Current Issue : January - March Volume : 2021 Issue Number : 1 Articles : 5 Articles
The Underwater Vehicle Manipulator System (UVMS) is an essential equipment for underwater operations. However, it is\ndifficult to control due to the constrained problems of weak illumination, multidisturbance, and large inertia in the underwater\nenvironment. After the UVMS mathematical model based on water flow disturbance is established, fusion image enhancement\nalgorithm based on Retinex theory is proposed to achieve fine perception of the target. The control method based on redundant\nresolution algorithm is adopted to establish the anti-interference controller of the manipulator, which can compensate the internal\nand external uncertain interference. Finally, stable underwater operation is realized. The target ranging method is used to solve the\nangle of each joint of the manipulator to complete the tracking and grasping of the target. Underwater experiments show that the\nalgorithm can improve the clarity of underwater images, ensure the accuracy of robot capture, and optimize the UVMS\ncontrol performance....
Permanent magnet synchronous motors (PMSMs) have been widely applied in the shipborne rocket launcher systems due to their\nhigh torque density and high efficiency. However, since there are many external disturbances from the shipborne rocket launcher,\nthe tracking performance under random noises needs to be improved. In this paper, an improved finite control set optimal control\n(IFCSOC) based on a super-twisting extended state observer (SESO) is investigated for position tracking control of PMSMs. The\nSESO can improve the anti-interference ability of the proposed controller. Moreover, in order to improve tracking accuracy,\nTaylorâ??s formula is used to solve the phase-lag problem of nonlinear tracking differentiator in IFCSOC. Simulation shows that\ncompared with conventional FCSOC, IFCSOC exhibits better robustness under random disturbances. Furthermore, the semiphysical\nexperiment is conducted to verify the proposed IFCSOC strategy....
Model predictive control (MPC) has been widely implemented in the motor because of its simple control design and good results.\nHowever, MPC relies on the permanent magnet synchronous motor (PMSM) system model. With the operation of the motor,\nparameter drift will occur due to temperature rise and flux saturation, resulting in model mismatch, which will seriously affect the\ncontrol accuracy of the motor. This paper proposes a model predictive control based on parameter disturbance compensation that\nmonitors system disturbances caused by motor parameter drift and performs real-time parameter disturbance compensation. And\nthe frequency-domain method was used to analyze the convergence and filterability of the model. The Bode diagram of\nmeasurement error and input disturbance was studied when the parameters were underdamped, critically damped, and\noverdamped. Guidelines for parameter selection are given. Simulation results show that the proposed method has good dynamic\nperformance, anti-interference ability, and parameter robustness, which effectively avoids the current static difference and\noscillation problems caused by parameter changes....
Tank level control is ubiquitous in industry. The focus of this paper is on accurate\nliquid level control in single tank systems which can be actuated continuously\nand modulation of the level setpoint is also required, for example in\ncascade control loops or supervisory Model Predictive Control (MPC) applications.\nTo avoid common problems encountered when using fixed gain or\nadaptive/gain scheduled schemes, an accurate technique based around feedback\nlinearization and Proportional Integral (PI) control is introduced. This\nsimple controller can maintain linear performance over the full operating\nrange of a uniform tank. As will be demonstrated, the implementation overhead\ncompared to a regular PI controller is negligible, making it ideal for industrial\nimplementation. Implementation details and parameter identification\nfor adaptive implementation are discussed. Simulations coupled with experimental\nresults using a large-scale laboratory level control system using commercial\nindustrial control equipment validate the approach, and illustrate its\neffectiveness for both level tracking and disturbance rejection....
This article describes a technique that allows a photovoltaic (PV) production\nunit to obtain the maximum power at all times. Here, we use the MPPT control\nvia fuzzy logic on a DC/DC boost-type converter. In order to achieve our\ngoals, we first proceeded to model a PV panel. The resulting model offers the\npossibility to better account for the influence of different physical quantities\nsuch as temperature, irradiation, series resistance, shunt resistance and diode\nsaturation current. Thus, the maximum power to be provided by the PV system\nis acquired by fuzzification and defuzzification of the input and output\nvariables of the converter. Subsequently, a virtual model of an 800 Watt PV\nprototype is implemented in the Matlab environment. The simulation results\nobtained and presented, show the feasibility and efficiency of the proposed\ntechnology. Indeed, for a disturbance caused by a variation in brightness, our\nsystem guarantees the maximum stable power after 1.4 s. While for a load\nvariation, the maximum power is continuous....
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