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Full-Text Articles in Engineering
Implementation Of Neuroidentifiers Trained By Pso On A Plc Platform For A Multimachine Power System, Curtis Alan Parrott, Ganesh K. Venayagamoorthy
Implementation Of Neuroidentifiers Trained By Pso On A Plc Platform For A Multimachine Power System, Curtis Alan Parrott, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
Power systems are nonlinear with fast changing dynamics. In order to design a nonlinear adaptive controller for damping power system oscillations, it becomes necessary to identify the dynamics of the system. This paper demonstrates the implementation of a neural network based system identifier, referred to as a neuroidentifier, on a programmable logic controller (PLC) platform. Two separate neuroidentifiers are trained using the particle swarm optimization (PSO) algorithm to identify the dynamics in a two-area four machine power system, one neuroidentifier for Area 1 and the other for Area 2. The power system is simulated in real time on the Real …
Nsf Career: Scalable Learning And Adaptation With Intelligent Techniques And Neural Networks For Reconfiguration And Survivability Of Complex Systems, Ganesh K. Venayagamoorthy
Nsf Career: Scalable Learning And Adaptation With Intelligent Techniques And Neural Networks For Reconfiguration And Survivability Of Complex Systems, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
The NSF CAREER program is a premier program that emphasizes the importance the foundation places on the early development of academic careers solely dedicated to stimulating the discovery process in which the excitement of research enriched by inspired teaching and enthusiastic learning. This paper describes the research and education experiences gained by the principal investigator and his research collaborators and students as a result of a NSF CAREER proposal been awarded by the power, control and adaptive networks (PCAN) program of the electrical, communications and cyber systems division, effective June 1, 2004. In addition, suggestions on writing a winning NSF …
Formation Control Of Car-Like Mobile Robots: A Lyapunov Function Based Approach, S. A. Panimadai Ramaswamy, S. N. Balakrishnan
Formation Control Of Car-Like Mobile Robots: A Lyapunov Function Based Approach, S. A. Panimadai Ramaswamy, S. N. Balakrishnan
Mechanical and Aerospace Engineering Faculty Research & Creative Works
In literature leader - follower strategy has been used extensively for formation control of car-like mobile robots with the control law being derived from the kinematics. This paper takes it a step further and a nonlinear control law is derived using Lyapunov analysis for formation control of car-like mobile robots using robot dynamics. Controller is split into two parts. The first part is the development of a velocity controller for the follower from the error kinematics (linear and angular). The second part involves the use of the dynamics of the robot in the development of a torque controller for both …
Nonlinear H(Infinity) Missile Longitudinal Autopilot Design With Theta-D Method, Ming Xin, S. N. Balakrishnan
Nonlinear H(Infinity) Missile Longitudinal Autopilot Design With Theta-D Method, Ming Xin, S. N. Balakrishnan
Mechanical and Aerospace Engineering Faculty Research & Creative Works
In this paper, a new nonlinear H 1 control technique, called μ¡D H 1 method, is employed to design a missile longitudinal autopilot. The μ ¡D H 1 design has the same structure as that of linear H 1 , except that the two Riccati equations that are part of the solution process are state dependent. The μ ¡D technique yields suboptimal solutions to nonlinear optimal control problems in the sense that it provides an approximate solution to the Hamilton-Jacobi-Bellman (HJB) equation. It is also shown that this method can be used to provide an approximate closed-form solution to the …
Optimal Control Of Class Of Non-Linear Plants Using Artificial Immune Systems: Application Of The Clonal Selection Algorithm, S. A. Panimadai Ramaswamy, Ganesh K. Venayagamoorthy, S. N. Balakrishnan
Optimal Control Of Class Of Non-Linear Plants Using Artificial Immune Systems: Application Of The Clonal Selection Algorithm, S. A. Panimadai Ramaswamy, Ganesh K. Venayagamoorthy, S. N. Balakrishnan
Electrical and Computer Engineering Faculty Research & Creative Works
The function of natural immune system is to protect the living organisms against invaders/pathogens. Artificial Immune System (AIS) is a computational intelligence paradigm inspired by the natural immune system. Diverse engineering problems have been solved in the recent past using AIS. Clonal selection is one of the few algorithms that belong to the family of AIS techniques. Clonal selection algorithm is the computational implementation of the clonal selection principle. The process of affinity maturation of the immune system is explicitly incorporated in this algorithm. This paper presents the application of AIS for the optimal control of a class of non-linear …
