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This article presents an analysis on the field weakening capability of a flux intensifying synchronous motor (FI-SM) with a small quantity of inset permanent magnets (PMs) on the rotor surface. Differing from other FI-SMs whose rotors are constructed by interweaving PMs and flux barriers (FBs), for the presented FI-SM, the PM dimensions can be easily adjusted without significantly altering the rotor configuration, thus offering more flexibility in rotor design and making it more feasible to analyze the influence of the added PM on flux density distribution, inductances, PM flux linkage, and field weakening capacities in conjunction with armature current. This article, through investigating this design potential, aims to provide an approach for design and analysis of this type of motor to enhance the performance and improve the field weakening capability. Experiments are conducted to validate the simulation.

FinSBD-3 is a shared task organized in the context of the 1st workshop on Financial Technology on the Web. The task focuses on extracting the entire structure of noisy PDF financial documents that include 1) sentences, lists, items, and organization of lists and items; 2) figures and tables; 3) headers and footers. This paper describes the approach that allows us to extract the figures and tables using their visual cues. We applied the object segmentation techniques in image processing to detect the location of figures and tables in the PDF files. A post-processing method is then executed in order to find exact content. The result shows the potential of this approach.

This article proposes a new fixed-time non-singular terminal sliding mode (FxNTSM) surface for synthesizing the tracking control algorithm of robotic manipulators. The new sliding mode surface has no singularity, improved convergence speed. It contains dynamic coefficients that can be adapted to control errors that drive the system states quickly converge to the equilibrium point within bounded convergence time regardless of initial system states. The settling time of the sliding motion phase is fully calculated in both cases, including the initial state of the system is far/near the equilibrium stability point. A novel finite-time NTSMC (FnNTSMC) is formed from a combination of the proposed sliding surface and auxiliary reaching control law (ARCL). Due to the advantages of the proposed sliding surface and mentioned reaching control law, therefore, the novel finite-time control system provides superior characteristics such as high tracking accuracy, improved fast convergence, and robustness to cope with the lumped uncertainties. The fixed time/finite-time stability and convergence of the control method are validated by using the Lyapunov criteria. Computer simulations applied to a 3-DOF robotic manipulator are presented to verify the effectiveness and outstanding properties of the proposed control method.

This article investigates a control algorithm for trajectory tracking control of robot manipulators in uncertain dynamical environments. To deal with chattering behavior that always still exists in the conventional sliding mode control, to remove the requirement about a dynamic model of the uncertain robot system, and prior knowledge of the unknown function such as its upper boundary, the proposed solution is to develop a control method that combines the advantages of both terminal sliding mode control and radial basis function neural network. Furthermore, the proposed controller’s fast stabilization and convergence are also significantly improved by using a novel adaptive fast reaching control law. Hence, the proposed controller’s performance expectations are always guaranteed such as high tracking accuracy, fast stabilization, chattering reduction, fast convergence, and robustness to uncertain dynamical environments. Especially, the proposed controller can operate without the robot’s dynamic model. Both theoretical investigations based on Lyapunov stability theory and computer simulation using MATLAB/Simulink are presented to confirm the effectiveness of the proposed control solution.

This paper comes up with a novel Fast Terminal Sliding Mode Control (FTSMC) for robot manipulators. First, to enhance the response, fast convergence time, against uncertainties, and accuracy of the tracking position, the novel Fast Terminal Sliding Mode Manifold (FTSMM) is developed. Then, a Supper-Twisting Control Law (STCL) is applied to combat the unknown nonlinear functions in the control system. By using this technique, the exterior disturbances and uncertain dynamics are compensated more rapidly and more correctly with the smooth control torque. Finally, the proposed controller is launched from the proposed sliding mode manifold and the STCL to provide the desired performance. Consequently, the stabilization and robustness criteria are guaranteed in the designed system with high-performance and limited chattering. The proposed controller runs without a precise dynamic model, even in the presence of uncertain components. The numerical examples are simulated to evaluate the effectiveness of the proposed control method for trajectory tracking control of a 3-Degrees of Freedom (DOF) robotic manipulator.

