## 2 Literature Review  
Kalman filters, fuzzy logic, neural networks, regression, and other techniques may be used to construct an FDI unit. In the case of sensor and actuator faults coexisting, Kalman filters are used in [7] for Fault Detection and Location (FDL) in a nonlinear model of an aeroengine. The proposed system uses hybrid Kalman filters and only detects the faults and does not estimate the sensor values in case of a fault. Another Kalman filter-based approach is proposed in [8], where a hybrid FTCS is used to introduce analytical redundancy in an engine to maintain the Air-Fuel Ratio (AFR). An active FTC system is proposed in [9], in which an Adaptive Interval Observer (AIO)-based FDI is used to accomplish trajectory tracking of an unmanned underwater vehicle that is susceptible to sensor failures and many uncertainties. The proposed AIO is inefficient in terms of computation power. In [10], the FDI uses fuzzy logic to predict nonlinear functions, and adaptive control is used to correct bias and obtain faults in the actuator. The proposed system may accept inaccurate data and inputs; hence, the accuracy may be compromised. [PERSON] and [PERSON] [11] suggested an FDI method that manages sporadic observations if the full measurements of data are unavailable. A linear regression-based robust AFTCS approach can be found in [12] for the AFR control of an IC engine. In the proposed approach, the nonlinear sensor values are estimated by using linear regression; hence, the system produces less accurate results. [PERSON] et al. [13] investigated the output degradations caused by incipient faults and predicted the residual life in a mechatronic device in a case study. Two analytical redundancy connections were discussed; these would boost the controlled system's separation capacity when multiple faults occurred. Reference [14] proposes an adaptive fault-tolerant approach for synthesizing a compensation controller for uncertain nonlinear pure-feedback systems with dead-zone actuators and stochastic failures. The failure mode of the actuator is characterized by a scalar Markovian-type function, and the compensation strategy is designed using adaptive backstepping methods. In [15], an Artificial Neural Network (ANN)-based approach is used to estimate the sensor values in case of a fault to prevent the engine shutdown, but the critical points (on which the estimation error may be high) are excluded from the analysis. The topic of command filtering-based event-triggered adaptive fuzzy control for a class of stochastic nonlinear systems with stochastic failures and input saturation is investigated in [16]. FuzzyLogic Systems (FLSs) are used to approximate unknown nonlinear functions and system dynamic changes induced by stochastic errors. The command filtering design approach is used to decrease the computational overhead. The average dwell-time technique is used to examine the AFTCS problem for a class of switched nonlinear systems in [17], in which neural networks are used to design the controller. Reference [18] describes a fault-tolerant control approach for singular systems in the presence of multiplicative failures based on control performance. Reference [19] discusses the design of an AFTCS for spacecraft attitude control with actuator defects, fault estimation errors, and control input limitations. For sensor errors in air-fuel ratio management of internal combustion engines, fuzzy-logic-based AFTCS was developed in [20] but it consisted only of an active part having computational inefficiency. In [21], AFTCS was proposed for both sensor and actuator faults with mathematical relationship observers and redundant actuators for the anti-surge control system of centrifugal compressors. The approach suggested is less sensitive due to substantial computational cost, and undesirable transients in switching the control strategy from a regular controller to a reconfigurable controller once the problem is effectively diagnosed, which are not prevented throughout this investigation.