Parameter Optimization Of Pss Based On Estimated Hessian Matrix From Trajectory Sensitivities, Jung-Wook Park, Ganesh K. Venayagamoorthy, Seung-Mook Baek
Parameter Optimization Of Pss Based On Estimated Hessian Matrix From Trajectory Sensitivities, Jung-Wook Park, Ganesh K. Venayagamoorthy, Seung-Mook Baek
Electrical and Computer Engineering Faculty Research & Creative Works
This paper describes the optimal tuning for the output limits of the power system stabilizer (PSS), which can improve the system damping performance immediately following a large disturbance. The non-smooth nonlinear parameters such as the saturation limits of the PSS cannot be tuned by the conventional methods based on linear approaches. To implement the systematic optimal tuning for the output limits of the PSS, a feedforward neural network (FFNN) is applied to the hybrid system model based on the differential-algebraic-impulsive-switched (DAIS) structure. The FFNN is firstly designed to identify the trajectory sensitivities obtained from the DAIS structure. Thereafter, it estimates …
Making The Power Grid More Intelligent, Salman Mohagheghi, Ronald G. Harley, Ganesh K. Venayagamoorthy
Making The Power Grid More Intelligent, Salman Mohagheghi, Ronald G. Harley, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
Summary form only given. This paper focuses on the applications of intelligent techniques for improving the performances of the power system controllers. Intelligent control techniques lay the foundation of the next generation of nonlinear controllers and have the advantage of further improving the controller's performance by incorporating heuristics and expert knowledge into its design. Most of these techniques are independent of any mathematical model of the power system, which proves to be a considerable advantage.
Online Reinforcement Learning-Based Neural Network Controller Design For Affine Nonlinear Discrete-Time Systems, Qinmin Yang, Jagannathan Sarangapani
Online Reinforcement Learning-Based Neural Network Controller Design For Affine Nonlinear Discrete-Time Systems, 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 general multi-input and multi- output affine unknown nonlinear discrete-time systems in the presence of bounded disturbances. Adaptive critic designs consist of two entities, an action network that produces optimal solution 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 and the action network is adapted simultaneously based on the information from the critic. In our online learning method, one NN is designated as the …
Reinforcement Learning Based Output-Feedback Control Of Nonlinear Nonstrict Feedback Discrete-Time Systems With Application To Engines, Peter Shih, Jonathan B. Vance, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier
Reinforcement Learning Based Output-Feedback Control Of Nonlinear Nonstrict Feedback Discrete-Time Systems With Application To Engines, Peter Shih, Jonathan B. Vance, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier
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 nonlinear discrete-time systems in the presence of bounded and unknown disturbances. The controller includes an observer for estimating states and the outputs, critic, and two action NNs for generating virtual, and actual control inputs. The critic approximates certain strategic utility function and the action NNs are used to minimize both the strategic utility function and their outputs. All NN weights adapt online towards minimization of a performance index, utilizing gradient-descent based rule. …
Near Optimal Output-Feedback Control Of Nonlinear Discrete-Time Systems In Nonstrict Feedback Form With Application To Engines, Peter Shih, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier
Near Optimal Output-Feedback Control Of Nonlinear Discrete-Time Systems In Nonstrict Feedback Form With Application To Engines, Peter Shih, Brian C. Kaul, Jagannathan Sarangapani, J. A. Drallmeier
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 nonlinear discrete-time systems in the presence of bounded and unknown disturbances. The controller includes an observer for estimating states and the outputs, critic, and two action NNs for generating virtual, and actual control inputs. The critic approximates certain strategic utility function and the action NNs are used to minimize both the strategic utility function and their outputs. All NN weights adapt online towards minimization of a performance index, utilizing gradient-descent based rule. …
