## 1 Introduction  
Electric aircrafts increase safety and reliability, impact the environment positively, and enhance system efficiency. Extensive efforts have been made to develop reliable and efficient onboard generation systems [1]. Typically, in aviation systems, the starting (through the devoted motor) and generation (through the generator) functionalities are decoupled, therefore increasing cost, size, weight, and complexity [2, 3]. An integrated starter/generator (ISG) has been introduced as a possible solution, either using a permanent magnet machine or a switched reluctance machine [4, 5]. While the first has high power Switched Reluctance Generators (SRGs) are used in various industrial applications, such as aviation power systems, electrical traction, battery charging, and wind power generation. In addition, thanks to the absence of windings and/or permanent magnets on the rotor, SRG offers several advantages over other generator types, including high power density, superior performance over a wide speed range, low production cost, high efficiency, and high fault tolerance [11; 12; 13; 14]. The SRG works on the decreasing part of its inductance profile [15; 16]. The SRG drive system consists of the machine, the converter, and the controller. Due to their attractive features, SRGs have been proposed for electric vehicles, wind energy, and aircraft applications [6; 17]. The generation mode of SRM is enabled by exciting different phases during the negative slope of the inductance curve. The drive circuit excites different phases at regular intervals, representing the excitation period that extends between turn-on, \(\theta_{\text{on}}\), and turn-off, \(\theta_{\text{off}}\) angles. The generation period is the interval between turn-off, \(\theta_{\text{off}}\), and when the phase current ceases and reaches zero [18]. Several control strategies for SRG over a wide speed range at constant output voltage have been provided in [19; 20]. Various studies dealing with turn-on/-off angles to improve machine efficiency have also been introduced [21; 22; 23; 24], while the angle control for maximum output power is achieved in [25]. Detecting the turn-on/off angles to deliver maximum power was used in [23; 26]. A fuzzy logic controller has been employed to control the output power by regulating the turn-on angle while keeping a constant turn-off angle [27; 28; 29; 30]. The Proportional-Integral-Derivative controller, PID, is commonly used in industrial applications [21; 31; 32], increasing system responsiveness, reducing steady-state error, and enhancing system stability. Particle swarm optimization (PSO) [33] is an effective optimization method. In addition to its simplicity of implementation, PSO can address optimization problems with multiple local optima. PSO is a bio-inspired algorithm that is simple in searching for an optimal solution in the solutions space. PSO has an inherent guidance strategy that allows PSO solutions to obtain essential data from improved solutions for further enhancement. The objective function is independent of the gradient of the objective with reduced hyperparameters, allowing for faster convergence. PSO in electric drive systems has been explored extensively in the literature. To estimate the electrical and mechanical properties of PMSMs, the PSO algorithm is used along with learning techniques [34]. Based on the PSO method, a rapid and systematic approach for estimating the mechanical parameters of SRM has been proposed [35]. Using the PSO approach, maximum power extraction from an SRG-based wind power-producing system was achieved [36]. The PID controller generally increases system responsiveness, steady-state error, and system stability. In addition, the PID parameters are affected by system characteristics. As a result, optimum PID settings are required to achieve the intended performance. PID settings may be fine-tuned in two ways: Traditionally and intelligently.