## 1 Introduction  
Nowadays, the industrialized world needs good accuracy with fast manipulation [1]. Therefore, a high-performance actuator with an efficient control system becomes essential [2, 3]. The brushless DC (BLDC) motor considers a permanent magnet synchronous machine that gives the advantage of the high starting torque and wide range of speeds [4, 5]. Also, the construction simplicity of the BLDC motor drive system offers several advantages not introduced with brushed DC motors in terms of low rotor inertia and size [6, 7]. Currently, the BLDC motor can be utilized in several engineering applications such as unmanned aeronautical vehicles (UAVs), industrial robots, CNC machines, electric vehicles, and servomechanism systems [8, 9]. The conventional PID controller uses on a wide scale in industrial applications due to its simplicity, reliability, and the easy tuning of its parameters [10]. The challenge of a conventional PID controller is the selection of the proper parameters for a certain application [11, 12]. There are several techniques to find the PID controller parameterssuch as trial and error and Ziegler and Nichols methods [13; 14]. However, these previous techniques give an approximate solution. Currently, optimization techniques can be used to find the optimal values of the PID controller parameters [15; 16]. There are different types of optimization techniques such as Genetic Algorithm (GA) which have been employed successfully to solve complex optimization problems, Particle Swarm Optimization (PSO), Ant Colony optimization, and Harmony Search (HS) optimization [17; 18]. The coronavirus (COVID-19) is considered an unknown respiratory virus, revealed in humans in December 2019, that has spread worldwide with more than 10 million infected people so far. This virus can infect people and these people can either die, infect other people, or recover after the infection. Vaccines and the immune defense system usually fight the disease and help to alleviate their effects while an individual is still infected [19]. This behavior can be modeled by a SIR model, consisting of three categories of individuals: S presents the number of susceptible, I stands for the number of infectious, and R is the number of recovered. Metaheuristics need to deal with huge search spaces, even infinite for the continuous cases, and must find suboptimal solutions in reasonable execution times [20]. The fast propagation of the coronavirus along with its ability to infect most of the countries in the world rapidly has inspired the novel metaheuristic proposed in this work, named Coronavirus Optimization Algorithm (CVOA). The proposed CVOA is used to find the optimal parameters for the PID controller. The vaccines stand for the used objective function which can treat the infected patients. A study case is included in this work to discuss the CVOA performance. CVOA has been used to find the optimal values for the PID controller parameters based on a multi-objective function that contains the desired rise time, desired settling time, desired maximum overshoot, and desired steady-state error. The purpose of the control system is to accurately track a preselected reference speed for BLDC motor under different types of loads. A Comparative study using Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), and Genetic Algorithms was presented in [21]. The results showed that the proposed GWO algorithm has a lower root mean square compared to PSO and GA, and therefore, it could be an effective method for optimal PID controllers for power converters applications. The position control of BLDC motor using HS Algorithm optimization technique was demonstrated in [22]. It demonstrates that the HS method can improve the dynamic performance of the system in a better way. The design of a PSO optimized PID controller for the process liquid level control was illustrated in [23]. The computer simulation results show far better settling time and peak values with PSO optimized PID control, thus ensuring overall higher efficiency.