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Operations Research, Systems Engineering and Industrial Engineering
Electrical and Computer Engineering Faculty Research & Creative Works
- Keyword
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- Neurocontrollers (12)
- Lyapunov Methods (10)
- Nonlinear Control Systems (9)
- Adaptive Control (8)
- Closed Loop Systems (8)
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- Control System Synthesis (7)
- Discrete-time systems (6)
- Learning (Artificial Intelligence) (6)
- Neural Networks (6)
- Discrete Time Systems (5)
- Neural Network (5)
- Adaptive Systems (4)
- Decentralized Control (4)
- Lyapunov Method (4)
- Observers (4)
- Access Protocols (3)
- Complex Networks (3)
- Distributed Power Control (3)
- Embedded Systems (3)
- Feedback (3)
- Formation Control (3)
- Learning Systems (3)
- Neural Networks (NNs) (3)
- Nonlinear control theory (3)
- Optimal Control (3)
- Power System Control (3)
- Telecommunication Congestion Control (3)
- Wireless Sensor Network (3)
- Adaptive Critic (2)
- Congestion Control (2)
Articles 31 - 60 of 65
Full-Text Articles in Engineering
Online Reinforcement Learning Neural Network Controller Design For Nanomanipulation, Qinmin Yang, Jagannathan Sarangapani
Online Reinforcement Learning Neural Network Controller Design For Nanomanipulation, Qinmin Yang, Jagannathan Sarangapani
Electrical and Computer Engineering Faculty Research & Creative Works
In this paper, a novel reinforcement learning neural network (NN)-based controller, referred to adaptive critic controller, is proposed for affine nonlinear discrete-time systems with applications to nanomanipulation. In the online NN reinforcement learning method, one NN is designated as the critic NN, which approximates the long-term cost function by assuming that the states of the nonlinear systems is available for measurement. An action NN is employed to derive an optimal control signal to track a desired system trajectory while minimizing the cost function. Online updating weight tuning schemes for these two NNs are also derived. By using the Lyapunov approach, …
Adaptive Power Control Protocol With Hardware Implementation For Wireless Sensor And Rfid Reader Networks, Kainan Cha, Jagannathan Sarangapani, David Pommerenke
Adaptive Power Control Protocol With Hardware Implementation For Wireless Sensor And Rfid Reader Networks, Kainan Cha, Jagannathan Sarangapani, David Pommerenke
Electrical and Computer Engineering Faculty Research & Creative Works
The development and deployment of radio frequency identification (RFID) systems render a novel distributed sensor network which enhances visibility into manufacturing processes. In RFID systems, the detection range and read rates will suffer from interference among high-power reading devices. This problem grows severely and degrades system performance in dense RFID networks. Consequently, medium access protocols (MAC) protocols are needed for such networks to assess and provide access to the channel so that tags can be read accurately. In this paper, we investigate a suite of feasible power control schemes to ensure overall coverage area of the system while maintaining a …
Neural Network Control Of Robot Formations Using Rise Feedback, Jagannathan Sarangapani, Travis Alan Dierks
Neural Network Control Of Robot Formations Using Rise Feedback, Jagannathan Sarangapani, Travis Alan Dierks
Electrical and Computer Engineering Faculty Research & Creative Works
In this paper, a combined kinematic/torque control law is developed for leader-follower based formation control using backstepping in order to accommodate the dynamics of the robots and the formation in contrast with kinematic-based formation controllers that are widely reported in the literature. A neural network (NN) is introduced along with robust integral of the sign of the error (RISE) feedback to approximate the dynamics of the follower as well as its leader using online weight tuning. It is shown using Lyapunov theory that the errors for the entire formation are asymptotically stable and the NN weights are bounded as opposed …
Online Reinforcement Learning Control Of Unknown Nonaffine Nonlinear Discrete Time Systems, Qinmin Yang, Jagannathan Sarangapani
Online Reinforcement Learning Control Of Unknown Nonaffine Nonlinear Discrete Time Systems, Qinmin Yang, Jagannathan Sarangapani
Electrical and Computer Engineering Faculty Research & Creative Works
