Direct decentralized neural control for nonlinear MIMO magnetic levitation system

A direct modified Elman neural networks (MENNs)-based decentralized controller is proposed to control the magnets of a nonlinear and unstable multi-input multi-output (MIMO) levitation system for the tracking of reference trajectories. First, the operating principles of a magnetic levitation system with two moving magnets are introduced. Then, due to the exact dynamic model of the MIMO magnetic levitation system is not clear, two MENNs are combined to be a direct MENN-based decentralized controller to deal with the highly nonlinear and unstable MIMO magnetic levitation system. Moreover, the connective weights of the MENNs are trained online by back-propagation (BP) methodology and the convergence analysis of the tracking error using discrete-type Lyapunov function is provided. Based on the direct and decentralized concepts, the computational burden is reduced and the controller design is simplified. Furthermore, the experimental results show that the proposed control scheme can control the magnets to track various periodic reference trajectories simultaneously in different operating conditions effectively.     

Decentralized variable structure control for mismatched uncertain large-scale systems: a new approach

In this paper, a decentralized variable structure controller for a class of large-scale systems with mismatched uncertainties is proposed. In every subsystem, two sets of switching surfaces is introduced. New invariance conditions are derived such that the system in the new sliding mode is completely invariant to both matched and mismatched uncertainties. To demonstrate the effectiveness of the proposed scheme, a decentralized variable structure controller is synthesized to perform the new sliding mode. Moreover, the stability analysis of the overall system is also provided.     

Multiphase B-spline level set and incremental shape priors with applications to segmentation and tracking of left ventricle in cardiac MR images

This paper presents a new multiphase active contour model for object segmentation and tracking. The paper introduces an energy functional which incorporates image feature information to drive contours toward desired boundaries, and shape priors to constrain the evolution of the contours with respect to reference shapes. The shape priors, in the model, are constructed by performing the incremental principal component analysis (iPCA) on a set of training shapes and newly available shapes which are the resulted shapes derived from preceding segmented images. By performing iPCA, the shape priors are updated without repeatedly performing PCA on the entire training set including the existing shapes and the newly available shapes. In addition, by incrementally updating the resulted shape information of consecutive frames, the approach allows to encode shape priors even when the database of training shapes is not available. Moreover, in shape alignment steps, we exploit the shape normalization procedure, which takes into account the affine transformation, to directly calculate pose transformations instead of solving a set of coupled partial differential equations as in gradient descent-based approaches. Besides, we represent the level set functions as linear combinations of continuous basic functions expressed on B-spline basics for a fast convergence to the segmentation solution. The model is applied to simultaneously segment/track both the endocardium and epicardium of left ventricle from cardiac magnetic resonance (MR) images. Experimental results show the desired performances of the proposed model.     

Model Reference Adaptive Control Design for a Shunt Active-Power-Filter System

In this paper, model reference adaptive control (MRAC) is proposed for a single-phase shunt active power filter (APF) to improve line power factor and to reduce line current harmonics. The proposed APF controller forces the supply current to be sinusoidal, with low current harmonics, and to be in phase with the line voltage. The advantages of using MRAC over conventional proportional-integral control are its flexibility, adaptability, and robustness; moreover, MRAC can self-tune the controller gains to assure system stability. Since the APF is a bilinear system, it is hard to design the controller. This paper will solve the stability problem when a linearization method is used to solve the nonlinearity of the system. Moreover, by using Lyapunov's stability theory and Barbalat's lemma, an adaptive law is designed to guarantee an asymptotic output tracking of the system. To verify the proposed APF system, a digital signal controller (dsPIC30F4012) is adopted to implement the algorithm of MRAC, and a 1-kVA laboratory prototype is built to test feasibility. Experimental results are provided to verify the performance of the proposed APF system.     

Design of large-scale time-delayed systems with dead-zone input via variable structure control

To deal with time delay in the interconnection and dead-zone nonlinearity in the input, a new decentralized variable structure control (DVSC) law is proposed for a class of uncertain large-scale system (LSS) in this paper. The developed DVSC for the interconnected systems is realized independently through the delay terms. The applied control effort of the DVSC law is always out of the dead-band to eliminate the effects arisen from the dead-zone nonlinearity in the input function. In the sliding mode, it can be seen that the invariance condition also holds. It is worth noting that the traditional large-scale time-delayed system is only a special case in this work. Illustrative examples are given to verify the validity of the developed controller.     

Modified FIR filter with phase compensation technique tofeedforward active noise controller design

In this paper, a finite-impulse response (FIR) filter with phase compensation is proposed to design the digital controller for active noise cancellation in ducts. This method can overcome the influence of delay effects, which arise from the analog devices, and then help to improve the ability of noise reduction. Moreover, all the control algorithms are implemented in a fixed-point-type digital signal processor that produces an antinoise signal to cancel noise in the authors' experiments. Experiments are demonstrated in a polyvinyl chloride material circular duct. It is proved that, by using the FIR filter with phase compensation, the reduction of broadband noise is about 20 dB, and about 50 dB of narrowband noise. The system also provides the ability to cancel the noise with two harmonic components, like automobile noise