system without requiring prior knowledge of the system dynamics. Additionally, the controller does not need any global optimization algorithm for tuning the parameters (KP, KI, KD) beforehand. This controller can be implemented for both linear and non-linear systems. © 2004-2012 IEEE.