In this paper, a novel neural network (NN) based online reinforcement learning controller is designed for nonaffine nonlinear discrete-time systems with bounded disturbances. The nonaffine systems are represented by nonlinear auto regressive moving average with exogenous input (NARMAX) model with unknown nonlinear functions. An equivalent affine-like representation for the tracking error dynamics is developed first from the original nonaffine system. Subsequently, a reinforcement learning-based neural network (NN) controller is proposed for the affine-like nonlinear error dynamic system. The control scheme consists of two NNs. One NN is designated as the critic, which approximates a predefined long-term cost function, whereas an …
Near Optimal Neural Network-Based Output Feedback Control Of Affine Nonlinear Discrete-Time Systems, Qinmin Yang, Jagannathan Sarangapani
Near Optimal Neural Network-Based Output Feedback Control Of Affine Nonlinear Discrete-Time Systems, Qinmin Yang, Jagannathan Sarangapani
Electrical and Computer Engineering Faculty Research & Creative Works
In this paper, a novel online reinforcement learning neural network (NN)-based optimal output feedback controller, referred to as adaptive critic controller, is proposed for affine nonlinear discrete-time systems, to deliver a desired tracking performance. The adaptive critic design consist of three entities, an observer to estimate the system states, an action network that produces optimal control input and a critic that evaluates the performance of the action network. The critic is termed adaptive as it adapts itself to output the optimal cost-to-go function which is based on the standard Bellman equation. By using the Lyapunov approach, the uniformly ultimate boundedness …
Neural Network Controller Development And Implementation For Spark Ignition Engines With High Egr Levels, Jonathan B. Vance, Atmika Singh, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier
Neural Network Controller Development And Implementation For Spark Ignition Engines With High Egr Levels, Jonathan B. Vance, Atmika Singh, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier
Electrical and Computer Engineering Faculty Research & Creative Works
Past research has shown substantial reductions in the oxides of nitrogen (NOx) concentrations by using 10% -25% exhaust gas recirculation (EGR) in spark ignition (SI) engines (see Dudek and Sain, 1989). However, under high EGR levels, the engine exhibits strong cyclic dispersion in heat release which may lead to instability and unsatisfactory performance preventing commercial engines to operate with high EGR levels. A neural network (NN)-based output feedback controller is developed to reduce cyclic variation in the heat release under high levels of EGR even when the engine dynamics are unknown by using fuel as the control input. A separate …
Reinforcement Learning Based Output-Feedback Controller For Complex Nonlinear Discrete-Time Systems, Peter Shih, Jagannathan Sarangapani
Reinforcement Learning Based Output-Feedback Controller For Complex Nonlinear Discrete-Time Systems, Peter Shih, Jagannathan Sarangapani
Electrical and Computer Engineering Faculty Research & Creative Works
A novel reinforcement-learning based output-adaptive neural network (NN) controller, also referred as the adaptive-critic NN controller, is developed to track a desired trajectory for a class of complex feedback nonlinear discrete-time systems in the presence of bounded and unknown disturbances. This nonlinear discrete-time system consists of a second order system in nonstrict form and an affine nonlinear discrete-time system tightly coupled together. Two adaptive critic NN controllers are designed - primary one for the nonstrict system and the secondary one for the affine system. A Lyapunov function shows the uniformly ultimate boundedness (UUB) of the closed-loop tracking error, weight estimates …
Route Aware Predictive Congestion Control Protocol For Wireless Sensor Networks, Carl Larsen, Maciej Jan Zawodniok, Jagannathan Sarangapani
Route Aware Predictive Congestion Control Protocol For Wireless Sensor Networks, Carl Larsen, Maciej Jan Zawodniok, Jagannathan Sarangapani
Electrical and Computer Engineering Faculty Research & Creative Works
Congestion in wireless sensor networks (WSN) may lead to packet losses or delayed delivery of important information rendering the WSN-based monitoring or control system useless. In this paper a routing-aware predictive congestion control (RPCC) yet decentralized scheme for WSN is presented that uses a combination of a hop by hop congestion control mechanism to maintain desired level of buffer occupancy, and a dynamic routing scheme that works in concert with the congestion control mechanism to forward the packets through less congested nodes. The proposed adaptive approach restricts the incoming traffic thus preventing buffer overflow while maintaining the rate through an …
