To improve the performance of a thermal plant based on Peltier cell actuators, an online parametric estimation via artificial neural networks and adaptive controller is presented. The control actions  are based on adaptive digital controller and an adaptive quadratic linear regulator approaches. The Artificial neural networks topology is based on ARX and NARX models, and its training algorithms, such as accelerated backpropagation and recursive least square. The Control strategies are design-oriented to adaptive digital PID controller and quadratic linear regulator framework. The proposal is evaluated on  temperature control of an object that is inside of a chamber.

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Physical simulation is a useful tool for examining the events that occur during the multiple stages of thermomechanical processing, since it requires no industrial equipment. Instead, it involves hot deformation testing in the laboratory, similar to industrial-scale processes, such as controlled hot rolling and forging, but under different conditions of friction and heat transfer. Our purpose in this work was to develop an artificial neural network (ANN) to optimize the thermomechanical behavior of stainless-steel biomaterial in a double-pass hot compression test, adapted to the Arrhenius–Avrami constitutive model. The method consists of calculating the static softening fraction (Xs) and mean recrystallized grain size (ds), implementing an ANN based on data obtained from hot compression tests, using a vacuum chamber in a DIL 805A/D quenching dilatometer at temperatures of 1000, 1050, 1100 and 1200 °C, in passes (ε1 = ε2) of 0.15 and 0.30, a strain rate of 1.0 s−1 and time between passes (tp) of 1, 10, 100, 400, 800 and 1000 s. The constitutive analysis and the experimental and ANN-simulated results were in good agreement, indicating that ASTM F-1586 austenitic stainless steel used as a biomaterial undergoes up to Xs = 40% of softening due solely to static recovery (SRV) in less than 1.0 s interval between passes (tp), followed by metadynamic recrystallization (MDRX) at strains greater than 0.30. At T > 1050 °C, the behavior of the softening curves Xs vs. tp showed the formation of plateaus for long times between passes (tp), delaying the softening kinetics and modifying the profile of the curves produced by the moderate stacking fault energy, γsfe = 69 mJ/m2 and the strain-induced interaction between recrystallization and precipitation (Z-phase). Thus, the use of this ANN allows one to optimize the ideal thermomechanical parameters for distribution and refinement of grains with better mechanical properties.

An anti-collision method for multi-agent systems based on the centralization and decentralization effects in a control protocol is presented in this article. In this context, a matrix called Anti-Laplacian is defined, it acts as a decentralizing agents when the UAVs are on a conflicting route. The matrix adjustment occurs through proportionality relations with the Laplacian Matrix from an interaction graph between the agents. The adjustment method aims a balance between centering and decentering to avoid collisions. Controlled quadcopters follow the trajectory indicated by virtual agents that act as guides for the real ones. The tests are performed via simulation for the most critical cases, with protocols involving flock centralization. As a virtual agent, a first-order model is used in the simulation, the method efficiency is observed by varying the number of agents involved.