## 5 Conclusions  
In general, EAs are stochastic, population-based, nature-inspired algorithms that have long been considered too time complex for solving dynamic problems. This paper tries to refute this myth by proposing an online adaptive controller based on the (1+1)-DES algorithm. The (1+1)-DES uses single-membered population that reduces time complexity on the one hand, while exploiting an inherent feature of ES, i.e., the self-adaptation, on the other. This feature allows the proposed algorithm to track the continuous changes in the input variable of the controlled system precisely, due to the mutation operator's likely preference for smaller changes over larger ones. The experimental results of the proposed OAC-DES show fairly better properties in comparison with a linear PI-controller for the same highly non-linear plant and sampling time \(T_{sv}\in[1,5]\) ms. The ability to lower transient and disturbance peaks, reduce steady state error in the step responses of the velocity feedback control loop, and to offer online adaptation capability to load torque changes and unexpected torque disturbances, makes the OAC-DES an excellent candidate to control of fast mechatronic devices such as a non-linear, single-degree-of-freedom robotic mechanism. The rapid response time of the proposed OAC-DES in adapting to torque disturbances could also be further increased simply by adding more evaluations in the (1+1)-DES algorithm. The unique advantages of using the (1+1)-DES algorithm in the OAC-DES include:  
* It is simple to perform complete optimization within 10 generations within the sampling time of the feedback-controlled system \(T_{sv}=3\) ms.
* It can avoid being stuck at into local minimum in the way comparable adaptive online control methods based on gradient learning algorithms (e.g., ANN or classical least square methods) often are. A detailed proof of global convergence for (1+1)-ES with constant mutation strength is found in [40]. These results suggest that properly developed and tuned EAs could also be used in online environments, where robustness and rapid response are the most important requirements. From this we conclude that this controller has great potential for future development. This development could take several directions, but in general this type of controller would be most suitable for constantly changeable and unpredictable control plants, as for instance: (1) a direct-drive robotic mechanism with more degrees of freedom could be used, or (2) the same algorithm could be applied in other feedback control systems (e.g., for adaptive cruise control in automotive industry, or control of hydraulic cylinders and motors). Nevertheless, other population-based, nature-inspired algorithms could be incorporated into the OAC-DES and tested on the non-linear robotic mechanism. Also, a theoretic analysis of the OAC-DES rigorous system's stability is an important task left for the future. In our opinion, the proposed approach could be generalized from a single order dynamic system to higher order dynamic systems. However, serious problems with initial introductory learning may be encountered during the start-up, and thus a step-by-step process should be employed. In line with this, step wise learning is proposed here, where controlling the simplest axis would be learned first, followed by the next axis, and so on. For the purposes of higher order dynamic systems, a faster computer configuration would be needed, and the program code of the (1+1)-DES algorithm would need to be time-optimized. Nevertheless, restlessness of OAC-DES should be taken into account and proper remediation should be implemented. Methodology, R.S., D.F. and I.F. ; Software, D.F. ; Validation, J.S., I.F.J., I.F., R.S. and D.F. ; Writing - original draft, R.S., D.F., J.S., I.F.J. and I.F. ; Data curation, I.F.J. This research received no external funding. The authors declare no conflict of interest. [PERSON] acknowledges the financial support from the Slovenian Research Agency (Research Core Funding No. P2-0057).