Adaptive Critic Neural Network Force Controller For Atomic Force Microscope-Based Nanomanipulation, Qinmin Yang, Jagannathan Sarangapani
Adaptive Critic Neural Network Force Controller For Atomic Force Microscope-Based Nanomanipulation, Qinmin Yang, Jagannathan Sarangapani
Electrical and Computer Engineering Faculty Research & Creative Works
Automating the task of nanomanipulation is extremely important since it is tedious for humans. This paper proposes an atomic force microscope (AFM) based force controller to push nano particles on the substrates. A block phase correlation-based algorithm is embedded into the controller for the compensation of the thermal drift which is considered as the main external uncertainty during nanomanipulation. Then, the interactive forces and dynamics between the tip and the particle, particle and the substrate are modeled and analyzed. Further, an adaptive critic NN controller based on adaptive dynamic programming algorithm is designed and the task of pushing nano particles …
Neural Network Based Decentralized Excitation Control Of Large Scale Power Systems, Wenxin Liu, Ganesh K. Venayagamoorthy, Donald C. Wunsch, David A. Cartes, Jagannathan Sarangapani
Neural Network Based Decentralized Excitation Control Of Large Scale Power Systems, Wenxin Liu, Ganesh K. Venayagamoorthy, Donald C. Wunsch, David A. Cartes, Jagannathan Sarangapani
Electrical and Computer Engineering Faculty Research & Creative Works
This paper presents a neural network (NN) based decentralized excitation controller design for large scale power systems. The proposed controller design considers not only the dynamics of generators but also the algebraic constraints of the power flow equations. The control signals are calculated using only local signals. The transient stability and the coordination of the subsystem controllers can be guaranteed. NNs are used to approximate the unknown/imprecise dynamics of the local power system and the interconnections. All signals in the closed loop system are guaranteed to be uniformly ultimately bounded (UUB). Simulation results with a 3-machine power system demonstrate the …
Adaptive And Probabilistic Power Control Algorithms For Dense Rfid Reader Network, Kainan Cha, Anil Ramachandran, Jagannathan Sarangapani
Adaptive And Probabilistic Power Control Algorithms For Dense Rfid Reader Network, Kainan Cha, Anil Ramachandran, Jagannathan Sarangapani
Electrical and Computer Engineering Faculty Research & Creative Works
In radio frequency identification (RFID) systems, the detection range and read rates may suffer from interferences between high power devices such as readers. In dense networks, this problem grows severely and degrades system performance. In this paper, we investigate feasible power control schemes to ensure overall coverage area of the system while maintaining a desired data rate. The power control should dynamically adjust the output power of a RFID reader by adapting to the noise level seen during tag reading and acceptable signal-to-noise ratio (SNR). We present a novel distributed adaptive power control (DAPC) and probabilistic power control (PPC) as …
Decentralized Power Control With Implementation For Rfid Networks, Kainan Cha, Anil Ramachandran, David Pommerenke, Jagannathan Sarangapani
Decentralized Power Control With Implementation For Rfid Networks, Kainan Cha, Anil Ramachandran, David Pommerenke, Jagannathan Sarangapani
Electrical and Computer Engineering Faculty Research & Creative Works
In radio frequency identification (RFID) systems, the detection range and read rates will suffer from interference among high power reading devices. This problem grows severely and degrades system performance in dense RFID networks. In this paper, we investigate a suite of feasible power control schemes to ensure overall coverage area of the system while maintaining a desired read rate. The power control scheme and MAC protocol dynamically adjusts the RFID reader power output in response to the interference level seen locally during tag reading for an acceptable signal-to-noise ratio (SNR). We present novel distributed adaptive power control (DAPC) and probabilistic …
Distributed Power Control For Cellular Networks In The Presence Of Channel Uncertainties, Maciej Jan Zawodniok, Q. Shang, Jagannathan Sarangapani
Distributed Power Control For Cellular Networks In The Presence Of Channel Uncertainties, Maciej Jan Zawodniok, Q. Shang, Jagannathan Sarangapani
Electrical and Computer Engineering Faculty Research & Creative Works
