A Comparative Study of Design of Active Fault-Tolerant Control System for Air–Fuel Ratio Control of Internal Combustion Engine Using Particle Swarm Optimization, Genetic Algorithm, and Nonlinear Regression-Based Observer Model

In this article, three distinct strategies for designing an Active Fault-Tolerant Control System (AFTCS) Air-Fuel Ratio (AFR) control of Internal Combustion (IC) engine in a process plant to avoid shutdown, are presented. The proposed AFTCS employs Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Nonlinear Regression (NLR)-based observer model the Fault Detection Isolation (FDI) unit analytical redundancy. A comparison between these techniques is carried out determine least expensive most accurate approach. results show that nonlinear regression produces highly by consuming very low computational power, its response time also as compared GA PSO. obtained NLR requires 99.6% 93.1% less throttle MAP estimation, respectively, reducing estimation error 0.01. simulation system MATLAB/Simulink environment. prove superior fault tolerance performance sensor faults AFR system, especially Manifold Absolute Pressure (MAP) terms oscillatory reported existing literature.