Optimal Neuro-Controller Synthesis For Impulse-Driven System, Xiaohua Wang, S. N. Balakrishnan
Optimal Neuro-Controller Synthesis For Impulse-Driven System, Xiaohua Wang, S. N. Balakrishnan
Mechanical and Aerospace Engineering Faculty Research & Creative Works
This paper presents a new controller design technique for systems driven with impulse inputs. Necessary conditions for optimal impulse control are derived. A neural network structure to solve the resulting equations is presented. The solution concepts are illustrated with a few example problems that exhibit increasing levels of difficulty. Two linear problems-one scalar and one vector-and a benchmark nonlinear problem-Van Der Pol oscillator-are used as case studies. Numerical results show the efficacy of the new solution process for impulse driven systems. Since the theoretical development and the design technique are free from restrictive assumptions, this technique is applicable to many …
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, …
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 …
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 …
A Fault-Tolerant P-Q Decoupled Control Scheme For Static Synchronous Series Compensator, Wei Qiao, Ganesh K. Venayagamoorthy, Ronald G. Harley
A Fault-Tolerant P-Q Decoupled Control Scheme For Static Synchronous Series Compensator, Wei Qiao, Ganesh K. Venayagamoorthy, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
Control of nonlinear devices in power systems relies on the availability and the quality of sensor measurements. Measurements can be corrupted or interrupted due to sensor failure, broken or bad connections, bad communication, or malfunction of some hardware or software (referred to as missing sensor measurements in this paper). This paper proposes a fault-tolerant control scheme (FTCS) for a static synchronous series compensator (SSSC). This FTCS consists of a sensor evaluation and (missing sensor) restoration scheme (SERS) cascaded with a P-Q decoupled control scheme (PQDC). It is able to provide effective control to the SSSC when single or multiple crucial …
Neuroadaptive Model Following Controller Design For A Nonaffine Uav Model, Nishant Unnikrishnan, S. N. Balakrishnan
Neuroadaptive Model Following Controller Design For A Nonaffine Uav Model, Nishant Unnikrishnan, S. N. Balakrishnan
Mechanical and Aerospace Engineering Faculty Research & Creative Works
This paper proposes a new model-following adaptive control design technique for nonlinear systems that are nonaffine in control. The adaptive controller uses online neural networks that guarantee tracking in the presence of unmodeled dynamics and/or parameter uncertainties present in the system model through an online control adaptation procedure. The controller design is carried out in two steps: (i) synthesis of a set of neural networks which capture the unmodeled (neglected) dynamics or model uncertainties due to parametric variations and (ii) synthesis of a controller that drives the state of the actual plant to that of a reference model. This method …
An Optimal Dynamic Inversion Approach For Controlling A Class Of One-Dimensional Nonlinear Distributed Parameter Systems, Radhakant Padhi, S. N. Balakrishnan
An Optimal Dynamic Inversion Approach For Controlling A Class Of One-Dimensional Nonlinear Distributed Parameter Systems, Radhakant Padhi, S. N. Balakrishnan
Mechanical and Aerospace Engineering Faculty Research & Creative Works
Combining the principles of dynamic inversion and optimization theory, a new approach is presented for stable control of a class of one-dimensional nonlinear distributed parameter systems, assuming the availability a continuous actuator in the spatial domain. Unlike the existing approximate-then-design and design-then-approximate techniques, here there is no need of any approximation either of the system dynamics or of the resulting controller. Rather, the control synthesis approach is fairly straight-forward and simple. The controller formulation has more elegance because we can prove the convergence of the controller to its steady state value. To demonstrate the potential of the proposed technique, a …
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; …
A Neural Network Based Wide Area Monitor For A Power System, Xiaomeng Li, Ganesh K. Venayagamoorthy
A Neural Network Based Wide Area Monitor For A Power System, Xiaomeng Li, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
