Current Issue : July - September Volume : 2017 Issue Number : 3 Articles : 5 Articles
This study proposes a method based on Dempster-Shafer theory (DST) and fuzzy neural network (FNN) to improve the reliability\nof recognizing fatigue driving. This methodmeasures driving states usingmultifeature fusion. First,FNNis introduced to obtain the\nbasic probability assignment (BPA) of each piece of evidence given the lack of a general solution to the definition of BPA function.\nSecond, a modified algorithm that revises conflict evidence is proposed to reduce unreasonable fusion results when unreliable\ninformation exists. Finally, the recognition result is given according to the combination of revised evidence based on Dempster�s\nrule. Experiment results demonstrate that the recognition method proposed in this paper can obtain reasonable results with the\ncombination of information given by multiple features. The proposed method can also effectively and accurately describe driving\nstates....
In this paper, advanced interval type-2 fuzzy sliding mode control (AIT2FSMC) for robot manipulator is proposed. The proposed\nAIT2FSMC is a combination of interval type-2 fuzzy system and sliding mode control. For resembling a feedback linearization\n(FL) control law, interval type-2 fuzzy system is designed. For compensating the approximation error between the FL control law\nand interval type-2 fuzzy system, sliding mode controller is designed, respectively.The tuning algorithms are derived in the sense\nof Lyapunov stability theorem. Two-link rigid robot manipulator with nonlinearity is used to test and the simulation results are\npresented to show the effectiveness of the proposed method that can control unknown system we...
Inconsistencies that are associated with parallel-connected cells used in electric vehicles\ninduce varied states of charge (SOCs) in each cell. Thus, loop current in the battery pack is inevitable,\nand this reduces overall capacity, energy utilization rate, and pack lifetime. However, no method\nis available to address loop current. To reduce loop current and the resulting battery inconsistency,\na parallel-connected cell pack (PCCP) model that considers thermal effects is established, and a novel\nSimscape model that is based on PCCP is successfully constructed. Furthermore, the strategy of\nparallel-connected cell energy management (PCCEM) is proposed to utilize fuzzy logic control (FLC)\nstrategy, which automatically adjusts the number of cells in a circuit in accordance with the load\ndemand, and turns on the first N switches in the corresponding SOC order. The New European\nDriving Cycle (NEDC) driving cycle simulation shows that the PCCEM strategy considerably\nreduces loop current and improves the consistency of battery performance and the utilization rate of\nbattery power....
A learning control strategy is preferred for the control and guidance of a fixed-wing unmanned aerial vehicle to deal with lack\nof modeling and flight uncertainties. For learning the plant model as well as changing working conditions online, a fuzzy neural\nnetwork (FNN) is used in parallel with a conventional P (proportional) controller. Among the learning algorithms in the literature,\na derivative-free one, sliding mode control (SMC) theory-based learning algorithm, is preferred as it has been proved to be\ncomputationally efficient in real-time applications. Its proven robustness and finite time converging nature make the learning\nalgorithm appropriate for controlling an unmanned aerial vehicle as the computational power is always limited in unmanned\naerial vehicles (UAVs). The parameter update rules and stability conditions of the learning are derived, and the proof of the stability\nof the learning algorithm is shown by using a candidate Lyapunov function. Intensive simulations are performed to illustrate the\napplicability of the proposed controller which includes the tracking of a three-dimensional trajectory by the UAV subject to timevarying\nwind conditions. The simulation results show the efficiency of the proposed control algorithm, especially in real-time\ncontrol systems because of its computational efficiency....
A spherical wheel robot or Ballbotââ?¬â?a robot that balances on an actuated spherical ballââ?¬â?is a new and recent type of robot in\nthe popular area of mobile robotics. This paper focuses on the modeling and control of such a robot. We apply the Lagrangian\nmethod to derive the governing dynamic equations of the system.We also describe a novel Fuzzy SlidingMode Controller (FSMC)\nimplemented to control a spherical wheel mobile robot.The nonlinear nature of the equations makes the controller nontrivial.We\ncompare the performance of four different fuzzy controllers: (a) regulation with one signal, (b) regulation and position control with\none signal, (c) regulation and position control with two signals, and (d) FSMC for regulation and position control with two signals.\nThe system is evaluated in a realistic simulation and the robot parameters are chosen based on a LEGO platform, so the designed\ncontrollers have the ability to be implemented on real hardware....
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