This paper shows that the aerodynamic effects can be compensated in a quadrotor system by means of a control allocation approach using neural networks. Thus, the system performance can be improved by replacing the classic allocation matrix, without using the aerodynamic inflow equations directly. The network training is performed offline, which requires low computational power. The target system is a Parrot MAMBO drone whose flight control is composed of PD-PID controllers followed by the proposed neural network control allocation algorithm. Such a quadrotor is particularly susceptible to the aerodynamics effects of interest to this work, because of its small size. We compared the mechanical torques commanded by the flight controller, i.e., the control input, to those actually generated by the actuators and established at the aircraft. It was observed that the proposed neural network was able to closely match them, while the classic allocation matrix could not achieve that. The allocation error was also determined in both cases. Furthermore, the closed-loop performance also improved with the use of the proposed neural network control allocation, as well as the quality of the thrust and torque signals, in which we perceived a much less noisy behavior.

This article proposes a desensitized optimal control (DOC) methodology to enhance the trajectory robustness of electric aircraft. First, a comprehensive multiphysics model of electric aircraft is built based on the first principle. An emphasis is put on the battery pack characteristics led by the cell unevenness. Thereafter, the optimal control technique is applied to study the influence of thermal effects and battery unevenness on flight performance. Furthermore, to alleviate the impact of cell unevenness, the crude control scheme is augmented by DOC. The proposed method generates a more robust reference trajectory for aircraft against cell unevenness. Processor-in-the-loop tests prove that the trajectory is trackable via a tracking controller. Compared to the standard trajectory optimization and nonlinear model predictive control, the proposed method significantly reduces the sensitivity of battery pack temperature regarding cell unevenness with acceptable computational effort.

Dynamic power management policies are essential for battery-powered consumer electronics devices, which are mostly equipped with power consuming network interfaces. Timeout-based power management policies are implemented on most operating systems, but the selection of the policy parameters generally resort to heuristic approaches and there is lack of support for network interfaces dynamic power management. Therefore, this paper introduces an innovative solution for timeout-based power management policies for network interfaces. The presented policy exploits statistical data analysis and the optimization of both power savings and performance. The effectiveness of the introduced policy is demonstrated by experimental results1.

Energy efficiency has become one of the key challenges for a large class of electronic systems. Longer time between battery recharges is highly desirable for battery-powered devices such as mobile phones, digital cameras, Internet tablets, and electronic organizers. Energy efficiency is also important for electronic systems powered from the electric grid since it may reduce power consumption and the cooling requirements. Power savings are possible because electronic systems generally have an idle state, when, for example, the processor can run in a low-power state. Thus, the correct estimation of the workload model plays an essential role in the decision of which and when a power state transition should be performed by the electronic system. This paper introduces a multisize sliding window workload estimation technique for dynamic power management (DPM) in nonstationary environments. This technique reduces both the effects of identification delay and sampling error present in the previous fixed-size sliding window approach. The system is modeled by discrete-time Markov chains and the model offers a rigorous mathematical formulation of the problem and allows one to obtain an excellent trade-off between performance and power consumption.

In this work, we propose an aero-pendulum control system design method. We have proposed the aero-pendulum dynamic model employing Newton`s second law of motion, the dynamic model of the DC motor, and the thrust model. We have estimated the model parameters by designing the experiments, acquiring the data, applying a nonlinear optimization algorithm for the parameter estimation, and performing the model validation. We have designed the aero-pendulum control system by first linearizing the nonlinear aero-pendulum model by small-signal analysis and designing a linear controller. We have verified the designed aero-pendulum control system by both simulations and experiments. We have concluded that the control system of a didactic aero-pendulum can be developed by means our proposed design method.

Opportunistic routing has gained much attention as a new and promising approach to relay packets in wireless mesh networks. The collaboration of intermediate nodes to deliver packets without a predetermined route, using the broadcast nature of wireless channel, increases the transmission reliability and network throughput. Nevertheless, such characteristics make the process of admission control even more challenging than in a traditional unipath routing protocol. In this paper, we propose an admission control mechanism for quality of service (QoS)-enabled opportunistic routing protocols. This mechanism performs admission control and resource reservation in multiple neighbors according to their importance for the relaying process. We also consider the influence of using thresholds for QoS parameters in order to improve the service provided to adaptive applications. Simulation results allow us to reach two conclusions: (i) the proposed mechanism achieves higher success rate than both a unipath-based and a multipath-based admission control mechanisms and (ii) thresholds allow high resource reservation even in network topologies with scarce resources.