Neural Network Controller for Manipulation of Micro-Scale Objects

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 theoretical conclusions

Nonlinear Control Concepts for a UA

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 UA 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 100-400 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 surfaces and correlating the corresponding variations at the outputs. A Single Network Adaptive Critic, which is a neural network-based optimal controller, is developed as part of a nonlinear flight control system. An online learning neural network is augmented to form an outer loop to reconfigure and supplement the optimal controller to guarantee a practical stability for the airplane. This paper also presents some simulations from the hardware-in-the-loop testing and concludes with an analysis of the flight performance metrics for the controller under investigation

Development and Implementation of New Nonlinear Control Concepts for a UA

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 surfaces and correlating the corresponding variations at the outputs. A Single Network Adaptive Critic, which is a neural network based optimal controller, is developed as part of a nonlinear flight control system. An online learning neural network is augmented to form an outer loop to reconfigure and supplement the optimal controller to guarantee a practical stability for the airplane. This paper also presents some simulations from the hardware-in-the-loop testing and concludes with an analysis of the flight performance metrics for the controller under investigation

Design and implementation of controllers for a class of electromechanical systems

A differentially steered nonholonomic mobile robot platform has been fabricated to implement and validate nonlinear control laws. The platform can also be used to validate hierarchical cooperative control techniques. As a first step towards building a fleet of autonomous robots, novel control methods are developed and implemented on the robot platform for trajectory tracking as well as point stabilization. As an extension to the development of nonlinear controllers, a novel reinforcement learning based neural network is developed to control a manipulation task for a micro-electro-mechanical system (MEMS). The nonlinear controllers developed for both the robot and the MEMS have online neural networks to compensate for the unknown nonlinear system dynamics --Abstract, page iv

Implementation and validation of adaptive-critic based optimal neuro-controller on a multi-input multi-output system

In this study, an adaptive critic-based neural network is developed for optimal control of structures and implemented on a Multi-Input Multi-output (MIMO) system in an attempt to minimize its vibrations. This MIMO system consists of two vertically suspended cantilever beams interconnected by three blocks of wood and is embedded with piezoelectric sensors and actuators and interfaced to a PC with a DAS 1602 data acquisition card. The system model is first acquired by experimental methods. The adaptive critic-based controller for the system model consists of two multi-layer perceptron networks called the action and the critic networks. The action network outputs the optimal control while the critic network outputs the co-state vector and critiques the control output from the action network. The networks are trained offline for a wide range of initial conditions using the equations obtained by Dynamic Programming methodology. After a required degree of convergence is achieved the resulting action network is used for optimal control in a feedback loop. Since all the states are not available for feedback, a Kalman estimator is designed for state estimation. Using the state estimates the neuro-controller is implemented on the structure and the experimental results are obtained. These results are compared with that of an optimal output feedback controller. A reduced order model of the system is then determined and a neurocontroller is designed for the same and implemented on the system. A robust controller is also designed for the second order system to provide “extra control” when there exists uncertainties in the system that makes the existing controller lose its optimality. The performance characteristic of the reduced order neuro-controller is compared with the full-order neuro-controller and the robustness of the neuro-controller is analyzed --Abstract, page 3

Implementation of Nonlinear Reconfigurable Controllers for Autonomous Unmanned Vehicles

Reconfigurable control will play a major role in the advancement of aerospace control especially in the realm of autonomous air vehicles. This paper consists of the development and future implementation of three nonlinear controllers. A modified dynamic inversion controller will be used for validation of the flight hardware. A Single Network Adaptive Critic (SNAC) controller will then be implemented to test its viability and robustness. Lastly, an outer loop controller in the form of an online learning neural network will be developed and implemented to supply extra control to the actual aircraft if a change in system dynamics is detected. To aid in system identification, parameter estimation will be performed before any autonomous flights are performed. The results of this project will validate the use of a SNAC controller for autonomous flight as well as prove the viability of an outer loop extra controller to account for changes in system dynamics

Whole genome characterization of Jaagsiekte Sheep Retrovirus isolated from OPA infected sheep (India) and structural assessment (In-silico) of proteins of JSRV

The study was conducted from 2017 to 2021 in sheep and goat population from Krishna, Guntur and Chittoor districts of Andhra Pradesh. A total of 1256 clinically unhealthy animals out of which 420 animals were manifested respiratory emaciated symptoms based on wheelbarrow test, all aged between 3 months to 4 years. Molecular diagnosis confirmed 38 samples out of 420 samples confirmed by wheelbarrow test. The prevalence rate of JSRV induced OPA is 3.02 % the less infection rate was observed in male as compared to females’ animals and this prevalence during the late winter seasons. The size of 7462 bp genome was sequenced from pathomorphological diversified animal lung sample. The genomic analysis reveals a total of 506 SNPs variants were recorded. The phylogenetic studies reveal, the isolated strain is very similar to South African strains but it diversified from UK and China strain. The structural and functional assessment studies based on derived amino acid sequence showed nonsignificant changes in 3D structures of proteins encoded by the JSRV genome. The substitution mutations both at Nucleotide and Amino acid sequence level suggest neutral evolution and they are under minimal evolutionary pressure

Research and Development of a Hybrid Rapid Manufacturing Process

This paper presents the research and development of a hybrid rapid manufacturing process being developed at the University of Missouri--Rolla. This process includes a laser deposition and a 5- axis CNC milling system. By combining laser deposition and machining processes, the resulting hybrid process can provide greater build capability and better accuracy and surface finish. The hybrid process can build some features that are difficult to build in using a purely deposition processes. The issues and related approaches in the research and development of the hybrid deposition-machining process, including laser deposition process, system design and integration, process planning, and sensor selection and control, are discussed