With the deregulation of power industry, many tie lines between control areas are driven to operate near their maximum capacity, especially those serving heavy load centers. Wide area controllers (WACs) using wide-area or global signals can provide remote auxiliary control signals to local controllers such as automatic voltage regulators, power system stabilizers, etc to damp out inter-area oscillations. The power system is highly nonlinear system with fast changing dynamics. In order to have an efficient WAC, an online system monitor/predictor is required to provide inter-area information to the WAC from time to time. This paper presents the design of an …
Optimal Control Of A Class Of One-Dimensional Nonlinear Distributed Parameter Systems With Discrete Actuators, Radhakant Padhi, S. N. Balakrishnan
Optimal Control Of A Class Of One-Dimensional Nonlinear Distributed Parameter Systems With Discrete Actuators, Radhakant Padhi, S. N. Balakrishnan
Mechanical and Aerospace Engineering Faculty Research & Creative Works
Combining the principles of dynamic inversion and optimization theory, a new approach is presented for stable control of a class of one-dimensional nonlinear distributed parameter systems with a finite number of actuators in the spatial domain. Unlike the existing ''approximate-then-design'' and ''design-then-approximate'' techniques, this approach does not use any approximation either of the system dynamics or of the resulting controller. The formulation has more practical significance because one can implement a set of discrete controllers with relative ease. To demonstrate the potential of the proposed technique, a real-life temperature control problem for a heat transfer application is solved through simulations. …
A Neural Network Based Optimal Wide Area Control Scheme For A Power System, Ganesh K. Venayagamoorthy, Swakshar Ray
A Neural Network Based Optimal Wide Area Control Scheme For A Power System, Ganesh K. Venayagamoorthy, Swakshar Ray
Electrical and Computer Engineering Faculty Research & Creative Works
With deregulation of the power industry, many tie lines between control areas are driven to operate near their maximum capacity, especially those serving heavy load centers. Wide area control systems (WACSs) using wide-area or global signals can provide remote auxiliary control signals to local controllers such as automatic voltage regulators, power system stabilizers, etc to damp out inter-area oscillations. This paper presents the design and the DSP implementation of a nonlinear optimal wide area controller based on adaptive critic designs and neural networks for a power system on the real-time digital simulator (RTDS©). The performance of the WACS as a …
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 …
Development And Analysis Of A Feedback Treatment Strategy For Parturient Paresis Of Cows, Radhakant Padhi, S. N. Balakrishnan
Development And Analysis Of A Feedback Treatment Strategy For Parturient Paresis Of Cows, Radhakant Padhi, S. N. Balakrishnan
Mechanical and Aerospace Engineering Faculty Research & Creative Works
An intelligent on-line feedback treatment strategy based on nonlinear optimal control theory is presented for the parturient paresis of cows. A limitation in the development of an existing nonlinear mathematical model for the homogeneous system is addressed and further modified to incorporate a control input. A neural network based optimal feedback controller is synthesized for the treatment of the disease. Detailed studies are used to analyze the effectiveness of a feedback medication strategy and it is compared with the current "impulse" strategy. The results show that while the current practice may fail in some cases, especially if it is carried …
Stationkeeping Of An L₂ Libration Point Satellite With Θ-D Technique, Ming Xin, S. N. Balakrishnan, Henry J. Pernicka, Michael W. Dancer
Stationkeeping Of An L₂ Libration Point Satellite With Θ-D Technique, Ming Xin, S. N. Balakrishnan, Henry J. Pernicka, Michael W. Dancer
Mechanical and Aerospace Engineering Faculty Research & Creative Works
A new method for L2 libration-point orbit stationkeeping is proposed in this paper using continuous thrust. The circular restricted three-body problem with Sun and Earth as the two primaries is considered. The unstable orbit about the L2 libration-point requires stationkeeping maneuvers to maintain the nominal path. In this study, an approach, called the "θ-D technique," based on optimal control theory gives a closed-form suboptimal feedback solution to solve this nonlinear control problem. In this approach the Hamiltonian-Jacobi-Bellman (HJB) equation is solved approximately by adding some perturbations to the cost function. The controller is designed such that the actual …
New External Neuro-Controller For Series Capacitive Reactance Compensator In A Power Network, Jung-Wook Park, Ganesh K. Venayagamoorthy, Ronald G. Harley
New External Neuro-Controller For Series Capacitive Reactance Compensator In A Power Network, Jung-Wook Park, Ganesh K. Venayagamoorthy, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
