Abstract
Nature inspired metaheuristics algorithms have been the target of several studies in the most varied scientific areas due to their high efficiency in solving real world problems. This is also the case of agriculture. Among the most well-established nature inspired metaheuristics the ones selected to be addressed in this work are the following: genetic algorithms, differential evolution, simulated annealing, harmony search, particle swarm optimization, ant colony optimization, firefly algorithm and bat algorithm. For each of them, the mechanism that inspired it and a brief description of its operation is presented, followed by a review of their most relevant agricultural applications. © Springer Nature Switzerland AG 2019.

Abstract
The paper deals with a robust constrained control based on extended state observer (ESO) and state controller design using integral plant models of step-wise increasing complexity. The presented approach is used to enforce and consolidate the student's skills by repeating the state observer and state controller design for several modifications of the plant model. Importance of the control design evaluation by appropriate performance measures is also stressed. Thereby, this approach may help in discovering new interesting properties of this type of control.

Abstract
Currently smartphones are used extensively by students. Thus, their proper use within teaching/learning activities is a reality which needs to be addressed. How teachers and students without advanced programming skills can develop their own mobile devices applications? An attempt to answer this question is presented in control engineering teaching and learning activities. It will be shown that the MIT App Inventor 2 provides an excellent tool to develop simple applications for Android based mobile devices. Examples of applications designed both by lecturers and students using the MIT App Inventor 2 are described, outlining different uses and features. A case study of using applications to support teaching/learning of PLC programming is presented.

Abstract
Particle swarm optimization (PSO) is a search algorithm inspired by the collective behavior of flocking birds and fishes. This algorithm is widely adopted for solving optimization problems involving one objective. The evaluation of the PSO progress is usually measured by the fitness of the best particle and the average fitness of the particles. When several objectives are considered, the PSO may incorporate distinct strategies to preserve nondominated solutions along the iterations. The performance of the multiobjective PSO (MOPSO) is usually evaluated by considering the resulting swarm at the end of the algorithm. In this paper, two indices based on the Shannon entropy are presented, to study the swarm dynamic evolution during the MOPSO execution. The results show that both indices are useful for analyzing the diversity and convergence of multiobjective algorithms.

Abstract
Students born in a digital era require adjusted teaching and learning methodologies incorporating new technologies. A common difficulty found by students is how to test their controller designs in a real system. Thus, the development of affordable, portable and easy to use feedback control kits is highly desirable. The idea is that both lecturers and students can perform simple practical experiments anytime and anywhere. The APMonitor temperature control lab is an Arduino based control kit which fulfils these requirements. Proportional, integrative and derivative control is in operation in the vast majority of industrial process control loops. Thus, it is a mandatory topic in most undergraduate introductory feedback control courses. A teaching/learning PID control experiment for undergraduate Biomedical Engineering student's based on the temperature control lab is reported here. Results received from students are presented.

Abstract
The paper presents extension of model-based disturbance rejection method based on Magnitude optimum method. The original method requires the pre-chosen values of controller and compensator filter values, which might result in a relatively noisy process input signals that are not tolerated in practice. In order to keep the process input noise under control, the proposed tuning method requires user-defined controller and compensator noise gain for high frequencies, instead. The experiments on process models show that the proposed approach is successful even though the actual and the identified process models do not match.