In this paper, a novel distributed power control (DPC) scheme for cellular network 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-Interference ratio (SIR), a new DPC scheme, which can estimate the slowly varying channel uncertainty, is proposed so that a target SIR at the receiver can be maintained. Further, the standard assumption of a constant interference during a link's power update used in other works in the literature is relaxed. A CDMA-based cellular network …
Adaptive Distributed Fair Scheduling And Its Implementation In Wireless Sensor Networks, Maciej Jan Zawodniok, Jagannathan Sarangapani, Steve Eugene Watkins, James W. Fonda
Adaptive Distributed Fair Scheduling And Its Implementation In Wireless Sensor Networks, Maciej Jan Zawodniok, Jagannathan Sarangapani, Steve Eugene Watkins, James W. Fonda
Electrical and Computer Engineering Faculty Research & Creative Works
A novel adaptive and distributed fair scheduling (ADFS) scheme for wireless sensor networks is shown through hardware implementation. In contrast to simulation, hardware evaluation provides valuable feedback to protocol and hardware development process. The proposed protocol focuses on quality-of-service (QoS) issues to address flow prioritization. Thus, when nodes access a shared channel, the proposed ADFS allocates the channel bandwidth proportionally to the weight, or priority, of the packet flows. Moreover, ADFS allows for dynamic allocation of network resources with little added overhead. Weights are initially assigned using user specified QoS criteria. These weights are subsequently updated as a function of …
Development And Implementation Of Optimized Energy-Delay Sub-Network Routing Protocol For Wireless Sensor Networks, Maciej Jan Zawodniok, Jagannathan Sarangapani, Steve Eugene Watkins, James W. Fonda
Development And Implementation Of Optimized Energy-Delay Sub-Network Routing Protocol For Wireless Sensor Networks, Maciej Jan Zawodniok, Jagannathan Sarangapani, Steve Eugene Watkins, James W. Fonda
Electrical and Computer Engineering Faculty Research & Creative Works
The development and implementation of the optimized energy-delay sub-network routing (OEDSR) protocol for wireless sensor networks (WSN) is presented. This ondemand routing protocol minimizes a novel link cost factor which is defined using available energy, end-to-end (E2E) delay and distance from a node to the base station (BS), along with clustering, to effectively route information to the BS. Initially, the nodes are either in idle or sleep mode, but once an event is detected, the nodes near the event become active and start forming sub-networks. Formation of the inactive network into a sub-network saves energy because only a portion of …
Neural Network-Based Output Feedback Controller For Lean Operation Of Spark Ignition Engines, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier, Jonathan B. Vance, Pingan He
Neural Network-Based Output Feedback Controller For Lean Operation Of Spark Ignition Engines, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier, Jonathan B. Vance, Pingan He
Electrical and Computer Engineering Faculty Research & Creative Works
Spark ignition (SI) engines running at very lean conditions demonstrate significant nonlinear behavior by exhibiting cycle-to-cycle dispersion of heat release even though such operation can significantly reduce NOx emissions and improve fuel efficiency by as much as 5-10%. A suite of neural network (NN) controller without and with reinforcement learning employing output feedback has shown ability to reduce the nonlinear cyclic dispersion observed under lean operating conditions. The neural network controllers consists of three NN: a) A NN observer to estimate the states of the engine such as total fuel and air; b) a second NN for generating virtual input; …
Neuro Control Of Nonlinear Discrete Time Systems With Deadzone And Input Constraints, Pingan He, Wenzhi Gao, Jagannathan Sarangapani
Neuro Control Of Nonlinear Discrete Time Systems With Deadzone And Input Constraints, Pingan He, Wenzhi Gao, Jagannathan Sarangapani
Electrical and Computer Engineering Faculty Research & Creative Works
A neural network (NN) controller in discrete time is designed to deliver a desired tracking performance for a class of uncertain nonlinear systems with unknown deadzones and magnitude constraints on the input. The NN controller consists of two NNs: the first NN for compensating the unknown deadzones; and the second NN for compensating the uncertain nonlinear system dynamics. The magnitude constraints on the input are modeled as saturation nonlinearities and they are dealt with in the Lyapunov-based controller design. The uniformly ultimate boundedness (UUB) of the closed-loop tracking errors and the neural network weights estimation errors is demonstrated via Lyapunov …
