Dynamic model reference control of a PMAC motor for automotive traction drives

The permanent magnet AC (PMAC) motor drive is a bilinear closely coupled system subject to saturation due to finite DC supply voltage and current limitation for hardware protection. Model reference control can be applied to the PMAC motor, with PI current controllers tracking the model reference current command values. The finite supply voltage constraint results in degradation of system performance when the current regulators saturate. In this paper, a dynamic model reference controller is presented which includes the current and voltage limits and constrains the magnitude of the current vector command signals, operating the system just within the limits of saturation. This allows the PI controllers to accurately track the command signals and retain control of the current vector. The controller ensures maximum possible dynamic performance of the system. The system and controller is presented and experimentally verified, and the PI controller gains are found by Monte Carlo simulation

Optimisation of maintenance scheduling strategies on the grid

The emerging paradigm of Grid Computing provides a powerful platform for the optimisation of complex computer models, such as those used to simulate real-world logistics and supply chain operations. This paper introduces a Grid-based optimisation framework that provides a powerful tool for the optimisation of such computationally intensive objective functions. This framework is then used in the optimisation of maintenance scheduling strategies for fleets of aero-engines, a computationally intensive problem with a high-degree of stochastic noise, achieving substantial improvements in the execution time of the algorithm

Optimisation of maintenance scheduling strategies on the grid

The emerging paradigm of Grid Computing provides a powerful platform for the optimisation of complex computer models, such as those used to simulate real-world logistics and supply chain operations. This paper introduces a grid-based optimisation framework that provides a powerful tool for the optimisation of such computationally intensive objective functions. This framework is then used in the optimisation of maintenance scheduling strategies for fleets of aero-engines, a computationally intensive problem with a high-degree of stochastic noise

On steady state and dynamic performance of model reference control for a permanent magnet synchronous motor

Existing control strategies seldom explore the dynamic system characteristics of permanent magnet motors, relying upon the onset of current errors to detect and regulate current phase advance. Also, look-up table functions are the solution of steady state equations. For high performance motors with small time constants, insufficient voltage headroom is available at the onset of current error to allow movement of the current vector to its optimal position quickly enough, with resulting reduced torque output or instability. A model reference control method is proposed, which has a relatively small computational overhead, forms an appropriate basis for adaptive methods, and controls the system smoothly through the transition into the phase advance mode by extending the controller to utilise the dynamic system terms. The onset of phase advance is anticipated, the resulting voltage headroom allowing the model reference to retain control of the current vecto

The Role of the Loading Condition in Predictions of Bone Adaptation in a Mouse Tibial Loading Model

The in vivo mouse tibial loading model is used to evaluate the effectiveness of mechanical loading treatment against skeletal diseases. Although studies have correlated bone adaptation with the induced mechanical stimulus, predictions of bone remodeling remained poor, and the interaction between external and physiological loading in engendering bone changes have not been determined. The aim of this study was to determine the effect of passive mechanical loading on the strain distribution in the mouse tibia and its predictions of bone adaptation. Longitudinal micro-computed tomography (micro-CT) imaging was performed over 2 weeks of cyclic loading from weeks 18 to 22 of age, to quantify the shape change, remodeling, and changes in densitometric properties. Micro-CT based finite element analysis coupled with an optimization algorithm for bone remodeling was used to predict bone adaptation under physiological loads, nominal 12N axial load and combined nominal 12N axial load superimposed to the physiological load. The results showed that despite large differences in the strain energy density magnitudes and distributions across the tibial length, the overall accuracy of the model and the spatial match were similar for all evaluated loading conditions. Predictions of densitometric properties were most similar to the experimental data for combined loading, followed closely by physiological loading conditions, despite no significant difference between these two predicted groups. However, all predicted densitometric properties were significantly different for the 12N and the combined loading conditions. The results suggest that computational modeling of bone’s adaptive response to passive mechanical loading should include the contribution of daily physiological load

Neural control of nonlinear systems with composite adaptation for improved convergence of Gaussian networks

The use of composite adaptive laws for control of the ane class of nonlinear systems having unknown dynamics is proposed. These dynamics are approximated by Gaussian radial basis function neural networks whose parameters are updated by a composite law that is driven by both tracking and estimation errors. This is motivated by the need to improve the speed of convergence of the unknown parameters, hence resulting in better system performance. To ensure global stability despite the inevitable network approximation errors, the control law is augmented with a low gain sliding mode component and deadzone adaptation is used for the indirect part of the composite law. The stability of the system is analyzed and the effectiveness of the method is demonstrated by simulation.peer-reviewe