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Epistemic Skepticism is one of the most controversial issues in epistemology. To block it, some find the failure of closure helpful. Dretske puts forward a recursive analysis of Knowledge. In his view, the analysis makes the closure principle fail which in turn helps to undermine the paradigmatic arguments for epistemic Skepticism. However, some including contextualists, dogmatists, or neo-mooreans, on the other hand, maintain the closure and yet undermine skepticism. In the present paper, I am not going to explore various approaches to preserving closure, criticize Dretske’s analysis as a knowledge nor will I study the setbacks of closure failure. I intend to show that, contrary to the current understanding of Dretske’s analysis, the mentioned analysis does not necessarily lead up to closure failure: There is a specific reading of the recursive base such that not only does it preserve the closure principle but also it offers a way to defeat the classic arguments for Skepticism. To better examine my suggestion compared to Dreske’s and see their exact differences, I will first study Dretske’s analysis of knowledge. Then, I will show that my suggestion respects the closure principle yet undermines skepticism.
 

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The equation of Motion by Gibbs - Appell formulation has been used the least for deriving the dynamic equations of manipulator robots. So, in this paper a new systematic method for deriving the equation of motion of n - rigid robotic manipulators with revolute - prismatic joints (R - P - J) is considered. The equation of motion for this robotic system is obtained based on (G - A) formulation. All the mathematical operations are done by only 3×3 and 3×1 matrices. Also, all dynamic characteristics of a link are expressed in the same link local coordinate system. Based on the developed formulation, an algorithm is proposed that recursively and systematically derives the equation of motion. Finally, a computational simulation for a manipulator with three (R - P - J) is presented to show the ability of this algorithm in deriving and solving high degree of freedom of robotic system.

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The main purpose of this paper is to derive the inverse dynamic equation of motion of n-rigid robotic manipulator that mounted on a mobile platform, systematically. To avoid the Lagrange multipliers associated with the nonholonomic constraints the approach of Gibbs-Appell formulation in recursive form is adopted. For modeling the system completely and precisely the dynamic interactions between the manipulator and the mobile platform as well as both nonholonomic constraints associated with the no-slipping and the no-skidding conditions are also included. In order to reduce the computational complexity, all the mathematical operations are done by only 3×3 and 3×1 matrices. Also, all dynamic characteristics of a link are expressed in the same link local coordinate system. Finally, a computational simulation for a manipulator with five revolute joints that mounted on a mobile platform is presented to show the ability of this algorithm in generating the equation of motion of mobile robotic manipulators with high degree of freedom.

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The main goal of this paper is to present a mathematical model for inverse dynamic equation of elastic robotic manipulator with revolute-prismatic joints. Due to the fact that there is no limitation on the number of mechanical arms, the proposed model must be extracted based on a systematic and automotive algorithm. Also according to the high computational complexity, the equations should be formed by a recursive formulation. Hence, a recursive and systematic methodology for deriving the equation of motion of elastic robotic arm with revolute-prismatic joints is presented. The inverse dynamic equations for this robotic system are obtained based on Gibbs-Applle formulation. All dynamic expressions of a link are expressed in the same link local coordinate system. Finally, in order to show the ability of this formulation in deriving and solving the equation of motion of such systems, a computational simulation for a flexible single robotic arm with revolute-prismatic joint is presented.

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In this paper a method for online identification of satellite moment of inertia tensor parameters based on recursive least squares method, is presented. It is assumed that the satellite actuators are three orthogonal reaction wheels. Dynamic equations of the satellite are extracted in a special manner. The only available sensor is a three axes rate gyro which measures the angular velocity of satellite in the body coordinate system. Due to existence of noise in this sensor, the regressor matrix used in least squares method, changes stochastically. So in this case, the classic least squares method is not useful, and it cannot converge. For solving this problem, a modified least squares method with robust scheme is presented and its stability is proved using Lyapunov stability theory. The presented method can be used online in presence of measurement noise and other sensor imperfections. Simulation results have shown that this method can identify inertia parameters of the satellite with less than 3 percent error comparing to real parameters before and after changes.

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This paper proposes a new hierarchical identification method for fractional-order systems. In this method, a SISO (single input, single output) state space model has been considered in which parameters and also state variables should be estimated.  By using a linear transformation and a shift operator, the system will be transformed into a form appropriate for identification of a fractional-order system. Then, the unknown parameters will be identified through a recursive least squares method and the states will be estimated using a fractional order Kalman filter. This identification method is based on the hierarchical identification principle that reduces the computational burden and is easy to implement on computer. The promising performance of the proposed method is verified using two stable fractional-order systems.    

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Aerospace Launch Vehicles (ALVs), used for launching artificial satellites and space stations to Earth orbits, usually encounter with failure in navigation systems . In these cases, survival of an ALV during accurate payloads injection in orbits is one of the most critical issues for Guidance and Control systems.An important challenge for safety of Aerospace Launch Vehicle (ALV) is their reliability against all types of faults. There is a requirement for on-board fault detection without deteriorating the performance of ALV. In this paper, a new software sensor is proposed for fault detection and compensation based on symmetrical behavior of the yaw and pitch channels of an ALV. For this purpose, using identification techniques on the yaw channel, a new software sensor is developed as an online rigid dynamic predictor for the pitch channel. The proposed software sensor is employed to generate the residual of estimation error as an indicator of predefined faults. The main novelty of this software sensor is online tuning of the virtual sensor against unforeseen variations in the parameters of the vehicle. Robustness of the new control system in the presence of asymmetric behavior is investigated. The efficiency of the proposed fault tolerant method is illustrated through simulations.

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In this paper, a trajectory tracking control strategy for a quadrotor flying robot is developed. At first, dynamic model is obtained by lagrange-euler approach. Then, control structure, consisting of a model-based predictive controller, has been used based on state space error to track transitional movements for reference trajectory and also robust nonlinear H∞ control is applied for stabilizing the rotational movements and reject the external disturbance. In both controllers the integral of the position error is considered, allowing the achievement of a null steady-state error when sustained disturbances are acting on the system. The external disturbances is considered as aerodynamic torques. If uncertainties increase, the designed control system will be unable to track and stabilizing perform properly and completely. So finally, in order to eliminate the effects of parameter uncertainties the recursive least squares is used for estimating mass and moment inertia parameters which are linear and it is applied to the control system. Simulation results show that by using estimation of system parameters, the proposed control system has a promising performance in terms of stabilization and position tracking even in the presence of external disturbance and parametric uncertainties.

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In this paper, designing optimal linear controller for non-holonomic wheeled mobile robots based on Linear Quadratic Gaussian (LQG) controller is considered. Parameters of the governing kinematics equation of motion are derived based on system identification techniques by using real experimental data. The autoregressive moving average-exogenous input (ARMAX) models are taken into account. The least square (LS) algorithm is utilized to estimate the parameters of the model. Thereafter, optimal model order and the performance of the model are determined using several statistical analyses. Also, the recursive LS (RLS) with forgetting factor is employed to demonstrate the convergence of the model parameters. Verification of discrete linear model implies the possibility of using the linear controllers. Therefore, the optimal LQG controller for wheeled mobile robots is designed to track the reference trajectory. The Kalman observer is used to estimate un-measurable states of the robot. Furthermore, the optimal linear control together with system identification techniques yields simpler controller than nonlinear controllers. Designed controller and verified model are simulated using the MATLAB-Simulink software. Results show the effectiveness of the controller in tracking the desired reference trajectory.