Online Deep Fuzzy Learning for Control of Nonlinear Systems Using Expert Knowledge

This article presents an online learning method for improved control of nonlinear systems by combining deep learning and fuzzy logic. Given the ability of deep learning to generalize knowledge from training samples, the proposed method requires minimum amount of information about the system to be controlled. However, in robotics, particularly in aerial robotics where the operating conditions may vary, online learning is required. In this article, fuzzy logic is preferred to provide supervising feedback to the deep model for adapting to variations in the system dynamics as well as new operational conditions. The learning method is divided into two phases: offline pretraining and online posttraining. In the former, the system is controlled by a conventional controller and a deep fuzzy neural network (DFNN) is pretrained based on the recorded input-output dataset, in order to approximate the inverse dynamical model of the system. In the latter, only the pretrained DFNN is used to control the system. In this phase, the fuzzy logic, which encodes the expert knowledge, is utilized to observe the behavior of the system and to correct the action of DFNN instantaneously. The experimental results show that the proposed online learning-based approach improves the trajectory tracking performance of the unmanned aerial vehicle.     «

This article presents an online learning method for improved control of nonlinear systems by combining deep learning and fuzzy logic. Given the ability of deep learning to generalize knowledge from training samples, the proposed method requires minimum amount of information about the system to be controlled. However, in robotics, particularly in aerial robotics where the operating conditions may vary, online learning is required. In this article, fuzzy logic is preferred to provide supervising f...     »

autonomous aerial vehicles; control engineering computing; expert systems; feedback; fuzzy logic; fuzzy neural nets; learning (artificial intelligence); mobile robots; nonlinear control systems; online deep fuzzy learning; nonlinear systems; expert knowledge; deep learning; fuzzy logic; system dynamics; online posttraining; deep fuzzy neural network; offline pretraining; inverse dynamical model approximation; unmanned aerial vehicle; Control systems; Training; Fuzzy logic; Neurons; Nonlinear systems; Fuzzy neural networks; Noise measurement; Adaptive process control; aerial robotics; deep learning; fuzzy logic; nonlinear systems     «

autonomous aerial vehicles; control engineering computing; expert systems; feedback; fuzzy logic; fuzzy neural nets; learning (artificial intelligence); mobile robots; nonlinear control systems; online deep fuzzy learning; nonlinear systems; expert knowledge; deep learning; fuzzy logic; system dynamics; online posttraining; deep fuzzy neural network; offline pretraining; inverse dynamical model approximation; unmanned aerial vehicle; Control systems; Training; Fuzzy logic; Neurons; Nonlinear sys...     »