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Articles 1 - 8 of 8
Full-Text Articles in Engineering
Feedback Linearization Based Power System Stabilizer Design With Control Limits, Wenxin Liu, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Jagannathan Sarangapani
Feedback Linearization Based Power System Stabilizer Design With Control Limits, Wenxin Liu, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Jagannathan Sarangapani
Electrical and Computer Engineering Faculty Research & Creative Works
In power system controls, simplified analytical models are used to represent the dynamics of power system and controller designs are not rigorous with no stability analysis. One reason is because the power systems are complex nonlinear systems which pose difficulty for analysis. This paper presents a feedback linearization based power system stabilizer design for a single machine infinite bus power system. Since practical operating conditions require the magnitude of control signal to be within certain limits, the stability of the control system under control limits is also analyzed. Simulation results under different kinds of operating conditions show that the controller …
Neural Network Stabilizing Control Of Single Machine Power System With Control Limits, Wenxin Liu, Jagannathan Sarangapani, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Mariesa Crow
Neural Network Stabilizing Control Of Single Machine Power System With Control Limits, Wenxin Liu, Jagannathan Sarangapani, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Mariesa Crow
Electrical and Computer Engineering Faculty Research & Creative Works
Power system stabilizers are widely used to generate supplementary control signals for the excitation system in order to damp out the low frequency oscillations. This paper proposes a stable neural network (NN) controller for the stabilization of a single machine infinite bus power system. In the power system control literature, simplified analytical models are used to represent the power system and the controller designs are not based on rigorous stability analysis. This work overcomes the two major problems by using an accurate analytical model for controller development and presents the closed-loop stability analysis. The NN is used to approximate the …
Neural Network Controller For Manipulation Of Micro-Scale Objects, Vijayakumar Janardhan, Pingan He, Jagannathan Sarangapani
Neural Network Controller For Manipulation Of Micro-Scale Objects, Vijayakumar Janardhan, Pingan He, Jagannathan Sarangapani
Electrical and Computer Engineering Faculty Research & Creative Works
A novel reinforcement learning-based neural network (RLNN) controller is presented for the manipulation and handling of micro-scale objects in a microelectromechanical system (MEMS). In MEMS, adhesive, surface tension, friction and van der Waals forces are dominant. Moreover, these forces are typically unknown. The RLNN controller consists of an action NN for compensating the unkoown system dynamics, and a critic NN to tune the weights of the action NN. Using the Lyapunov approach, the uniformly ultimate houndedness (UUB) of the closed-loop tracking error and weight estimates are shown by using a novel weight updates. Simulation results are presented to substantiate the …
Distributed Power Control Of Cellular Networks In The Presence Of Channel Uncertainties, Maciej Jan Zawodniok, Q. Shang, Jagannathan Sarangapani
Distributed Power Control Of Cellular Networks In The Presence Of Channel Uncertainties, Maciej Jan Zawodniok, Q. Shang, Jagannathan Sarangapani
Electrical and Computer Engineering Faculty Research & Creative Works
A novel distributed power control (DPC) scheme for cellular networks in the presence of radio channel uncertainties such as path loss shadowing, and Rayleigh fading is presented. Since these uncertainties can attenuate the received signal strength and can cause variations in the received Signal-to-lnterference ratio (SIR), the proposed DPC scheme maintains a target SIR at the receiver provided the uncertainty is slowly varying with time. The DPC estimates the time varying nature of the channel quickly and uses the information to arrive at a suitable transmitter power value . Further, the standard assumption of a constant interference during a link's …
Adaptive Critic Neural Network-Based Object Grasping Control Using A Three-Finger Gripper, Gustavo Galan, Jagannathan Sarangapani
Adaptive Critic Neural Network-Based Object Grasping Control Using A Three-Finger Gripper, Gustavo Galan, Jagannathan Sarangapani
Electrical and Computer Engineering Faculty Research & Creative Works
Grasping of objects has been a challenging task for robots. The complex grasping task can be defined as object contact control and manipulation subtasks. In this paper, object contact control subtask is defined as the ability to follow a trajectory accurately by the fingers of a gripper. The object manipulation subtask is defined in terms of maintaining a predefined applied force by the fingers on the object. A sophisticated controller is necessary since the process of grasping an object without a priori knowledge of the object's size, texture, softness, gripper, and contact dynamics is rather difficult. Moreover, the object has …
Discrete-Time Neural Network Output Feedback Control Of Nonlinear Systems In Non-Strict Feedback Form, Pingan He, Jagannathan Sarangapani
Discrete-Time Neural Network Output Feedback Control Of Nonlinear Systems In Non-Strict Feedback Form, Pingan He, Jagannathan Sarangapani
Electrical and Computer Engineering Faculty Research & Creative Works
An adaptive neural network (NN)-based output feedback controller is proposed to deliver a desired tracking performance for a class of discrete-time nonlinear systems, which is represented in non-strict feedback form. The NN backstepping approach is utilized to design the adaptive output feedback controller consisting of: 1) a NN observer to estimate the system states with the input-output data, and 2) two NNs to generate the virtual and actual control inputs, respectively. The non-causal problem in the discrete-time backstepping design is avoided by using the universal NN approximator. The persistence excitation (PE) condition is relaxed both in the NN observer and …
Decentralized Neural Network Control Of A Class Of Large-Scale Systems With Unknown Interconnection, Wenxin Liu, Jagannathan Sarangapani, Donald C. Wunsch, Mariesa Crow
Decentralized Neural Network Control Of A Class Of Large-Scale Systems With Unknown Interconnection, Wenxin Liu, Jagannathan Sarangapani, Donald C. Wunsch, Mariesa Crow
Electrical and Computer Engineering Faculty Research & Creative Works
A novel decentralized neural network (DNN) controller is proposed for a class of large-scale nonlinear systems with unknown interconnections. The objective is to design a DNN for a class of large-scale systems which do not satisfy the matching condition requirement. The NNs are used to approximate the unknown subsystem dynamics and the interconnections. The DNN is designed using the back stepping methodology with only local signals for feedback. All of the signals in the closed loop (system states and weights estimation errors) are guaranteed to be uniformly ultimately bounded and eventually converge to a compact set.
Adaptive Force-Balancing Control Of Mems Gyroscope With Actuator Limits, Mohammed Hameed, Jagannathan Sarangapani
Adaptive Force-Balancing Control Of Mems Gyroscope With Actuator Limits, Mohammed Hameed, Jagannathan Sarangapani
Electrical and Computer Engineering Faculty Research & Creative Works
This work presents an adaptive force-balancing control (AFBC) scheme with actuator limits for a MEMS Z-axis gyroscope. The purpose of the adaptive force-balancing control is to identify major fabrication imperfections so that they are properly compensated unlike the case of conventional force-balancing controlled gyroscope. The proposed AFBC scheme controls the vibratory modes of the proof mass while ensuring that the control input satisfies the magnitude constraints and the performance of the gyroscope is enhanced in the presence of fabrication uncertainties. Consequently, commonly reported problems of MEMS gyroscope such as quadrature compensation, drive and sense axes frequency tuning are not needed …