Reinforcement Learning-Based Output Feedback Control Of Nonlinear Systems With Input Constraints, Pingan He, Jagannathan Sarangapani
Reinforcement Learning-Based Output Feedback Control Of Nonlinear Systems With Input Constraints, Pingan He, Jagannathan Sarangapani
Electrical and Computer Engineering Faculty Research & Creative Works
A novel neural network (NN) -based output feedback controller with magnitude constraints is designed to deliver a desired tracking performance for a class of multi-input-multi-output (MIMO) discrete-time strict feedback nonlinear systems. Reinforcement learning in discrete time is proposed for the output feedback controller, which uses three NN: 1) a NN observer to estimate the system states with the input-output data; 2) a critic NN to approximate certain strategic utility function; and 3) an action NN to minimize both the strategic utility function and the unknown dynamics estimation errors. The magnitude constraints are manifested as saturation nonlinearities in the output feedback …
Neural Network-Based Control Of Nonlinear Discrete-Time Systems In Non-Strict Form, Jagannathan Sarangapani, Zheng Chen, Pingan He
Neural Network-Based Control Of Nonlinear Discrete-Time Systems In Non-Strict Form, Jagannathan Sarangapani, Zheng Chen, Pingan He
Electrical and Computer Engineering Faculty Research & Creative Works
A novel reinforcement learning-based adaptive neural network (NN) controller, also referred as the adaptive-critic NN controller, is developed to deliver a desired tracking performance for a class of non-strict feedback nonlinear discrete-time systems in the presence of bounded and unknown disturbances. The adaptive critic NN controller architecture includes a critic NN and two action NNs. The critic NN approximates certain strategic utility function whereas the action neural networks are used to minimize both the strategic utility function and the unknown dynamics estimation errors. The NN weights are tuned online so as to minimize certain performance index. By using gradient descent-based …
Decentralized Discrete-Time Neural Network Controller For A Class Of Nonlinear Systems With Unknown Interconnections, Jagannathan Sarangapani
Decentralized Discrete-Time Neural Network Controller For A Class Of Nonlinear Systems With Unknown Interconnections, Jagannathan Sarangapani
Electrical and Computer Engineering Faculty Research & Creative Works
A novel decentralized neural network (NN) controller in discrete-time is designed for a class of uncertain nonlinear discrete-time systems with unknown interconnections. Neural networks are used to approximate both the uncertain dynamics of the nonlinear systems and the unknown interconnections. Only local signals are needed for the decentralized controller design and the stability of the overall system can be guaranteed using the Lyapunov analysis. Further, controller redesign for the original subsystems is not required when additional subsystems are appended. Simulation results demonstrate the effectiveness of the proposed controller. The NN does not require an offline learning phase and the weights …
A Robust Controller For The Manipulation Of Micro Scale Objects, Qinmin Yang, Jagannathan Sarangapani
A Robust Controller For The Manipulation Of Micro Scale Objects, Qinmin Yang, Jagannathan Sarangapani
Electrical and Computer Engineering Faculty Research & Creative Works
A suite of novel robust controllers is presented for the manipulation and handling of micro-scale objects in a micro-electromechanical system (MEMS) where adhesive, surface tension, friction and van der Waals forces are dominant. Moreover, these forces are typically unknown. The robust controller overcomes the unknown system dynamics and ensures the performance in the presence of actuator constraints by assuming that the upper bounds on these forces are known. On the other hand, for the robust adaptive controller, the unknown forces are estimated online. Using the Lyapunov approach, the uniformly ultimate boundedness (UUB) of the closed-loop manipulation error is shown for …
Energy-Efficient Rate Adaptation Mac Protocol For Ad Hoc Wireless Networks, Maciej Jan Zawodniok, Jagannathan Sarangapani
Energy-Efficient Rate Adaptation Mac Protocol For Ad Hoc Wireless Networks, Maciej Jan Zawodniok, Jagannathan Sarangapani
Electrical and Computer Engineering Faculty Research & Creative Works
