Catch me and soundcloud.com/mutrix on tour this spring!Mar 14Fox TheatreBoulder, COMar 15RawkusColorado Springs, COMar 20Mesa Theater and ClubGrand Junction, COMar 21Vain Night Club Orlando, FLMar 27DemoSt Louis, MOMar 29Bigs BarSioux Falls, SDApr 01The Lit LoungeFayetteville, ARApr 02Elektricity NightclubPontiac, MIApr 03The LoftMinneapolis, MNApr 10Tantrum NightclubBangor, MEApr 13U Street Music HallWashington, DCApr 16Middle EastCambridge, MAApr 18Nothin Fancy Music HallVernon, NYApr 26Webster Hall - The BassmentNew York, NYMay 01NEW EARTH MUSIC HALLAthens, GAMay 02TEMPTMurfreesboro, TN
EVE Online is a free MMORPG sci-fi strategy game where you can embark on your own unique space adventure. EVE's open world MMORPG sandbox, renowned among online space games, lets you choose your own path and engage in combat, exploration, industry and much more. Play the world's #1 space MMO today!
Reverb.com is offering a limited-time deal on the Alternator synthesizer by Sinevibes (offered at 37% off its regular price), bundled with a free copy of the excellent Singularity delay by the same developer.
In this article, a singularity-free terminal sliding mode (SFTSM) control scheme based on the radial basis function neural network (RBFNN) is proposed for the quadrotor unmanned aerial vehicles (QUAVs) under the presence of inertia uncertainties and external disturbances. Firstly, a singularity-free terminal sliding mode surface (SFTSMS) is constructed to achieve the finite-time convergence without any piecewise continuous function. Then, the adaptive finite-time control is designed with an auxiliary function to avoid the singularity in the error-related inverse matrix. Moreover, the RBFNN and extended state observer (ESO) are introduced to estimate the unknown disturbances, respectively, such that prior knowledge on system model uncertainties is not required for designing attitude controllers. Finally, the attitude and angular velocity errors are finite-time uniformly ultimately bounded (FTUUB), and numerical simulations illustrated the satisfactory performance of the designed control scheme.
To solve the dependence of the FTC on a nonlinear system model, multiple control methods have the capability to approximate nonlinear functions, which is used to implement the estimation task of the nonlinear system, i.e., adaptive control  and optimal control . In the past years, a method called RBFNN has been widely introduced to approximate the dynamic parameters of nonlinear systems, such that no prior knowledge of model information is required in [7, 8]. In , a RBFNN method was devolved to estimate the unknown model uncertainties of robot manipulators. In , a RBFNN-based SMC scheme was presented to guarantee the asymptotic convergence of the system states under the model uncertainties. Compared with other estimation methods [5, 6], RBFNN has faster convergence speed and local approximation capability to avoid local minima problems. Thus, RBFNN is more suitable for real-time control, such as QUAV attitude control.
Inspired by the above discussions, an RBFNN-based finite-time adaptive attitude tracking controller is designed for the attitude tracking problem of QUAVs with inertial uncertainty and unknown external disturbances, and the main contributions are summarized in the following:(i)Instead of employing any piecewise continuous functions, a SFTSMS is proposed to avoid the singularity directly in the differential of the sliding variable(ii)An auxiliary function is designed to handle a potential singularity resulted from the use of the error-related inverse matrix in the attitude controller design(iii)By employing RBFNN and ESO to estimate the unknown dynamics, prior knowledge on system model uncertainties is not required in the controller design, and the tracking errors are FTUUB by the proposed control law
In practical systems, neural networks (NNs) are online estimation techniques for unknown nonlinear uncertainties. Due to the approximation characteristics and faster learning convergence, RBFNN is widely used in the estimation of nonlinear functions in the field of control. This section will introduce the structure of RBFNN.
RBFNN consists of three parts: input layer, output layer, and hidden layer. As shown in Figure 2, is the neural network input vector, is the weight of the th network node, represents the output vector, and is the basis function, which can approximate nonlinear uncertainties with high precision through the linear combination of Gaussian functions, which is given by the following :where is the center of the RBF and means the scaling parameter of the network node .
Due to the existence of the term of in the expression of , it may cause the potential singularity issue when . Consequently, an auxiliary function is constructed to solve the singularity caused by in the controller design.
Use (11) to approximate the nonlinear uncertainties (29).where is the NN input vector, represents the ideal weight vector, is the approximation error satisfying , , and denotes the Gaussian function (9).
In this study, a finite-time convergent RBFNN-based adaptive controller has been constructed to resolve a tracking problem of quadrotor UAVs. Firstly, a SFTSMS is proposed to realize the finite-time convergence of the tracking errors, which can directly avoid the potential singularity problem without requiring any piecewise continuous functions. Besides, an auxiliary function is proposed to purposely prevent the hidden singularity issue caused by the error-related matrix in the controller design. Then, a finite-time attitude controller is designed to guarantee that the system states were FTUUB. With the presented control scheme by RBFNN and ESO, prior knowledge about the unknown nonlinear uncertainties and external disturbances is not required. Finally, comparative simulations have shown the effectiveness of the designed control scheme.
The third edition of Singularity Tech Day will take place on November 30 and will be 100% online so that you can enjoy it from anywhere in the world. ¡In addition, it will be totally free!
The main test server for EVE Online is called Singularity, or Sisi for short. On here you are free to test upcoming features and changes, as well as participating in mass tests organized by CCP. Sisi is used by players like yourself, CCP developers and ISD Bughunting volunteers to test.
If you witness a player breaking the rules on the Singularity Test Server, please report them by emailing the Customer Support department at firstname.lastname@example.org with a detailed description of the incident and any supporting evidence that can help with the investigation.
Koch: Well, Francis was right in that the standard conception of free will, that has the soul hovering above the brain and making it "freely" decide this way or that, is an illusion. It simply does not work at the conceptual or empirical level However, more subtle readings of free will remain, as I discuss in my book. Yet we are all less free than we like to believe. What remains, though, is that I am the principal actor in my life, so I better take responsibility for my actions.
Koch: I have stopped eating the flesh of mammals and birds, as they too share the wonders of experience with us. We are all nature's children. We all experience the pains and pleasures of life. Furthermore, the commodious literature on voluntary actions makes it quite clear that we are less free than we think we are, that our prior actions, beliefs and habits shape us in untold ways. This has made me more humble.
You're looking at OpenBook, NAP.edu's online reading room since 1999. Based on feedback from you, our users, we've made some improvements that make it easier than ever to read thousands of publications on our website. 2b1af7f3a8