This paper proposes a combination between a neural network and an adaptive sliding mode control for trajectory tracking control of a 3-DOF planar parallel manipulator. It has a complicated dynamic model, including modelling uncertainties, frictional uncertainties and external disturbances. The proposed control algorithm is to use a PID sliding mode surface, an adaptive sliding mode controller with a neural network to overcome the drawback of the traditional sliding mode controllers, such as slow response rate with variation of uncertainties and external disturbances, chattering, and upper bound values of undefined dynamics which affects system performance, high wear of moving mechanical parts and high heat losses in power circuits. The radial basis function neural network is designed to compensate for uncertainties and external disturbances, which allows small switching gain. Hence, the chattering can be significantly reduced. In addition, an adaptive control law is used to adaptively converge small switching gains of the sliding mode controller as the neural network reduces model uncertainties. The effectiveness of the proposed control strategy is demonstrated by simulations which are conducted by using the combination of Sim-Mechanics and SolidWorks.

This paper proposes an adaptive integral sliding mode tracking control for robotic manipulators. Our proposed control method is developed based on the benefits of both integral sliding mode control and adaptive control, such as high robustness, high accuracy, and estimation ability. In this paper, an integral sliding mode controller is designed with the elimination of the reaching stage to provide better trajectory tracking accuracy and to stabilize the closed-loop system. To reduce the computation complexity, an adaptive controller with only one simple adaptive law is used to estimate the upper-bound values of the lumped model uncertainties. As a result, the requirement of their prior knowledge is eliminated and then decrease the computation cost. Consequently, this controller provides better tracking accuracy and handles the dynamic uncertainties and external disturbances more strongly. The system global stability of the controller is guaranteed by using Lyapunov criteria. Finally, the effectiveness of the proposed control method is tested by computer simulation for a PUMA560 robotic manipulator.

In this paper, a full-order sliding mode tracking control system is developed for industrial robots. First, to dismiss the effects of perturbations and uncertainties, while to improve faster response time and to eliminate the singularity, a full-order sliding function is selected. Next, to reach the prescribed tracking path and to remove the chattering, a control method is designed for robot manipulators by using a combination of full-order sliding function and a continuous adaptive control term. Additionally, the unknown dynamic model of the robot is estimated by adopting a radial basis function neural network. Due to the combination of these methodologies, the proposed controller can run free of exact robot dynamics. The suggested controller provides strong properties of high tracking accuracy and quick response with minimum tracking errors. In simulation analysis, the simulated performances verify high effectiveness of the proposed controller in trajectory tracking control of a 3-DOF robot manipulator.

This study presents a new adaptive synchronized computed torque control algorithm based on neural networks for three degree-of-freedom planar parallel manipulators. The basic idea of the proposed control algorithm is to use the incorporation of cross-coupling errors of active joints with the adaptive computed torque control algorithm, online self-tuned neural networks, and error compensators. The key to the success of the proposed approach is to improve the trajectory tracking accuracy of the parallel manipulator’s end-effector while driving the synchronization errors among active joints to zero. The uncertainties of the control system such as modeling errors, frictional terms, and external disturbances are adaptively compensated online during the trajectory tracking of the parallel manipulator. Using the Lyapunov theory, it is proved that the tracking errors and error rates of the overall system asymptotically converge to zero. To demonstrate the effectiveness of the proposed control algorithm, compared simulations are conducted using MATLAB/Simulink [version 2013a] combined with Solidworks 2014.

Control of parallel manipulators is a challenging and difficult task. In this paper, we propose a new adaptive synchronized sliding mode controller for planar parallel manipulators by using fuzzy logic system. The proposed controller is based on the combination of the definition of synchronized control, sliding mode control and online self-tuning fuzzy logic system. Firstly, the dynamic model of parallel manipulators is presented in active joint space. Based on this dynamic model, a synchronized sliding mode controller is developed. And then, in order to compensate the uncertainties of the control system, an online self-tuned fuzzy logic system is adopted. The self-tuning law is proposed by using the cross-coupling error of the parallel manipulator. The stability of the closed loop system is guaranteed by using Lyapunov theory. The simulations were conducted on Matlab/Simulink to verify the effectively of the proposed control algorithm.

