Synthesis of neurocontroller for multirotor unmanned aerial vehicle based on neuroemulator

This paper presents a method of creating a neurocontroller based on a multilayer perceptron for an unmanned aerial vehicle. We show how a neural network can effectively emulate dynamic characteristics of an aerial craft. Another network learns to control the emulator, using backpropagation algorithm to calculate the error in its control signal. A set of parameters is used to analyze the efficiency of the stabilization and the weights of the neurocontroller are adjusted accordingly. It is shown that the system meets stabilization requirements with sufficient number of iterations. Described method can be used to remotely control unmanned aerial vehicles operating in changing environment

Design and implementation of a digital neural processor for detection applications

The main focus of this research is to develop a digital neural network (processor) and hardware (VLSI) implementation of the same for detection applications, for example in the distance protection of power transmission lines. Using a hardware neural processor will improve the protection system performance over software implementations in terms of speed of operation, response time for faults etc. The main aspects of this research are software design, performance analysis, hardware design and hardware implementation of the digital neural processor. The software design is carried out by developing an object oriented neural network simulator with backpropagation training using C++ language. A preliminary analysis shows that the inputs to the neural network need to be preprocessed. Two filters have been developed for this purpose, based on the analysis of the training data available. The performance analysis involves studying quantization effects (determination of precision requirements) in the network. -- The hardware design involves design of the neural network and the preprocessors. The neural processor consists of three types of processing elements (neurons): input, hidden and output neurons. The input neurons form the input layer of the processor which receive input from the preprocessors. The input layer can be configured to directly receive external input by changing the mode of operation. The output layer gives the signal to the relay for tripping the line under fault. Each neuron consists of datapath and local control unit. Datapath consists of the components for forward and backward passes of the processor and the register file. The local control unit controls the flow of data within a neuron and co-ordinates with the global control unit which controls the flow of data between layers. The neurons and the layers are pipelined for improving the throughput of the processor. The neural processor and the filters are implemented in VLSI using hardware description language (VHDL) and Synopsys / Cadence CAD tools. All the components are individually verified and tested for their functionality and implemented using 0.5 μ CMOS technology

Enhancing the Structural Performance with Active and Semi-Active Devices Using Adaptive Control Strategy

Changes in the characteristics of the structure, such as damage, have not been considered in most of the active and semi-active control methods that have been used to control and optimize the response of civil engineering structures. In this dissertation, a direct adaptive control which can deal with the existence of measurement errors and changes in structural characteristics or load conditioning is used to control the performance of structures. A Simple Adaptive Control Method (SACM) is modified to control civil structures and improve their performance. The effectiveness of the SACM is verified by several numerical examples. The SACM is used to reduce the structural response such as drift and acceleration using active and semi-active devices, and its performance is compared with that of other control methods. Also, a probabilistic indirect adaptive control method is developed and its behavior is compared to the SACM using a simple numerical example. In addition to the simplicity of the SACM implementation, the results show that SACM is very effective to reduce the response of structures with linear and non-linear behavior in comparison with other control methods