A CMOS digital microcircuit, utilizing sub-micron technology, was designed for the purpose of providing integrated control circuitry to an array of high-voltage micro-electromechanical systems (MEMS) switch based phase shifter devices. The grid of MEMS phase shifters is part of a phased array antenna system. The phase of each element of the array is controlled via the CMOS microcircuit. This paper presents the design, physical layout, and the measured test results for CMOS digital control circuit that has been fabricated using the MOSIS Integrated Circuit Fabrication Service. The circuit converts serial data to parallel outputs to reduce number of control lines and lower the cost of wiring the phased array. In addition, discrete digital control circuitry for loading phase shifts into each MEMS device is presented.
This paper starts with an overview of a classical PID controller design. An account of how Neural Networks may be incorporated to provide control is such a setup. The example used in this paper is the problem of controlling a High Frequency Acoustics Platform (HFAP) in-flight. The HFAP is towed by a ship and flown in the water behind the ship to acquire acoustic data reflected from the sea floor. The stability of such a platform is of prime importance to the accuracy of data collected. Using fight data from previous runs of the platform, a Neural Network is trained. The trained network is then used to predict the behavior of the platform. These predictions may then be directly translated to control signals minimizing the platform's spatial deviations. In this paper results form the trained Neural Network on predicting the behavior of the platform are displayed. Network prediction results illustrating the ability of the network to operate with partial input are displayed. Displaying these results in contrast with conventional controller results given the same input parameters emphasizes the importance of such a feature. Finally the use of different network architectures and the cost of using these network, in terms of computing power is investigated.
This paper addresses the impact of neural networks on autonomous systems. Some neural network models are used to illustrate the effectiveness and suitability of these networks for space exploration. Fault tolerance and self learning capabilities of neural networks are used to illustrate such suitability. The advantages and disadvantages of the utilization of neural networks in autonomous systems are discussed and contrasted with the conventional systems currently in use.