In this paper, the employment of neural networks with sliding mode control in the control of a linear drive with flexible transmission element is described. Linear drives with flexible transmission elements are cheape...
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In this paper, the employment of neural networks with sliding mode control in the control of a linear drive with flexible transmission element is described. Linear drives with flexible transmission elements are cheaper and also more efficient than the ones with rigid transmissions like power screw systems. Hence, these devices play an important role in the industry. A neuro-sliding mode controller cascaded with a discrete sliding mode controller is used to control the system. Neuro-sliding mode controller is used in the outer loop and produces reference for the discrete sliding mode controller which serves as a force controller, in the inner loop. The control signal of the neuro-controller is obtained by minimizing an error function which is derived from Lyapunov stability analysis. The controller performance is tested with different loading conditions and different friction torques and the results are presented.
The paper deals with position tracking control for a linear belt-driven servomechanism. It utilizes VSS theory for control design. The selected sliding manifold was extended in order to involve also non-rigid modes of...
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The paper deals with position tracking control for a linear belt-driven servomechanism. It utilizes VSS theory for control design. The selected sliding manifold was extended in order to involve also non-rigid modes of the elastical servodrive. However, the proposed controller is simple and practical for implementation. The experiments presented in the paper show that the proposed control scheme effectively suppresses vibrations and furthermore extends the closed-loop bandwidth.
Recent years witnessed a growing interest in biped walking robots because of their advantageous use in the human environment. However, their control requires many problems to be solved because of the many degrees of f...
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Recent years witnessed a growing interest in biped walking robots because of their advantageous use in the human environment. However, their control requires many problems to be solved because of the many degrees of freedom and nonlinearity in their dynamics. The so-called open loop walking with offline trajectory generation is one of the control approaches in the literature. There are various difficulties involved in this approach, the most important one being the difficulty in tuning the gait parameters. This paper proposes an online fuzzy adaptation scheme for one of the trajectory parameters in the offline generated walking pattern. A fuzzy identifier system, represented as a three-layer feed-forward neural network is employed to compute the parameter as a function of time in simulations. Fuzzy system parameters are adapted via back-propagation. Virtual torsional springs are attached to the trunk center of the biped. The torque generated by the springs serve as the criterion for the tuning and they help maintaining a stable and a longer walk which is necessary for the online tuning process. 3D simulation and animation techniques are employed for a 12-DOF biped robot to test the proposed adaptive method.
Past three decades witnessed a growing interest in biped walking robots because of their advantageous use in the human environment. However, their control is challenging because of their many DOFs and nonlinearities i...
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Past three decades witnessed a growing interest in biped walking robots because of their advantageous use in the human environment. However, their control is challenging because of their many DOFs and nonlinearities in their dynamics. Offline trajectory generation and the so-called open loop walking is one of the control approaches in the literature. There are various problems involved in this approach, the most pronounced one being the difficulty in tuning the gait parameters. This paper proposes an online fuzzy adaptation scheme for one of the trajectory parameters in the offline generated walking pattern. A fuzzy logic system, represented as a three-layer feed-forward neural network is employed to compute the parameter as a function of time. Fuzzy system parameters are adapted via backpropagation. An on-line tuning algorithm is employed. Virtual torsional springs are attached to the trunk center of the biped. The torques generated by the springs serve as the criteria for the tuning and they help maintaining a stable and a longer walk which is necessary for the on-line tuning process. 3D simulation techniques are employed for a 12-DOF biped robot to test the proposed adaptive method.
Patterned Media Storage (PMS) is one of the promising technologies to overcome the limitations of the conventional magnetic recording. For a high areal density PMS, both inter-track interference (ITI) and inter-symbol...
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We provide a systematic analysis of levels of integration between discrete high-level reasoning and continuous low-level feasibility checks to address hybrid planning problems in robotic applications. We identify four...
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