Abstract
Fuzzy logic systems have been widely used for controlling nonlinear and complex dynamic systems by programming heuristic knowledge. But these systems are computationally complex and resource intensive. This paper presents a technique of development and porting of a fuzzy logic approximation PID controller (FLAC) in an automatic cruise control (ACC) system. ACC is a highly nonlinear process and its control is trivial due to the large change in parameters. Therefore, a suitable controller based on heuristic knowledge will be easy to develop and provide an effective solution. But the major problem with employing fuzzy logic controller (FLC) is its complexity. Moreover, the designing of Rulebase requires efficient heuristic knowledge about the system which is rarely found. Therefore, in this paper, a novel rule extraction process is used to derive a FLAC. This controller is then ported on a C6748 DSP hardware with timing and memory optimization. Later, it is seamlessly connected to a network to support remote reconfigurability. A performance analysis is drawn based on processor-in loop test with Simulink model of a cruise control system for vehicle.
Highlights
Fuzzy logic controllers (FLC) have gained high appreciation in nonlinear control [1, 2] Proportional-IntegralDerivative (PID) controllers and model predictive controllers (MPC) are widely used in industrial processes [3,4,5]
This paper introduces a generic fuzzy logic controller system (G-FLCS) which extracts fuzzy control parameter (FCP) from an input-output relationship of a plant using genetic algorithm and provides a web based user interface (WebUI) for users to tweak these parameters for fine tuning of the fuzzy control system
automatic cruise control (ACC) has been used in this paper as an example subsystem of a vehicle, but this feature of remote reconfigurability can be extended to other modules of vehicle automation and advance driver assistance systems (ADAS)
Summary
Fuzzy logic controllers (FLC) have gained high appreciation in nonlinear control [1, 2] Proportional-IntegralDerivative (PID) controllers and model predictive controllers (MPC) are widely used in industrial processes [3,4,5] This is mainly due to its aptitude which can counter nonlinear control problems by programming heuristic knowledge. This paper introduces a generic fuzzy logic controller system (G-FLCS) which extracts FCP from an input-output relationship of a plant using genetic algorithm and provides a web based user interface (WebUI) for users to tweak these parameters for fine tuning of the fuzzy control system. This system provides a remote accessibility and control over systems.
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