On the issue of LQG embedded control realization in a Maglev system

Abstract

Sensor selection in control design receives substantial interest in the last few years. We disseminate work on Field Programmable Gate Array (FPGA)-based embedded software platform validating a systematic sensor selection framework and target efficient FPGA resource allocation. Sensor selection combines multi-objective optimization, Linear-Quadratic-Gaussian (LQG) control, applied to a Maglev suspension. The nonlinear Maglev model is realized on software platform forming a Hardware-in-the-loop (HIL) as an economic and reliable validation platform for the design setup. The LQG controller was modeled in fixed point, described in Verilog Hardware Description Language (HDL) and tied up with an ethernet core to form an FPGA-in-the-loop system prior to logic synthesis and FPGA place and route. The results illustrate efficient FPGA resource allocation level pertinent to extending to a core sensor fault tolerant scheme

Execution Time of Optimal Controls in Hard Real Time, a Minimal Execution Time Solution for Nonlinear SDRE

“…In literature, progress based on sensor selection in control design receives a substantial interest in the last few years. For example, in [24] a Linear-Quadratic-Gaussian (LQG) control is applied to a Maglev suspension and it has been pointed out the significance of achieving also, for RT scenario, even more performing solutions to combine multi-objective optimization through an adequate sensor choice. Regarding the control techniques, different works deal with optimal control laws and suggest a discrete implementation on micro-controllers [10].…”

“…we refer to (16) as the discrete-time linear plant model (24). In this paper, the state feedback controller is designed using the linear quadratic regulator and the discrete-time linear model of the system (16).…”