Abstract: A method for determining MTPA, flux-weakening, and MTPV operating points over the full speed range of an IPM motor for the most efficient torque control of the motor using a neural network is provided. The neural network is trained using a cloud-based neural network training algorithm. A special technique is developed to generate neural network training data, that is particularly suitable and favorable, to develop a high-performance neural network-based IPM torque control system, and the impact of variable motor parameters is embedded into the neural network system development and training. The provided method can achieve a fast and accurate current reference generation with a simple neural network structure, for optimal torque control of an IPM motor. The method can handle the MTPA, MTPV, and flux-weakening operation considering physical motor constraints.

Abstract: An example method for controlling a DC/DC converter or a standalone DC microgrid comprises an artificial neural network (ANN) based control method integrated with droop control. The ANN is trained to implement optimal control based on approximate dynamic programming. In one example, Levenberg-Marquardt (LM) algorithm is used to train the ANN, where the Jacobian matrix needed by LM algorithm is calculated via a Forward Accumulation Through Time algorithm. The ANN performance is evaluated by using power converter average and switching models. Performance evaluation shows that a well-trained ANN controller has a strong ability to maintain voltage stability of a standalone DC microgrid and manage the power sharing among the parallel distributed generation units. Even in dynamic and power converter switching environments, the ANN controller shows an ability to trace rapidly changing reference commands and tolerate system disturbances, and operate the DC/DC converter or the microgrid in standalone conditions.

Abstract: A dc/dc buck converter controller comprises an artificial neural network (ANN) controller comprising an input layer and an output layer. The input layer receives an error value of a dc/dc buck converter and an integral of the error value. The output layer produces an output error voltage. A pulse-width-modulation (PWM) sliding mode controller (SMC) is configured to receive the output error voltage and produce a control action voltage by multiplying the output error voltage with a PWM gain. A drive circuit is configured to receive the control action voltage and provide a drive voltage to an input switch of the dc/dc buck converter. A PI control block is configured to modify a reference output voltage based on a maximum current constraint input. A locking circuit is configured to maintains the output error voltage at the saturation limit of the PWM SMC to manage a maximum duty cycle constraint.

Abstract: A dc/dc buck converter controller comprises an artificial neural network (ANN) controller comprising an input layer and an output layer. The input layer receives an error value of a dc/dc buck converter and an integral of the error value. The output layer produces an output error voltage. A pulse-width-modulation (PWM) sliding mode controller (SMC) is configured to receive the output error voltage and produce a control action voltage by multiplying the output error voltage with a PWM gain. A drive circuit is configured to receive the control action voltage and provide a drive voltage to an input switch of the dc/dc buck converter. A PI control block is configured to modify a reference output voltage based on a maximum current constraint input. A locking circuit is configured to maintains the output error voltage at the saturation limit of the PWM SMC to manage a maximum duty cycle constraint.

Abstract: An example method for controlling a DC/DC converter or a standalone DC microgrid comprises an artificial neural network (ANN) based control method integrated with droop control. The ANN is trained to implement optimal control based on approximate dynamic programming. In one example, Levenberg-Marquardt (LM) algorithm is used to train the ANN, where the Jacobian matrix needed by LM algorithm is calculated via a Forward Accumulation Through Time algorithm. The ANN performance is evaluated by using power converter average and switching models. Performance evaluation shows that a well-trained ANN controller has a strong ability to maintain voltage stability of a standalone DC microgrid and manage the power sharing among the parallel distributed generation units. Even in dynamic and power converter switching environments, the ANN controller shows an ability to trace rapidly changing reference commands and tolerate system disturbances, and operate the DC/DC converter or the microgrid in standalone conditions.

Abstract: Provided is a method for extracting and refining alkaloids from ipecac, comprising: (1) grinding ipecac, adding acidic methanol/ethanol solution for extraction, obtaining an extraction solution A, concentrating under a reduced pressure, and obtaining a concentrated solution B; (2) using reversed-phase polymer filler J for adsorption, and performing desorption by washing with water, collecting a washing solution C, eluting with an alcoholic solution E and collecting a desorption solution D; (3) injecting the washing solution C and the desorption solution D into a preparative high performance liquid chromatograph for separation and purification respectively, to collect a solution G, and a solution H and a solution I respectively; and (4) concentrating the solutions G, H and I, which are then subjected to reversed-phase polymer filler K for adsorption respectively; eluting them respectively after adsorption with an alcoholic solution F; concentrating obtained eluates to dryness; and then performing vacuum drying

Abstract: Provided is a method for extracting and refining alkaloids from ipecac, comprising: (1) grinding ipecac, adding acidic methanol/ethanol solution for extraction, obtaining an extraction solution A, concentrating under a reduced pressure, and obtaining a concentrated solution B; (2) using reversed-phase polymer filler J for adsorption, and performing desorption by washing with water, collecting a washing solution C, eluting with an alcoholic solution E and collecting a desorption solution D; (3) injecting the washing solution C and the desorption solution D into a preparative high performance liquid chromatograph for separation and purification respectively, to collect a solution G, and a solution H and a solution I respectively; and (4) concentrating the solutions G, H and I, which are then subjected to reversed-phase polymer filler K for adsorption respectively; eluting them respectively after adsorption with an alcoholic solution F; concentrating obtained eluates to dryness; and then performing vacuum drying