In the network shortest path interdiction problem, an evader attempts to find the shortest path between the origin and the destination in a network, while an interdictor attempts to maximize the length of this shortes...
详细信息
In the network shortest path interdiction problem, an evader attempts to find the shortest path between the origin and the destination in a network, while an interdictor attempts to maximize the length of this shortest path by interdicting network arcs with limited resources. Therefore, the problem can be formulated as a bi-level programming problem. Existing methods for solving this problem have either low accuracy or slow convergence speed. To address this, in this article, we propose a new algorithm to overcome the above challenges by transforming the problem into an iterative generalized set coverage problem and then solving it by using zero-one linear programming. At each step of the iteration, we obtain a better solution than at the previous step by setting a fixed parameter to interdict a dynamic set regarding the possible interdiction paths. We rigorously prove that the iterative algorithm can converge to the optimal solution. Additionally, the convergence speed is significantly faster than those of baseline methods. For the fixed parameter setting problem, we also propose a parameter adaptive algorithm to further accelerate the convergence speed. Finally, the excellent performance of the proposed algorithms is verified in randomly generated networks and real networks.
This paper proposes an integral sliding mode control that integrates a high-gain observer (HGO) and a radial basis function neural network (RBFNN) for a permanent magnet synchronous motor (PMSM) with uncertainty. Sinc...
详细信息
This paper proposes an integral sliding mode control that integrates a high-gain observer (HGO) and a radial basis function neural network (RBFNN) for a permanent magnet synchronous motor (PMSM) with uncertainty. Since the second-order motion equation of the PMSM is used to improve the control performance, the speed derivative, which cannot be measured directly, is required. Thus, the HGO is designed to estimate the unknown state (speed derivative). In addition, the RBFNN is designed to approximate the compounded disturbance including the lumped disturbance of system and the HGO error effect. Unlike previous studies, the output of the RBFNN is compensated by both the controller and the HGO to improve the system robustness and observer accuracy. The sliding function and the HGO error are both taken into account in the RBFNN to explicitly guarantee the stability of the whole system. To demonstrate the superiority of the proposed method, comparative simulations and experiments were carried out in different cases.
This paper considers the timing error compensation problem for spiral based Self-Servowriting (SSW) process in disk drives manufacturing, wherein the product servo patterns are written based on the prewritten spiral t...
详细信息
This paper considers the timing error compensation problem for spiral based Self-Servowriting (SSW) process in disk drives manufacturing, wherein the product servo patterns are written based on the prewritten spiral tracks. The disk eccentricity and position error in the spiral tracks cause Repeatable Timing Error (RTE) which deteriorates the product servo pattern format and, as a result, the servo sectors are not uniformly distributed along the circular tracks. The spindle speed variation and sensor noise induce Non-repeatable Timing Error (NRTE) which results in phase incoherency at the servo sectors between adjacent tracks. In this paper, a Recursive Least Square (RLS) based parameter adaptive algorithm (PAA) is proposed to estimate and cancel the RTE. An error shaping filter is designed to improve the estimation performance of PAA and reduce the effect of NRTE in the system. Finally, simulation studies using industry supplied data show the effectiveness of the proposed control scheme. The RTE can be cancelled up to 90% and the quality of the written servo sectors is improved by 91%.
Magnetic compasses are widely used in vehicle navigation systems to measure the vehicle headings with respect to the Earth's magnetic north. Due to the local variation of the Earth's magnetic flux density and ...
详细信息
Magnetic compasses are widely used in vehicle navigation systems to measure the vehicle headings with respect to the Earth's magnetic north. Due to the local variation of the Earth's magnetic flux density and the induced magnetic field of the magnetized vehicle body, continuous calibrations of magnetic compasses are required to maintain accurate heading measurements. In this paper two different online compass calibration methods, one based on the parameter adaptation algorithm and the other based on the functional learning algorithm, are developed to achieve online self-calibration function for a flux-gate compass using GPS heading measurements as reference signals. Simulation and experiment results show that the algorithms are effective in removing the magnetic biases and providing a reliable method to improve the magnetic compass performance.
暂无评论