Resource constraints in ad hoc wireless networks require that they are energy efficient during both transmission and rate adaptation. In this paper, we propose a novel energy-efficient rate adaptation protocol that selects modulation schemes online to maximize throughput based on channel state while saving energy. This protocol uses the distributed power control (DPC) algorithm (M. Zawodniok et al., 2004) to accurately determine the necessary transmission power and to reduce the energy consumption. Additionally, the transmission rate is altered using energy efficiency as a constraint to meet the required throughput, which is estimated with queue fill ratio. Moreover, back-off scheme is …
Predictive Congestion Control Mac Protocol For Wireless Sensor Networks, Maciej Jan Zawodniok, Jagannathan Sarangapani
Predictive Congestion Control Mac Protocol For Wireless Sensor Networks, Maciej Jan Zawodniok, Jagannathan Sarangapani
Electrical and Computer Engineering Faculty Research & Creative Works
Available congestion control schemes, for example transport control protocol (TCP), when applied to wireless networks results in a large number of packet drops, unfairness with a significant amount of wasted energy due to retransmissions. To fully utilize the hop by hop feedback information, a suite of novel, decentralized, predictive congestion control schemes are proposed for wireless sensor networks in concert with distributed power control (DPC). Besides providing energy efficient solution, embedded channel estimator in DPC predicts the channel quality. By using the channel quality and node queue utilizations, the onset of network congestion is predicted and congestion control is initiated. …
Block Phase Correlation-Based Automatic Drift Compensation For Atomic Force Microscopes, Qinmin Yang, Eric W. Bohannan, Jagannathan Sarangapani
Block Phase Correlation-Based Automatic Drift Compensation For Atomic Force Microscopes, Qinmin Yang, Eric W. Bohannan, Jagannathan Sarangapani
Electrical and Computer Engineering Faculty Research & Creative Works
Automatic nanomanipulation and nanofabrication with an Atomic Force Microscope (AFM) is a precursor for nanomanufacturing. In ambient conditions without stringent environmental controls, nanomanipulation tasks require extensive human intervention to compensate for the many spatial uncertainties of the AFM. Among these uncertainties, thermal drift is especially hard to solve because it tends to increase with time and cannot be compensated simultaneously by feedback. In this paper, an automatic compensation scheme is introduced to measure and estimate drift. This information can be subsequently utilized to compensate for the thermal drift so that a real-time controller for nanomanipulation can be designed as if …
Neural Network -Based Nearly Optimal Hamilton-Jacobi-Bellman Solution For Affine Nonlinear Discrete-Time Systems, Jagannathan Sarangapani, Zheng Chen
Neural Network -Based Nearly Optimal Hamilton-Jacobi-Bellman Solution For Affine Nonlinear Discrete-Time Systems, Jagannathan Sarangapani, Zheng Chen
Electrical and Computer Engineering Faculty Research & Creative Works
In this paper, we consider the use of nonlinear networks towards obtaining nearly optimal solutions to the control of nonlinear discrete-time systems. The method is based on least-squares successive approximation solution of the Generalized Hamilton-Jacobi-Bellman (HJB) equation. Since successive approximation using the GHJB has not been applied for nonlinear discrete-time systems, the proposed recursive method solves the GHJB equation in discrete-time on a well-defined region of attraction. The definition of GHJB, Pre-Hamiltonian function, HJB equation and method of updating the control function for the affine nonlinear discrete time systems are proposed. A neural network is used to approximate the GHJB …
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 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 …
A Distributed Power Control Mac Protocol For Wireless Ad-Hoc Networks., Maciej Jan Zawodniok, Jagannathan Sarangapani
A Distributed Power Control Mac Protocol For Wireless Ad-Hoc Networks., Maciej Jan Zawodniok, Jagannathan Sarangapani
Electrical and Computer Engineering Faculty Research & Creative Works
A novel distributed power control (DPC)⋅ scheme and a MAC protocol for wireless ad hoc networks in the presence of radio channel uncertainties such as path loss, Shadowing and Rayleigh fading is presented. The DPC quickly estimates the time-varying nature of the channel and uses the information to select a suitable transmitter power value in order to maintain a target Signal-to-Interference ratio (SIR) at the receiver. The standard assumption of a constant interference during a link's power update used in other works is relaxed. The performance of the proposed DPC is demonstrated analytically. The power used for all RTS-CTS-DATA-ACK frames …
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 …