This paper introduces a novel control strategy for uncertain nonlinear second-order systems. Our strategy proposes a novel adaptive fuzzy sliding mode controller, which based on a combination of a new non-singular fast terminal sliding variable and a continuous control algorithm. In this paper, our main contribution is to contain benefits of non-singular fast terminal sliding variables such as fast convergence and no singularity drawback along with strong robustness in the frame of perturbation and uncertainty. In the suggested controller, a continuous control law is added to refuse the drawbacks of sliding mode control about consideration on upper bound value of perturbations and uncertainties. Unfortunately, it is remarkable difficult for a real system to identify those upper bound limits in advance. It is reminded that to deal with above concern, a fuzzy logic control algorithm with adaptive updating law can be used to approximate switching control law. Thanks to this technique, the perturbations and uncertainties can be eliminated without chattering behavior in control input. Accordingly, the strong robustness and the stability of the suggested system is then secured with high accuracy performance. The robustness topic of the suggested system is also completely proven by Lyapunov approach. In our numerical simulation, performances comparison among the suggested control strategy, a sliding mode controller, and a non-singular terminal sliding mode controller are specifically performed. Our simulation result demonstrates the effectiveness, practicality of suggested control strategy for the joint position tracking control of a 3-DOF PUMA560 robot.

This paper proposes an adaptive position synchronization controller using orthogonal neural network for 3-DOF planar parallel manipulators. The controller is designed based on the combination of computed torque method with position synchronization technique and orthogonal neural network. By using the orthogonal neural network with online turning gains can overcome the drawbacks of the traditional feedforward neural network such as initial values of weights, number of processing elements, slow convergence speed and the difficulty of choosing learning rate. To evaluate the effectiveness of the proposed control strategy, simulations were conducted by using the combination of SimMechanics and Solidworks. The tracking control results of the parallel manipulators were significantly improved in comparison with the performance when applying non-synchronization controllers.

Parallel robotic manipulators have a complicated dynamic model due to the presence of multi-closed-loop chains and singularities. Therefore, the control of them is a challenging and difficult task. In this paper, a novel adaptive tracking controller is proposed for parallel robotic manipulators based on fully tuned radial basis function networks (RBFNs). For developing the controller, a dynamic model of a general parallel manipulator is developed based on D׳Alembert principle and principle of virtual work. RBFNs are utilized to adaptively compensate for the modeling uncertainties, frictional terms and external disturbances of the control system. The adaptation laws for the RBFNs are derived to adjust on-line the output weights and both the centers and variances of Gaussian functions. The stability of the closed-loop system is ensured by using the Lyapunov method. Finally, a simulation example is conducted for a 2 degree of freedom (DOF) parallel manipulator to illustrate the effectiveness of the proposed controller.

The control of a parallel robotic manipulator with uncertain dynamics is a noteworthy challenge due to the complicated dynamic model, multi-closed-loop chains, and singularities. This study develops a Synchronization Full-Order Terminal Sliding Mode Control (S-FOTSMC) for a 3-DOF planar parallel robotic manipulator with uncertain dynamics. First, to achieve faster convergence of position error and synchronization error variables with minimum values at the same time, a Synchronization Full-Order Terminal Sliding Mode Surface (S-FOTSMS) is constructed in the cross-coupling error&rsquo, s state space. Next, an integral of the switching control term is applied, that means, a continuous control term is extended for rejecting the effects of chattering. Finally, an SFOTSMC is designed to guarantee that sliding mode motion will occur. Consequently, the stability and the robustness of the proposed method are secured with high-performance irrespective of the influences of uncertain terms in the robot system. The simulation performances show the effectiveness of our proposed system for position tracking control of a 3-DOF planar parallel robotic manipulator.

Parallel manipulators have advantages like high accuracy, high stiffness, high payload capability, low moving inertia, and so on. In this paper, a detailed study to apply an online self gain tuning method using neural networks for nonlinear PD computed torque controller to a 2-dof parallel manipulator is presented. A novel nonlinear PD computed torque controller is achieved by combining conventional computed torque controller and auto tuning method using neural networks which has advantages such as flexibility, adaptation and learning ability. The proposed controller has a simple structure and little computation time while securing good performance in tracking trajectories of parallel manipulators. To verify the control performance, various simulations of a 2-dof parallel manipulator are conducted. Simulation results show the effectiveness of the proposed method in comparison with the conventional computed torque controller.

In this paper, an adaptive extended computed torque controller is proposed for trajectory tracking of 3 degree-of-freedom planar parallel manipulators. The dynamic model, including the modeling errors and uncertainties, is established in the joint space of 3 degree-of-freedom planar parallel manipulators. Based on the dynamic model, an adaptive extended computed torque control scheme is proposed in which a feed-forward neural network is combined with error compensators for adaptive compensating the unknown modeling errors and uncertainties of the parallel manipulators. The weights of the neural network are based on sliding functions and self-tuned online during the tracking control of system without any offline training phase. Using the combination of Sim Mechanics and Solid works, the comparative simulations are conducted for verifying the efficiency of the proposed control scheme.

