Comparative study on pole placement controller design with U-Model approach & classical approach

© 2016 TCCT. As a universal framework, U-model has established an enabling design prototype for the control of non-linear dynamic plants with concise and applicable linear approaches. This study is devoted to a remaining fundamental research question, that is, while U-model methodology is applied to a linear dynamic plant control system design, how different it is from classical linear approaches. Taking up an initial research, this comparative study uses pole placement controller design as an example to implement with two approaches in terms of U-Model based design and classical control design. Design efficiency and effectiveness are compared analytically and computationally via numerical experiment, which justifies the superiority of U-model in designing linear control systems (or at least in designing pole placement control). In addition, the study provides benchmark examples for users with their ad hoc applications

Audio Localization for Robots Using Parallel Cerebellar Models

© 2016 IEEE. A robot audio localization system is presented that combines the outputs of multiple adaptive filter models of the Cerebellum to calibrate a robot's audio map for various acoustic environments. The system is inspired by the MOdular Selection for Identification and Control (MOSAIC) framework. This study extends our previous work that used multiple cerebellar models to determine the acoustic environment in which a robot is operating. Here, the system selects a set of models and combines their outputs in proportion to the likelihood that each is responsible for calibrating the audio map as a robot moves between different acoustic environments or contexts. The system was able to select an appropriate set of models, achieving a performance better than that of a single model trained in all contexts, including novel contexts, as well as a baseline generalized cross correlation with phase transform sound source localization algorithm. The main contribution of this letter is the combination of multiple calibrators to allow a robot operating in the field to adapt to a range of different acoustic environments. The best performances were observed where the presence of a Responsibility Predictor was simulated

Complexity reduction in the H.264/AVC using highly adaptive fast mode decision based on macroblock motion activity

The H.264/AVC video coding standard is used in a wide range of applications from video conferencing to high-definition television according to its high compression efficiency. This efficiency is mainly acquired from the newly allowed prediction schemes including variable block modes. However, these schemes require a high complexity to select the optimal mode. Consequently, complexity reduction in the H.264/AVC encoder has recently become a very challenging task in the video compression domain, especially when implementing the encoder in real-time applications. Fast mode decision algorithms play an important role in reducing the overall complexity of the encoder. In this paper, we propose an adaptive fast intermode algorithm based on motion activity, temporal stationarity, and spatial homogeneity. This algorithm predicts the motion activity of the current macroblock from its neighboring blocks and identifies temporal stationary regions and spatially homogeneous regions using adaptive threshold values based on content video features. Extensive experimental work has been done in high profile, and results show that the proposed source-coding algorithm effectively reduces the computational complexity by 53.18% on average compared with the reference software encoder, while maintaining the high-coding efficiency of H.264/AVC by incurring only 0.097 dB in total peak signal-to-noise ratio and 0.228% increment on the total bit rate

Design, analysis and implementation of real-time harmonics elimination: A generalised approach

© The Institution of Engineering and Technology 2014. It is not a secret that the problem of harmonics and switching frequency can have a dreadful impact on switching power converters. To counter this problem, a variety of methods have been developed over the years. In this study, a novel method for controlling/eliminating harmonics in switching converters, based on real-time harmonic control, is suggested. The proposed method allows a complete control of the number of eliminated harmonics, as well as the fundamental. The method adopts a modulation strategy using a modified sine carrier. To determine the inverter's optimal switching angles, the well-established particle swarm optimisation algorithm is used. In order to assess the performance of the proposed approach, simulations are carried out. The validity of the method is demonstrated using the digital signal processor based dSPACE environment. The experimental results are very satisfactory and clearly prove the validity of the proposed approach

