## 1 Introduction  
Despite the development of more refined control techniques, the Proportional, Integral, and Derivative (PID) control continues to be ubiquitous for industrial control [1; 2]. Given the practical relevance of this type of control, many design methods have been proposed since the pioneering work developed by [PERSON] and [PERSON] [3]. PID tuning rules were developed for specific aspects, such as: Control modes (P, PI, PD, or PID); types of system model characteristics or forms (first order plus time delay, second order plus time delay, non-minimum phase, oscillatory, etc. ); PID controller structure (parallel, series, with filters, with two-degrees of freedom, etc. ); and anti-windup schemes [4; 5; 6; 7]. The PID control practical relevance also motivates inclusion in most introductory feedback control courses, for example [8]. Artificial neural networks, fuzzy logic, and evolutionary computation have been successfully applied for the PID controller design. The ever-increasing computational power enables a fast practical implementation of computer-based PID controller design methodologies. Optimization-based techniques have advantages over classical tuning methodologies, as the former can be used independently of both system dynamics and PID control structure. Examples of the most well-established population-based algorithms which seek inspiration from natural phenomena are: Genetic Algorithm(GA) [9], Ant-Colony Optimization, (ACO) [10], Genetic Programming (GP) [11], Differential Evolution (DE) [12], Particle Swarm Optimization (PSO) [13], Cuckoo Search Algorithm (CS) [14], Firefly Algorithm (FA) [15], Glowworm Swarm Optimization (GSO) [16], Gravitational Search Algorithm (GSA) [17], Grey Wolf Optimization (GWO) [18; 19; 20; 21], and Elephant Herding Optimization (EHO) [22]. From this set, the ones most used within control design applications are GA [23; 24] and more recently the PSO [25; 26]. PSO has been improved as a result of significant research effort [27; 28]. A commonly used argument against using bio-inspired and nature inspired metaheuristics over classical PID design methods is that these also require the adjustment of parameters prior to the optimization procedure. An advantage of the classical PSO algorithm is fewer adjustable heuristic settings compared with the basic GA. Moreover, PSO is more straightforward to implement than a GA. As the proposed technique aims to be simple to configure, the PSO algorithm is the selected optimization tool used in this study. Moreover, the proposed technique control performance is compared with a more recently introduced metaheuristic: The Grey Wolf Optimization proposed by [18]. There is an increasing pedagogical and research benchmark interest in pocket-sized and portable-based control experiments, as revealed by the following examples in [29; 30; 31; 32; 33; 34]. Microcontrollers enable a wide range of pocket-size laboratories as common tools both for teaching/learning purposes, as well as for control engineering practitioners testing controller designs. The paradigm is shifting from monolithic laboratory experiments that require significant resources to take-home and modular experiments. The Temperature Control Laboratory (TCLab) proposed by [35; 36; 37; 38] is used to teach PID control to undergraduate engineering students [39; 40]. The TCLab is a low-cost Arduino-based lab which is a pocket-sized, plug-and-play kit, meaning that it does not require the user to perform the assembly. This is quite advantageous for computer-based control courses in which the primary goal is to test the identification and control techniques rather than hardware assembly. The TCLab is programmed using different environments, such as: Python, MATLAB(r)SIMULINK(r), and GNU Octave. Moreover, a wide range of open-source supporting materials, including programs, videos, and tutorials are freely available in [24]. The PSO was applied to identify a first order plus time delay model for the TCLab kit in [39; 40].