“…T l = 0.04z (35) where a = 5, b = 12y, c = 25; with initial conditions: x(0) = 0.1, y(0) = 0.1 and z(0) = 0.5 [48]. Below are two cases in which the reference trajectory has different natures.…”
This article focuses on the optimal gain selection for Proportional Integral (PI) controllers comprising a speed control scheme for the Permanent Magnet Synchronous Motor (PMSM). The gains calculation is performed by means of different algorithms inspired by nature, which allows improvement of the system performance in speed regulation tasks. For the tuning of the control parameters, five optimization algorithms are chosen: Bat Algorithm (BA), Biogeography-Based Optimization (BBO), Cuckoo Search Algorithm (CSA), Flower Pollination Algorithm (FPA) and Sine-Cosine Algorithm (SCA). Finally, for purposes of efficiency assessment, two reference speed profiles are introduced, where an acceptable PMSM performance is attained by using the proposed PI controllers tuned by nature inspired algorithms.
“…T l = 0.04z (35) where a = 5, b = 12y, c = 25; with initial conditions: x(0) = 0.1, y(0) = 0.1 and z(0) = 0.5 [48]. Below are two cases in which the reference trajectory has different natures.…”
This article focuses on the optimal gain selection for Proportional Integral (PI) controllers comprising a speed control scheme for the Permanent Magnet Synchronous Motor (PMSM). The gains calculation is performed by means of different algorithms inspired by nature, which allows improvement of the system performance in speed regulation tasks. For the tuning of the control parameters, five optimization algorithms are chosen: Bat Algorithm (BA), Biogeography-Based Optimization (BBO), Cuckoo Search Algorithm (CSA), Flower Pollination Algorithm (FPA) and Sine-Cosine Algorithm (SCA). Finally, for purposes of efficiency assessment, two reference speed profiles are introduced, where an acceptable PMSM performance is attained by using the proposed PI controllers tuned by nature inspired algorithms.
“…Taking the angular acceleration as the state variable, i.e. 푥 1푗 = 휔 푗 , 푥 2푗 =푥 1푗 , 푥 3푗 = 푇 푒푗 , the equation in the ideal system state is [7,24] where…”
This paper investigates a virtual line shafting-based total-amount coordinated control method of multi-motor traction power to solve the traffic safety problem caused by train traction power loss. This method considers the total amount instead of the synchronous control amongst single motors in a multi-motor control system. Firstly, a block diagram of the proposed method is built. Secondly, on the basis of this diagram, an accurate system model with parameter perturbations is constructed. Thirdly, a virtual controller is designed to quickly adjust the output torque of the virtual motor and to realise a tracking control of the reference torque. A total-amount coordinated control strategy based on the integral sliding mode is also designed to keep the total traction power of the multi-motor system constant under uncertain and unknown disturbances. Lyapunov stability theory is used to prove the system stability. The simulation and experiment results verify the effectiveness of the virtual controller and the total-amount coordinated control strategy in guaranteeing system robustness under disturbances and parameter perturbations.
“…Thus, some signal differentiation method could be implemented. 34 Moreover, for highly uncertain operation environments, the following family of Hurwitz polynomials can be used to compute the control gains: P i,C (s) = (s + p i ) n i i = q, d (26) with n q = r + 3, n d = r + 2. Then , the control gains can be calculated algebraically as…”
Summary
A new dynamic output feedback control scheme for planned motion reference tracking on nonlinear permanent‐magnet synchronous motors in presence of uncertainty is proposed. Parametric uncertainty, unmodeled dynamics, and variable mechanical load torque are considered as time‐varying unknown signals to be suppressed actively by control voltage inputs. Dynamic feedback of output signals to be controlled are only required to perform entire planned motion tracking. The control scheme can be performed on both classes of interior‐ and surface‐permanent‐magnet synchronous motors. Analytical and numerical results prove the effectiveness of the dynamic output feedback control method on multi‐input multi‐output nonlinear synchronous motors.
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