## 1 Introduction
Additionally, optimal DC-motor control [17] and quantum computing [18] have been conducted using EAs. More information on EAs for dynamic environments can be found in review papers [19; 20]. Finally, alternative ways of solving NP-hard problems on heterogeneous networks were introduced in [21; 22]. The purpose of our study is to build an online adaptive controller using ESs for a dynamic non-linear control plant. The task of this controller is to minimize the difference between a desired input value and measured output value (also tracking error) obtained as a response by the controlled system. On the other hand, the optimal tracking error value must be obtained as quickly as possible. This means that in practice, such a controller, belonging to a class of fast mechatronic devices, must react within 1-5 ms. When a system is capable of responding within the mentioned interval, this response is treated as the online response. Typically, controls for systems requesting online response have until now been provided using PI-controllers with a proper parameter setting obtained after a lengthy parameter tuning process [23]. These controllers are linear in nature and have limited capabilities in controlling non-linear systems. The proposed adaptive controller using EAs is therefore better suited to this task and represents a novelty in control technique because, as far as we know, EAs have not been used for these purpose before. Although some authors have already tried to build an adaptive controller using an Artificial Neural Network (ANN) [24; 25], the ANN-learning process usually became stuck in local optima, causing the ANN to crash. In our study, we try to overcome both obstacles that occur when solving dynamic problems with EAs by focusing on Evolution Strategies (ES). ES are one of the older members of the EA family suitable for global optimization, and were originally introduced by [PERSON] [26] and [PERSON] [27] at the Technical University of Berlin in 1970. Due to an increase in the reactive behavior of the proposedadaptive controller, the single-membered Dynamic Evolution Strategies (DES) algorithm denoted as (1+1)-DES was chosen, because a single-membered population does not require long-term calculations. On the other hand, the inherited self-adaptation feature allows changes made in the current individual to better track the modifications of input values due to a characteristic of the Gaussian perturbation, where smaller changes are more likely than larger ones. Interestingly, the (1+1)-DES is not executed in a disembodied computer system, but is moved into the hardware, where it acts autonomously. Consequently, the fitness function cannot be calculated from the phenotype directly, but must be evaluated on the basis of its behavior in the environment in which it acts [28]. Actually, the environment is expressed as a difference between the desired (reference) and actual (measured) value obtained from the controlled system. Unfortunately, the measured value is not known at the moment when it is needed for the evaluation. Therefore, this value must be predicted using the so-called surrogate model [29]. In our study, the surrogate model is implemented using Artificial Neural Networks (ANN) capable of modeling highly non-linear systems [30; 31]. As a result, the proposed Online Adaptive Controller based on dynamic ES (OAC-DES) consists of two components: (1+1)-DES for dynamic optimization and ANN for fitness function prediction. The OAC-DES was used in controlling the highly non-linear laboratory setup (i.e., non-linear, single-degree-of-freedom robotic mechanism) in which velocity control was tested. Thus, the behavior of the proposed adaptive controller was compared with the behavior of the traditional PI-controller in a velocity control response test in real-time. The results obtained for the OAC-DES were very promising and showed significant potential for the future.