Prasanna Boyagoda

Power conversion PWM amplifier with two paralleled four quadrant chopper for MRI gradient coil magnetic field current tracking implementation

This paper presents a two-paralleled PWM amplifier using switch-mode power current tracking technique in order to generate a gradient magnetic field in the magnetic resonance imaging (MRI) system. This circuit has 8-IGBTs at their inputs/outputs so as to realize further high-power density. A digital control scheme is adopted to minimize the current ripple and improve its control response in the gradient coils (GCs). It is proved that the proposed technique will highly enlarge the diagnostic target and improve the image quality of MRI.

Analysis on a PWM power conversion amplifier with IGBT macro model to generate gradient magnetic fields in MRI systems

This paper presents a two-paralleled PWM amplifier using switch-mode power current tracking technique in order to generate a gradient magnetic field in the magnetic resonance imaging (MRI) system. This circuit has 8-IGBTs at their inputs/outputs so as to realize further high-power density. A digital control scheme can minimize the current ripple and improve its control response in the gradient coils (GCs). It is proved that the proposed technique will highly enlarge the diagnostic target and improve the image quality of MRI.

DSP-based high precision current tracking control of gradient coil in two-paralleled PWM amplifiers for MRI systems

This paper presents a two-paralleled PWM amplifier using the switch-mode power current tracking technique in order to generate a gradient magnetic field in a magnetic resonance imaging (MRI) system. This circuit has 8-IGBTs at their inputs/outputs so as to realize further high-power density. A digital control scheme can minimize the current ripple and improve its control response in the gradient coils (GCs). It is proved that the proposed technique will highly enlarge the diagnostic target and improve the image quality of MRI.

Digitally-controlled optimum current tracking scheme of two-paralleled high-power PWM amplifier for magnetic resonance imaging

This paper presents a two paralleled bridge power conversion circuit using switch-mode power current tracking technique in order to generate a gradient magnetic field in the magnetic resonance imaging (MRI) system. This power amplifier is connected in parallel with the conventional 4-switch type full-bridge PWM power conversion circuit using IGBTs at their inputs/outputs in order to realize further high-power density. A digital optimal control scheme for PWM power conversion circuit requires minimization of the current ripple and improvement of dynamic response characteristics of the current in the gradient coils (GCs). It is proposed in this paper that a unique current tracking control scheme can achieve the research objective. This power amplifier can be easily used in the advanced MRI because the output current waves are not restricted to ramped-square waves and can assume any arbitrary wave forms. It is proved that the proposed technique will highly enlarge the diagnostic target and improve the image quality of MRI.

A neural network based positional tracking controller for servo systems

Most neural network (NN) based trajectory tracking controllers for servo systems are built on learning explicit inverse dynamics of the system to be controlled. However, due to various complexities in these systems, the learning process may require a large amount of training data to obtain the exact dynamics of the system. To overcome this problem a novel NN based trajectory tracking controller is introduced which neither requires a priori knowledge of the dynamics nor learning of system dynamics. The proposed control scheme incorporates expert knowledge and is decentralized to deactivate the coupled dynamics associated with certain systems like robotic manipulators. The NN is employed to classify the system input-output measurements into several patterns depending on the displacement and velocity deviations from the respective desired trajectories. A proportional plus derivative gain control action is determined from a look-up table corresponding to the classification from the NN. Furthermore, an integrator is applied to enhance system performance. Several PD gains are introduced in a staggered format relative to the magnitudes of the displacement and velocity tracking errors, resulting in a controller that is robust to both structured and unstructured uncertainties.

Advanced digital control scheme of two-paralleled bridge type current tracking power conversion amplifier for magnetic resonance imaging

This paper presents a two parallel bridge PWM power conversion circuit for the switch-mode gradient power current tracking amplifiers for the gradient coil in the magnetic resonance imaging (MRI) systems. This power amplifying processor is connected in parallel with the conventional 4-switch type full-bridge power circuits using IGBTs at their inputs/outputs in order to realize further high-power density. A unique digital control scheme for PWM power conversion, which requires minimized ripple and improved rise/fall response characteristics of the current in the gradient coils (GCs), is proposed and described here. This power processor as an overall system is completely suited to the power amplifier for advanced MRI applications because the output current waves are not restricted to ramped-square waves and can assume any arbitrary waveform. It is expected that the proposed techniques will highly enlarge the diagnostic target and improve the image quality of MRI.

An advanced tracking controller with neural networks for servo systems

A novel controller for generic servo systems using a neural network input-output measurement classifier and a staggered proportional plus integral plus derivative-like gain control scheme is proposed. The controller incorporates a knowledge-based control strategy and does not require a priori knowledge of the plant. The system controller is robust to both structured and unstructured uncertainties.

Optimal control of a servo system regenerating conservative energy to a condenser

An optimal controller for a servo system with a manipulator is developed. The emphasis is on the minimization of the overall energy consumption of the system. The optimum current function is obtained under the minimization criteria of the consumption energy for a manipulator under gravity conditions and otherwise. The solution to the two-point-boundary-value-problem is obtained by using the generalized Newton-Raphson method. Further when the link of the manipulator is decelerated, the generated electric power from the motor is stored in a condenser. This is achieved while supplying the optimal current by using a DC chopper circuit. It is shown from experiment that the efficiency of energy storage is improved by increasing the capacitance of the condensers. The overall energy is shown to decrease by this method.

AC servo motor drive systems using auto-tuning gain parameter processor with automatic learning control scheme

In this paper, an advanced control method of system parameter auto-tuning implementation for an AC servo system using fuzzy reasoning logic with an automatic learning control function is described. This method includes three features: (i) it is not necessary to input some kinds of fuzzy rules to the servo system before starting auto-tuning operation, thus, the fuzzy rules can be automatically produced in logical process learning; (ii) knowledge and information about the system parameter tuning technique are not required; and (iii) both high speed response and robustness can be obtained. The feasible effectiveness of this auto-tuning processing approach for an AC servo system are practically confirmed through experimental results.

A servo system tracking controller based on neural networks

A novel neural network (NN) based trajectory tracking controller for a servo system that also incorporates a knowledge-based control scheme is proposed in this paper. A decentralized control scheme, which neither requires a priori knowledge of the plant nor learning of the system dynamics, is introduced to deactivate the coupled dynamics associated with certain systems like robotic manipulators. The NN is employed to classify the system input-output measurements into several patterns depending on the displacement and velocity deviations from the respective desired trajectories. A proportional plus derivative gain control action is determined from a look-up table corresponding to the classification from the NN. Furthermore, an integrator is applied to enhance system performance. Several PD gains are introduced in a staggered format relative to the magnitudes of the displacement and velocity tracking errors, resulting in a controller that is robust to both structured and unstructured uncertainties.