New Meta-heuristic-Based Approach for Identification and Control of Stable and Unstable Systems

Nowadays, the use of meta-heuristic algorithms (MAs) for tackling complicated engineering is-sues has shown significant promise, therefore applying MAs to optimum model parameters and PID parameters can be quite beneficial. As a result, this paper looks at the capabilities of four recently released resilient MAs in optimizing model parameters and PID parameters for various system behaviors. Hence, these four meta-heuristic algorithms are used such as Ant Colony Optimization (ACO), Cultural Algorithm (CA), Invasive Weed Optimization (IWO), and Black Hole Algorithm (BHA). The key contribution of this study is the employment of many meta-heuristics at the same time with the same objective function while taking into consideration each algorithm parameters for identification and control, then compared to traditional techniques such as Least square (LS) and Reference Model (RM). Thus, the most efficient algorithm is the one that yields the lowest cost function, has the lowest standard deviation (SD), and uses the least amount of CPU time. Regarding identification, simulation findings showed that CA algorithm has the best cost, lowest standard deviation (SD) and fewest CPU time 2.7838e-13, 7.1108e-13 and 3.1395(s), respectively. As for control system, it is shown that created intelligent-based controllers are more dependable than reference model controllers in stabilizing the behaviors of the various examined processes, with the IWO algorithm finds the best gains of PID and converges the fastest with best cost 3.2905e-10 and CPU time 48.8732(s). Moreover, ACO and BHA both failed to achieve satisfactory results in terms of accuracy and CPU time compared to others algorithms. Additionally, studies also showed that optimization methods has good performance, resilient and effective.