Adaptive split spectrum processing for ultrasonic signal in the pulse echo test

In this paper, an Adaptive Split Spectrum Processing technique (A-SSP) is proposed, to improve ultrasonic echoes detection. It is an arrangement of conventional Split Spectrum Processing (SSP) with an empirical method of analyzing nonlinear and non-stationary signals, called Empirical Mode Decomposition (EMD). This proposed technique allows breaking up the signal into several bands of frequencies in an adaptive way and intrinsic to the treated signal using EMD. It enables us to know the internal contents and the local changes of the ultrasonic signal and makes the detection of any desired targets more flexible for the coherent noise problem. In the combination phase of A-SSP, a linear operation for selected intrinsic mode functions and a non linear one for non selected intrinsic mode functions are used to reconstruct the signal with separated echoes.To evaluate the proposed techniques (A-SSP with different combination operations), firstly a mortar specimen with artificial defect is used to resolve the defects detection and localization problem. Secondly a paste cement specimen is also used to resolve the materials characterization problem. The signals were obtained using a technique applied in pulse-echo mode, known as the prism technique. Numerical and experimental tests were performed to verify the effectiveness and reliability of the proposed technique and to show its excellent performances

Towards evolving fault tolerant biologically inspired hardware using evolutionary algorithms

Embryonic hardware systems satisfy the fundamental characteristics found in nature which contribute to the development of any multi-cellular living being. Attempts of researchers' in this field to learn from nature have yielded promising results; they proved the feasibility of applying nature-like mechanisms to the world of digital electronics with self-diagnostic and self-healing characteristics, Design by humans however often results in very complex hardware architectures, requiring a large amount of manpower and computational resources. A wider objective is to find novel solutions to design such complex architectures for Embryonic Systems, by problem decomposition and unique design methodologies so that system functionality and performance will not be compromised. Design automation using reconfigurable hardware and EA (evolutionary algorithm), such as GA (genetic algorithms), is one way to tackle this issue. This concept applies the notion of EHW (evolvable hardware) to the problem domain. Unlocking the power of EHW for both novel design solutions and for circuit optimisation has attracted many researchers since the early '90s. The promise of using genetic algorithms through evolvable hardware design will, in this paper, be demonstrated by the authors by evolving a relatively simple combinatorial logic circuit (full-adder)

Mathematical analysis on urine flow traces for non-invasive diagnosis of Detrusor underactivity in men

Detrusor underactivity (DU) is still largely under researched and can only be diagnosed by invasive pressure flow studies (PFS). Theoretically, the flow shape of DU is different from bladder outlet obstruction (BOO), but in practice PFS is the only gold standard for diagnosing DU. It is suggested that detrusor muscle contraction and abdominal squeezing act in total different frequencies, 0.1Hz and 1Hz respectively, which could be an indicator for differentiating DU and BOO. However, this hypothesis has not been quantitatively validated. Therefore, continuing last year’s research, we have conducted a novel study on validating frequencies of abdominal and detrusor muscle activity as reflected in urine flow, and propose a potential indicator for diagnosing DU

Robust adaptive dynamic surface control scheme for a class of single‐input and single‐output uncertain nonlinear systems in strict‐feedback form

This article presents a robust adaptive dynamic surface control using σ-modification adaptation laws for a class of single-input and single-output (SISO) uncertain nonlinear systems in strict-feedback form with parametric uncertainties and external disturbances. The proposed scheme is developed by employing dynamic surface control based robust adaptive control technique. The key features of the approach are that, first, the problem of explosion of complexity inherent in the conventional adaptive backstepping control method is avoided, second, the proposed approach of σ-modification adaptation laws gives fast and accurate parameter estimation performance, and, third, the closed-loop signals of the system are proven to be uniformly ultimately bounded (UUB) by using the Lyapunov stability theory. We show the effectiveness of our approach by simulating an electromechanical system

Smart control based on neural networks for multicellular converters

A smart control based on neural networks for multicellular converters has been developed and implemented. The approach is based on a behavioral description of the different converter operating modes. Each operating mode represents a well-defined configuration for which an operating zone satisfying given invariance conditions, depending on the capacitors’ voltages and the load current of the converter, is assigned. A control vector, whose components are the control signals to be applied to the converter switches is generated for each mode. Therefore, generating the control signals becomes a classification task of the different operating zones. For this purpose, a neural approach has been developed and implemented to control a 2-cell converter then extended to a 3-cell converter. The developed approach has been compared to super-twisting sliding mode algorithm. The obtained results demonstrate the approach effectiveness to provide an efficient and robust control of the load current and ensure the balancing of the capacitors voltages