The use of beamforming and power control, combined or separately, has advantages and disadvantages, depending on the application. The combined use of beamforming and power control has been shown to be highly effective in applications involving the suppression of interference signals from different sources. However, it is necessary to identify efficient methodologies for the combined operation of these two techniques. The most appropriate technique may be obtained by means of the implementation of an intelligent agent capable of making the best selection between beamforming and power control. The present paper proposes an algorithm using reinforcement learning (RL) to determine the optimal combination of beamforming and power control in sensor arrays. The RL algorithm used was Q-learning, employing an ε-greedy policy, and training was performed using the offline method. The simulations showed that RL was effective for implementation of a switching policy involving the different techniques, taking advantage of the positive characteristics of each technique in terms of signal reception.
Resumo-Este artigo propõe uma estratégia de controle de velocidade para veículos equipados com motores elétricos de corrente contínua e ímã permanente (PMDC-Permanent Magnet DC). Esta estratégia tem como base um controlador nãolinear auto-regressivo com entradas exógenas (NARX-Nonlinear Autoregressive Exogenous) baseado em Redes Neurais Artificiais (RNA) do Tipo Perceptron de Multiplas Camadas (MLP-Multilayer Perceptron) treinado com algoritmo da retropropagação (BP-Backpropagation). O controlador neural atua de forma direta no PMDC, tendo como entrada o erro entre a velocidade atual e a velocidade de referência do veículo, estados passados da saída e finalmente o ângulo de inclinação longitudinal do veículo. Resultados obtidos, a partir de simulações para várias situações de percurso, são apresentados e mostram a eficiência do controlador proposto.
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