Improving the position accuracy of the closed chain robots needs a global robot kinematic model. Their closed loop constraints should be included in the global model via the specific passive joints of the robot. This paper proposes a simple but effective method to derive a mathematical formula for identification of all parameters of the robots having multiple closed chain mechanisms. An experimental calibration is carried out on a Hyundai HP160 robot to validate the correctness and effectiveness of the proposed method.

In this paper, an adaptive model-based control scheme is proposed for tracking control of five-bar manipulators with deadzone inputs. The proposed controller is based on the combination of nominal dynamic model of the five-bar manipulator, a wavelet network and a deadzone precompensator. The wavelet network and the precompensator are used for compensating the unknown deadzone inputs, modeling errors and uncertainties of the five-bar manipulator. The adaptation laws are derived for tuning parameters of the precompensator and wavelet network. The efficiency of the proposed control scheme is verified by comparative simulations.

In this paper, an investigation on the kinematics and the singularities of symmetrical 2 degrees of freedom (DOF) planar parallel manipulators is presented. The parallel manipulators considered are composed of five links, five revolute joints and two motors giving input rotation to the system. The forward kinematic and the inverse kinematic models of the parallel manipulators are performed. Jacobian matrices of the parallel manipulators are derived and three types of singularities are identified for them. The results of this paper are very useful for trajectory planning, analysis and control of 2 DOF parallel manipulators.

Tracking control of closed-chain robotic manipulators has posed a challenging and difficult task due to the complicated dynamic model, the presence of multi closed-loop chains and singularities. This paper presents a novel tracking controller using radial basic function networks (RBFNs) for closed-chain robotic manipulators. The dynamic model of a general closed-chain robotic manipulator is presented in the presence of structured and unstructured uncertainties. In order to compensate the uncertainties, the RBFNs are used. An adaptation law is proposed to adjust on-line the output weights of the RBFNs. The validity of the proposed controller is shown by computer simulations of a five-bar planar parallel manipulator.

Parallel manipulators have advantages like high accuracy, high stiffness, high payload capability, low moving inertia, and so on. This paper presents the problems of control the five-bar manipulators using computed torque control method. In order to improve the control performance, an online self gain tuning method using neural networks is proposed for gain tuning of computed torque controller. Simulation results show the effectiveness of the proposed method in comparison with the traditional computed torque control method.

This paper presents a learning object broker that aims to mediate and control accesses to learning objects offered by the "Learning Resource Exchange". This broker provides a unified view on access authorization to requestors and providers of learning objects.

In a large federation of commercial learning object repositories, customers might be confronted to different access control procedures put in place by different content providers to enforce their respective distribution models. This paper proposes a federated authorization service that provides a uniform access to learning objects protected by different authorization protocols. This service is based on a learning object broker that mediates different distribution models.

In a large federation of learning resource repositories metadata can become out of date, especially when resources are moved or deleted, frustrating users and making it critical to assure the availability of resources in any learning environment. Regularly checking all of URLs in a federation with thousands of resources negatively impacts content providers causing poor system performance, heavy network traffic and impoliteness. This paper describes a solution that ensures a balance between the number of resources and the number of checking requests performed. It uses a heuristic algorithm to select a group of resources to verify. Depending on the results, the system either stops checking or continues with a new selection of links constructed from URLs that are similar to the broken links in the previous round. To reach the global optimisation, the selection process also randomly picks other resources with a small probability of error.

This paper proposes a novel chattering free neuro-sliding mode controller for the trajectory tracking control of two degrees of freedom (DOF) parallel manipulators which have a complicated dynamic model, including modelling uncertainties, frictional uncertainties and external disturbances. A feedforward neural network (NN) is combined with an error estimator to completely compensate the large nonlinear uncertainties and external disturbances of the parallel manipulators. The online weight tuning algorithms of the NN and the structure of the error estimator are derived with the strict theoretical stability proof of the Lyapunov theorem. The upper bound of uncertainties and the upper bound of the approximation errors are not required to be known in advance in order to guarantee the stability of the closed-loop system. The example simulation results show the effectiveness of the proposed control strategy for the tracking control of a 2-DOF parallel manipulator. It results in its being chattering-free, very small tracking errors and its robustness against uncertainties and external disturbances.