The controllable capacitive reactance can be used as the input variable for the external controller of a series capacitive reactance compensator (SCRC) to improve the damping of low-frequency oscillations of the rotor angle and active power in a power system. Conventional linear PI controllers are tuned for best performance at one specific operating point of the nonlinear power system. At other operating point its performance degrades. Nonlinear optimal neuro-controllers are able to overcome this degradation. In this paper, the dual heuristic dynamic programming (DHP) optimization algorithm is applied to design an external nonlinear optimal neuro-controller for the SCRC. Simulation studies …
Optimal Control Synthesis Of A Class Of Nonlinear Systems Using Single Network Adaptive Critics, Radhakant Padhi, Nishant Unnikrishnan, S. N. Balakrishnan
Optimal Control Synthesis Of A Class Of Nonlinear Systems Using Single Network Adaptive Critics, Radhakant Padhi, Nishant Unnikrishnan, S. N. Balakrishnan
Mechanical and Aerospace Engineering Faculty Research & Creative Works
Adaptive critic (AC) neural network solutions to optimal control designs using dynamic programming has reduced the need of complex computations and storage requirements that typical dynamic programming requires. In this paper, a "single network adaptive critic" (SNAC) is presented. This approach is applicable to a class of nonlinear systems where the optimal control (stationary) equation is explicitly solvable for control in terms of state and costate variables. The SNAC architecture offers three potential advantages; a simpler architecture, significant savings of computational load and reduction in approximation errors. In order to demonstrate these benefits, a real-life micro-electro-mechanical-system (MEMS) problem has been …
Development And Implementation Of New Nonlinear Control Concepts For A Ua, Vijayakumar Janardhan, Derek Schmitz, S. N. Balakrishnan
Development And Implementation Of New Nonlinear Control Concepts For A Ua, Vijayakumar Janardhan, Derek Schmitz, S. N. Balakrishnan
Mechanical and Aerospace Engineering Faculty Research & Creative Works
A reconfigurable flight control method is developed to be implemented on an Unmanned Aircraft (UA), a thirty percent scale model of the Cessna 150. This paper presents the details of the UAV platform, system identification, reconfigurable controller design, development, and implementation on the UA to analyze the performance metrics. A Crossbow Inertial Measurement Unit provides the roll, pitch and yaw accelerations and rates along with the roll and pitch. The 100400 mini-air data boom from spaceage control provides the airspeed, altitude, angle of attack and the side slip angles. System identification is accomplished by commanding preprogrammed inputs to the control …
Missile Longitudinal Autopilot Design Using A New Suboptimal Nonlinear Control Method, Ming Xin, S. N. Balakrishnan
Missile Longitudinal Autopilot Design Using A New Suboptimal Nonlinear Control Method, Ming Xin, S. N. Balakrishnan
Mechanical and Aerospace Engineering Faculty Research & Creative Works
A missile longitudinal autopilot is designed using a new nonlinear control synthesis technique called the θ-D approximation. The particular θ-D methodology used is referred to as the θ-D H2 design. The technique can achieve suboptimal closed-form solutions to a class of nonlinear optimal control problems in the sense that it solves the Hamilton-Jacobi-Bellman equation approximately by adding perturbations to the cost function. An interesting feature of this method is that the expansion terms in the expression for suboptimal control are nothing but solutions to the state-dependent Riccati equations associated with this class of problems. The θ-D H2 design has the …
A Novel Dual Heuristic Programming Based Optimal Control Of A Series Compensator In The Electric Power Transmission System, Jung-Wook Park, Ganesh K. Venayagamoorthy, Ronald G. Harley
A Novel Dual Heuristic Programming Based Optimal Control Of A Series Compensator In The Electric Power Transmission System, Jung-Wook Park, Ganesh K. Venayagamoorthy, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
In this paper, the dual heuristic programming (DHP) optimization algorithm is used for the design of a nonlinear optimal neurocontroller that replaces the proportional-integral (PI) based conventional linear controller (CONVC) in the internal control of a power electronic converter based series compensator in the electric power transmission system. The performance of the proposed DHP based neurocontroller is compared with that of the CONVC with respect to damping low frequency oscillations. Simulation results using the PSCAD/EMTDC software package are presented.