The University of Texas System

Frank L Lewis

Anthony J Calise

Dan O Popa

Ai Qun Liu

Michael B McFarland

Bruno Borovic

Javier Campos

Kyriakos Vamvoudakis

Rastko R Selmic

Vahidreza Nasirian

Ayla Gurel

Draguna L Vrabie

Octavian C Pastravanu

Ali Davoudi

Diego A Tacconi

Sarangapani Jagannathan

Disclosed are systems and methods relating to managing and sharing resources within a spatially-distributed electrical power network in a fully distributed fashion. The electrical power network includes source nodes each having a power source and a local controller. The electrical power network includes a physical layer where the source nodes are connected to a power distribution network including one or more loads. The electrical power network also includes a communication layer for communicating power information between source nodes and neighbor source nodes of the electrical power network.

System and method for distributed control of an electrical network

Various embodiments are disclosed for optimal online adaptive control. One such method includes a cost function determination by a critic network coupled to the system under control. The cost function is one produces a minimum value for a cost of the system under control when applied by an action network. The method also includes a control input determination by an action network. The control input determination uses the cost function to determine a control input to apply to the system under control. The control input is one that produces the minimum value for the cost of the system under control. The method also includes simultaneously tuning respective parameters of the critic network and the action network by applying respective tuning laws that do not involve the system dynamics function f(x) for the system under test.

Optimal online adaptive controller

Methods and apparatuses for sensing and adjusting lateral motion in a comb drive actuated MEMS device are provided. If lateral motion is sensed by a lateral motion sensor coupled to the comb drive actuated MEMS device, and the lateral motion is greater than a reference value, a feedback controller adjusts the lateral motion by providing a drive signal to a comb drive electrode of a comb drive actuator.

Systems and methods for improved control of micro-electrical-mechanical system (MEMS) electrostatic actuator

Methods and apparatuses for backlash compensation. A dynamics inversion compensation scheme is designed for control of nonlinear discrete-time systems with input backlash. The techniques of this disclosure extend the dynamic inversion technique to discrete-time systems by using a filtered prediction, and shows how to use a neural network (NN) for inverting the backlash nonlinearity in the feedforward path. The techniques provide a general procedure for using NN to determine the dynamics preinverse of an invertible discrete time dynamical system. A discrete-time tuning algorithm is given for the NN weights so that the backlash compensation scheme guarantees bounded tracking and backlash errors, and also bounded parameter estimates. A rigorous proof of stability and performance is given and a simulation example verifies performance. Unlike standard discrete-time adaptive control techniques, no certainty equivalence (CE) or linear-in-the-parameters (LIP) assumptions are needed.

Backlash compensation with filtered prediction in discrete time nonlinear systems by dynamic inversion using neural networks

Methods and systems for backlash compensation. Restrictive assumptions on the backlash nonlinearity (e.g. the same slopes of the lines, etc.) are not required. The compensator scheme has dynamic inversion structure, with a neural network in the feedforward path that approximates the backlash inversion error plus filter dynamics needed for backstepping design. The neural network controller does not require preliminary off-line training. Neural network tuning is based on a modified Hebbian tuning law, which requires less computation than backpropagation. The backstepping controller uses a practical filtered derivative, unlike the usual differentiation required by earlier backstepping routines. Rigorous stability proofs are given using Lyapunov theory. Simulation results show that the proposed compensation scheme is an efficient way of improving the tracking performance of a vast array of nonlinear systems with backlash.

Backlash compensation using neural network

The invention comprises in various embodiments controllers and apparatus for testing flexible manufacturing systems and methods of operating same. The controller includes a computer, a program, output lines from the computer, and devices connected to the output lines. The program uses matrix logic equations to determine when to start and stop jobs. The devices start and stop individual resources in response to logic signals from the computer. In one embodiment, the controller obtains sensor feedback on the distribution of work pieces and available resources in the manufacturing system. The invention also includes an apparatus and methods for testing a flexible manufacturing system. In response to the input of initial data, the apparatus for testing a flexible manufacturing system solves matrix equations and determines the activity and/or production of the manufacturing system. The apparatus for testing a flexible manufacturing system sends logical signals to a recording device which, in one embodiment, produce a cumulative display of the time dependent production or activity of the system.

Method and apparatus for testing and controlling a flexible manufacturing system

A family of novel multi-layer discrete-time neural net controllers is presented for the control of an multi-input multi-output (MIMO) dynamical system. No learning phase is needed. The structure of the neural net (NN) controller is derived using a filtered error/passivity approach. For guaranteed stability, the upper bound on the constant learning rate parameter for the delta rule employed in standard back propagation is shown to decrease with the number of hidden-layer neurons so that learning must slow down. This major drawback is shown to be easily overcome by using a projection algorithm in each layer. The notion of persistency of excitation for multilayer NN is defined and explored. New on-line improved tuning algorithms for discrete-time systems are derived, which are similar to e-modification for the case of continuous-time systems, that include a modification to the learning rate parameter plus a correction term. These algorithms guarantee tracking as well as bounded NN weights. An extension of these novel weight tuning updates to NN with an arbitrary number of hidden layers is discussed. The notions of discrete-time passive NN, dissipative NN, and robust NN are introduced.

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Arlington, TX, United States of America

Frank L Lewis