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Vehicular Ad-hoc Networks are dynamic and temporary network of on-the-fly vehicles with contemporaneous connectivity. Such a network is suitable for a range of applications; starting with those concerning entertainment to improved traffic negotiation to those aimed at human safety and emergency assistance. By its very nature, such a network has higher node mobility, ever-changing topology, varying...
This paper proposes a novel adaptive sliding mode observer to improve the performance of sensorless control of permanent magnet synchronous motor (PMSM) in the More Electric Aircraft (MEA) system. An adaptive sliding mode observer base on adaptively tuning the observer gain is investigated to relax the requirement for the bound of load uncertainties and strengthen the anti-interference ability. System...
A design of a feed-forward-based fractional-order model reference adaptive control (FO-MRAC) compensator using a modified MIT rule to control a DC-DC boost converter is presented in this work. As the plant is non-minimum phase, an implementation of the so-called parallel feed-forward configuration is required to make the plant satisfy ASPR (almost strictly positive real) conditions that in turn, allows...
Invisibility and robustness are two important performance indicators of watermarking algorithm. To improve the performance of watermarking algorithms, the visual model is introduced, and the most classic is the Watson model. However, the original Watson model is defective in resisting amplitude scaling. In this paper, we will propose a new improvement of the Watson model to overcome its shortcomings...
The robotic welding processes have been highly widespread in manufacturing industries due to large-scale production. An area where these processes are widely applied are shipyards, where there are necessary hundreds to thousands of kilograms of weld by hour. However, systems that operate in open-air environments are vulnerable to sundry disturbances, noises in measures, as well as possible unavailability...
This paper proposes an adaptive control for a rigidlink, flexible-joint robot using the Cerebellar Model Articulation Controller (CMAC) and the backstepping method, which is a suitable method when joints are underdamped and exhibit a large amount of flexibility. A previously proposed robust weight update method, deemed the introspective method, is placed into a Lyapunov-stable framework. In the introspective...
Complex simulations are often explained in terms of their features. However, the disconnect between such high-level analysis features and their ad hoc realization in a simulation program impedes effectively conducting exploratory analysis across the structural and representational space of the problem domain. Motivated by the significant role that exploratory analysis plays in computational discovery...
Probabilistic tracking algorithms typically using linear structure to update the learning model. Such linear structure is not appropriate for long-term robust tracking as the occlusion and other challenging factors may interfere the processing of incoming frames. Recently a spatio-temporal context (STC) algorithm based on Bayesian framework has using the context information between the target and...
This work proposes a modification to the Cerebellar Model Articulation Controller (CMAC) for use in a direct adaptive control scheme. With the original CMAC, when an oscillation in the input occurs between two CMAC cells and across the origin, the weights in the adjacent cells drift in opposite directions — resulting in control signal chatter. When the chatter is severe it can cause a sudden increase...
In this paper, a temporally iterative Gaussian Mixture Model (GMM) of Dynamic Texture (DT) for target detection using a moving PTZ camera, is proposed. Camera movement in a PTZ sensor causes motion-based target detection techniques to fail for the periods affected by the scene change. This is because the whole scene is considered a representation of the target motion. When the camera is in motion,...
Reinforcement learning algorithms have been shown to generate procedures for executing quantum control tasks to desired performances. Although reinforcement learning has been effective, its robustness in generating quantum control procedures under general noise condition needs to be tested. Here we consider adaptive quantum-enhanced interferometric phase estimation as a case study in quantum feedback...
Lately, it has been developed many studies related to the control of uncertain systems, one reason for this studies is because it is hard to precisely describe a system, therefore the process is expressed as uncertain systems. One way to represent an uncertain system is by describing the model as a convex combination of several knownmodels. In this paper a state feedback controller is designed for...
Part-based trackers are effective in exploiting local details of the target object for robust tracking. In contrast to most existing part-based methods that divide all kinds of target objects into a number of fixed rectangular patches, in this paper, we propose a novel framework in which a set of deformable patches dynamically collaborate on tracking of non-rigid objects. In particular, we proposed...
Markov Random Fields are widely used to model lightfield stereo matching problems. However, most previous approaches used fixed parameters and did not adapt to lightfield statistics. Instead, they explored explicit vision cues to provide local adaptability and thus enhanced depth quality. But such additional assumptions could end up confining their applicability, e.g. algorithms designed for dense...
To solve deep metric learning problems and producing feature embeddings, current methodologies will commonly use a triplet model to minimise the relative distance between samples from the same class and maximise the relative distance between samples from different classes. Though successful, the training convergence of this triplet model can be compromised by the fact that the vast majority of the...
This work presents a dead-time compensation technique for robust predictive current control of grid-connected voltage source inverter. Among current controls, dead-beat predictive controllers are one of the fastest, but are extremely sensitive to inconsistencies between the model and the actual plant. Dead-time modifies the plant model and is one of the main sources of distortion in high dynamic range...
This paper presents a robustness-improving discrete-time current controller utilizing predictive current control (PCC) for permanent magnet linear synchronous motor (PMLSM). The stability and robustness of the PCC system is highly affected by the parameters mismatch between the controller and the plant, and the inherent digital control delay can limit the bandwidth. Toward this, an integrated method...
In view of the magnetic chain recognition accuracy problem of the bearingless induction motor (BIM) under direct torque control (DTC), a speed sensorless based on Super-Twisting-model reference adaptive system (MRAS) is proposed. The speed sensoerless makes rotor flux on the foundation of Super-Twisting second-order sliding mode as a reference model. The speed sensorless is constructed by using MRAS...
Backstepping-based, robust stabilization procedure for nonlinear systems with time-varying parameters is presented. First a general approach for nonlinear parameter dependence is developed, then the special case of a linear-in-parameters system is considered. Possible simplifications of the design and benefits of this system structure are discussed and illustrated by examples.
The paper considers the problem of ship autopilot design based on Bech's model of the vessel. Since the ship model is highly nonlinear and the state vector is not completely measurable, the control system synthesis is performed by means of output feedback linearization method combined with a state observer. Due to considerable parameter variations and other substantial uncertainties a robust-adaptive...
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