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Finite-time control theory has been widely used as a mathematical tool to design robust controllers. By manipulating the finite-time convergence proof of this theory, we developed a new control design appropriately tuned for the finite-time control of the chaotic logistics system. In this experimental setup, the logistic equation is programmed into a PIC microcontroller, and a part of the controller was conceived using analog electronics. Because the system to be controlled is in the discrete-time domain, and the finite-time stability proof is stated in the continuous-time representation, our finite-time control approach is a good example for designing control algorithms in both time domain schemes. Hence, our experimental results support our main contribution. Pulse width modulation (PWM) is the format used to translate digital signals into the continuous-time field. Therefore, the main contribution of this article is the theoretical foundations for creating a recent controller that satisfies the convergence criterion in finite time and its construction using an 8-bit microcontroller. All this contributes to the chaotic logistical map. [ABSTRACT FROM AUTHOR]

The ball and beam experimental platform is an unstable nonlinear system widely used as a benchmark control setup for testing different controller approaches, especially for beginners on automatic control to improve their control knowledge skills. In this paper, we innovate it by governing the angular position of the beam with a twin-rotor system. Our experiment consists of a beam that rotates through a pivot, in which two propellers are attached to the ends of this beam. Hence, we have a recent one-degree aerial device, and instead of using a ball, we employ a mass moving on the beam, presenting friction on position to its movements on the beam. Then, the control objective is to regulate the mass position at some predefined zone on the beam, ensuring stability and robustness in front of external perturbations and unmodeled uncertainties. To do so, we define a classical PI controller. To assess closed-loop robustness, a mass was introduced to one propeller to induce perturbation, thereby simulating modeling variations or disturbances. The experimental results prove the goodness of our experimental platform for drone applications. [ABSTRACT FROM AUTHOR]

Image fusion deals with the ability to integrate data from image sensors at different instants when the source information is uncertain. Although there exist many techniques on the subject, in this paper, we develop two originative techniques based on principal component analysis and slicing image transformation to efficiently fuse a small set of noisy images. For instance, in neural data fusion, this approach requires a considerable number of corrupted images to efficiently produce the desired outcome and also requiring a considerable computing time because of the dynamics involved in the fusion data process. In our approaches, the computation time is considerably smaller. This results appealing to increasing feasibility, for instance, in remote sensing or wireless sensor network. Moreover, and according to our numerical experiments, when our methods are compared against the neural data fusion algorithm, they present better performance.

The main objective of this paper is to present a controller design based on the K-means clustering theory. The controller is realized in such way that when the plant output is located outside of the designed clustering set, the controller forces it to be in it. Moreover, and according to our real experiment applied to stabilize an unstable integrator plant, our controller approach design is also robust against un-vanishing perturbations and nonlinearity effects on the overall closed-loop system such as saturation, slew-rate limit, and limit bandwidth frequency operation.