This work proposes three robust mechanisms based on the MIT rule and the sliding-mode techniques. These robust mechanisms have to tune the gains of an adaptive Proportional-Derivative controller to steer a quadrotor in a predefined trajectory. The adaptive structure is a model reference adaptive control (MRAC). The robust mechanisms proposed to achieve the control objective (trajectory tracking) are MIT rule, MIT rule with sliding mode (MIT-SM), MIT rule with twisting (MIT-Twisting), and MIT rule with high order sliding mode (MIT-HOSM).

In this paper, we present the design and implementation of two nonlinear observers: nonlinear extended state observer and sliding mode observer for estimating the pitch, yaw and roll angles and angular rates of a fixed-wing unmanned aerial vehicles system under a decoupled-reduced model in real flight experiments. A backstepping control law is designed for control in a decentralized way for altitude, yaw and roll of the airplane. This scheme allows us to test experimentally the feasibility of using the online estimated data from the observers in flight control, which is useful for increasing the robustness of the control and the safety of flight. Furthermore, a comparative analysis of the performance of both nonlinear observers is conducted.

Stock portfolio management consists of defining how some investment resources should be allocated to a set of stocks. It is an important component in the functioning of modern societies throughout the world. However, it faces important theoretical and practical challenges. The contribution of this work is two-fold: first, to describe an approach that comprehensively addresses the main activities carried out by practitioners during portfolio management (price forecasting, stock selection and portfolio optimization) and, second, to consider uptrends and downtrends in prices. Both aspects are relevant for practitioners but, to the best of our knowledge, the literature does not have an approach addressing them together. We propose to do it by exploiting various computational intelligence techniques. The assessment of the proposal shows that further improvements to the procedure are obtained when considering downtrends and that the procedure allows obtaining portfolios with better returns than those produced by the considered benchmarks. These results indicate that practitioners should consider the proposed procedure as a complement to their current methodologies in managing stock portfolios.

Stock markets play a crucial role in modern society. Many individuals and organizations try to improve their performance in these markets by exploiting approaches that consider different types of factors. However, although there are great complexities involved, practitioners often use simplistic methodologies that neglect relevant features of the problem. That is, they leave out some activities that are essential to obtain better results in the related decision-making problem. Here, we propose to use a soft computing-based approach to comprehensively address the main activities of stock investments. We present a novel method for modeling expert knowledge through fuzzy logic that allows the investor to discard undesirable stocks (i.e., stocks that are not suitable for investment); thus, reducing computational complexity in the search process and likely improving the performance of the final stock portfolio. Extensive experiments allowed us to conclude that discarding undesirable stocks by exploiting the proposed method produces portfolios that outperform the benchmarks. Therefore, the proposal is a promising alternative to complement current approaches.