Abstract:Abstract:Regarding the shortcomings of the cross-coupling control structure during the start-up of a multi-motor with load-namely, a large synchronization error and a long start-up time-this paper proposes a fuzzy self-adjusting cross-coupling control structure. This structure combines a fuzzy self-adjusting filter and an advanced synchronization compensator. The fuzzy self-adjusting filter adjusts the "softened speed", a newly established concept, so that each motor follows the trajectory of the softened spee… Show more
“…As a result, reference feedforward or disturbance feedforward can be obtained [47]. Finally, also synchronizing control (also known as cross-coupling) can be used as a control architecture, referring to a control loop extension used to improve output synchronization between coupled subsystems that are both parts of a larger system [48]. Thereby, the synchronous control of subsystem outputs is improved by taking into account the output deviations.…”
Section: Control Configuration Optimizationmentioning
confidence: 99%
“…This can be applied by adding separate controllers (e.g., P/PI/PID) acting on the relative fault signals between different actuators. This can reduce the error between the different outputs when one or more subsystems are disturbed internally or externally [48].…”
Section: Control Configuration Optimizationmentioning
In plants consisting of multiple interacting subsystems, the decision on how to optimally select and place actuators and sensors and the accompanying question on how to control the overall plant is a challenging task. Since there is no theoretical framework describing the impact of sensor and actuator placement on performance, an optimization method exploring the possible configurations is introduced in this paper to find a trade-off between implementation cost and achievable performance.Moreover, a novel model-based procedure is presented to simultaneously co-design the optimal
“…As a result, reference feedforward or disturbance feedforward can be obtained [47]. Finally, also synchronizing control (also known as cross-coupling) can be used as a control architecture, referring to a control loop extension used to improve output synchronization between coupled subsystems that are both parts of a larger system [48]. Thereby, the synchronous control of subsystem outputs is improved by taking into account the output deviations.…”
Section: Control Configuration Optimizationmentioning
confidence: 99%
“…This can be applied by adding separate controllers (e.g., P/PI/PID) acting on the relative fault signals between different actuators. This can reduce the error between the different outputs when one or more subsystems are disturbed internally or externally [48].…”
Section: Control Configuration Optimizationmentioning
In plants consisting of multiple interacting subsystems, the decision on how to optimally select and place actuators and sensors and the accompanying question on how to control the overall plant is a challenging task. Since there is no theoretical framework describing the impact of sensor and actuator placement on performance, an optimization method exploring the possible configurations is introduced in this paper to find a trade-off between implementation cost and achievable performance.Moreover, a novel model-based procedure is presented to simultaneously co-design the optimal
“…Some effective synchronous control strategies have been investigated in recent decades, such as master-slave synchronization strategy [4], adjacent coupling control [5], ring coupling synchronization technique [6], cross-coupling synchronization strategy [7,8], virtual line-shaft synchronization scheme [9], and relative coupling synchronization method [10]. Among all the above mentioned synchronization schemes, adjacent coupling control is designed based on minimum relative axial thought, which means that the torque of each motor should be able to make the tracking error of itself converge to zero and lead the synchronization error to converge to zero stably between any motor and adjacent motors.…”
In this paper, a speed tracking and synchronization control approach is proposed for a multimotor system based on fuzzy active disturbance rejection control (FADRC) and enhanced adjacent coupling scheme. By employing fuzzy logic rules to adjust the coefficients of the extended state observer (ESO), FADRC is presented to guarantee the speed tracking performance and enhance the system robustness against external disturbance and parametric variations. Moreover, an enhanced adjacent coupling synchronization control strategy is proposed to simplify the structure of the speed synchronization controller through introducing coupling coefficients into the conventional adjacent coupling approach. Based on the proposed synchronization control scheme, an adaptive integral sliding mode control (AISMC) is investigated such that the chattering problem in conventional sliding mode control can be weakened by designing an adaptive estimation law of the control gain. Comparative simulations are carried out to prove the superiorities of the proposed method.
“…In order to guarantee the stable operation of the multiple motors, connected in parallel, Bidart et al [13] proposed a scheme of selecting one of the motors as the master motor. Based on an improved cross-coupling structure [14], Wei C. et al adopted a fuzzy self-adjusting cross-coupling control structure to control speed synchronism for the multiple permanent magnet synchronous motors that were connected in parallel. For the multi-PMSM system connected in series, Chiasson et al [15] presented a strategy to control each motor's quadrature current.…”
The permanent magnet synchronous motor (PMSM) with dual-rotating rotors is a typical nonlinear multi-variable coupled system. It is sensitive to load disturbances and the change of interior parameters. The traditional proportional-integral (PI) controller is widely used in the speed control of a motor because of its simplicity; however, it cannot meet the requirements needed for high performance. In addition, when the loads of both of the rotors change, it is difficult to ensure that the system runs stably. With an aim to mitigate these problems, a method called master-slave motor control is proposed to guarantee the stability of the motor system in all cases. And then, a speed controller is designed to eliminate the influence of uncertain terms. The proposed control strategy is implemented both in simulations and in experiments. Through the analysis and comparison of the proportional-integral (PI) controller and the sliding-mode controller, the effectiveness of the proposed control strategy is validated.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.