New sequential partial-update least mean M-estimate algorithms for robust adaptive system identification in impulsive noise

The sequential partial-update least mean square (S-LMS)-based algorithms are efficient methods for reducing the arithmetic complexity in adaptive system identification and other industrial informatics applications. They are also attractive in acoustic applications where long impulse responses are encountered. A limitation of these algorithms is their degraded performances in an impulsive noise environment. This paper proposes new robust counterparts for the S-LMS family based on M-estimation. The proposed sequential least mean M-estimate (S-LMM) family of algorithms employ nonlinearity to improve their robustness to impulsive noise. Another contribution of this paper is the presentation of a convergence performance analysis for the S-LMS/S-LMM family for Gaussian inputs and additive Gaussian or contaminated Gaussian noises. The analysis is important for engineers to understand the behaviors of these algorithms and to select appropriate parameters for practical realizations. The theoretical analyses reveal the advantages of input normalization and the M-estimation in combating impulsive noise. Computer simulations on system identification and joint active noise and acoustic echo cancellations in automobiles with double-talk are conducted to verify the theoretical results and the effectiveness of the proposed algorithms. © 2010 IEEE.published_or_final_versio

New sequential partial update switch-mode noise-constrained nlms adaptive filtering algorithms

The sequential partial update LMS (S-LMS)-based algorithms are efficient adaptive filtering algorithms for reducing the high arithmetic complexity in acoustic and related applications. A limitation of the algorithms is the degraded convergence speed. In this paper, a new family of sequential partial update switch-mode noise-constrained NLMS (S-SNCNLMS) algorithms is proposed. These algorithms use a new variable step-size (VSS) method to increase the convergence speed of the traditional partial update algorithms while achieving the same steady-state excess mean square error (EMSE). It employs a maximum step-size to improve the initial convergence and exploits the prior knowledge of the additive noise variance as in the noise-constrained (NC) approach near convergence. The mean and mean square convergence behaviors of these new switch mode algorithms are studied to characterize its convergence condition and steady-state EMSE. Based on the theoretical results, an automatic threshold selection method for mode switching is also developed. Computer simulations are conducted to verify the theoretical results and effectiveness of the proposed algorithms. ©2010 IEEE.published_or_final_versionThe 10th International Symposium on Communications and Information Technologies (ISCIT 2010), Tokyo, Japan, 26-29 October 2010. In Proceedings of 10th ISCIT, 2010, p. 435-44

System identification and speed control of electro- mechanical dual acting pulley continuously variable transmission

Researchers at Universiti Teknologi Malaysia (UTM) has designed, developed and patented an Electro-Mechanical Dual Acting Pulley Continuously Variable Transmission (EMDAP CVT). The newly developed EMDAP CVT is a complex nonlinear system. Since the system is difficult to be modeled, designing the suitable controller for the EMDAP CVT is a challenging task. However, it is possible to obtain model system and transfer function by employing System Identification (SI) technique. By having mathematical representation of the EMDAP CVT in form of transfer function, controller’s analysis and future works relating to the EMDAP CVT will be much easier. The main part of this research is to develop a model which is able to imitate the current EMDAP CVT system behaviours. Therefore, SI was performed to develop the model system and transfer function. Genetic Algorithm (GA) is used as an estimator with Nonlinear ARX (NARX) as a model structure. The mathematical modelling of the EMDAP CVT system is successfully presented and verified in form of 3rd order nonlinear transfer function. The focus of this research work is more on the implementation of speed control for the EMDAP CVT system based on model obtained from the SI. The EMDAP CVT speed controllers are designed for adjusting speed through providing appropriate CVT ratio to the system. The control objective is to achieve a desired output speed, which is used to specify and maintain the desired CVT ratio for the EMDAP CVT system. Proportional-Integral-Derivative (PID) controller is used as the basis and then fined tuned using conventional Ziegler-Nichols and Particle Swarm Optimization (PSO) method. Three controllers which are Proportional-plus-PSO (PPSO), Proportional-Derivative-plus-PSO (PD-PSO) and Proportional-Integral- Derivative-plus-PSO (PID-PSO) were developed to test the reliability of the obtained model system and transfer function. The performance of the designed controllers was demonstrated and validated through simulations and experiments. The error performance of the developed controllers is evaluated in terms of Integral of Absolute Error (IAE), Integral Square of Errors (ISE), Integral of Time multiplied by Absolute Errors (ITAE), and Mean Square Error (MSE). Based on the results, the PIDPSO speed controller gives a sufficient performance, such as settling time, overshooting and error performance. The validation approach resulted in lower than 5% percentage error thus verified the 95% confidence limit of the model system. Further controller’s analysis using Fuzzy Logic (FL) and Neural Network (NN) controllers were performed on the obtained model system and transfer function. The performance of the tested controllers were evaluated in terms of Steady State Error (SSE) and MSE values. All of the tested controllers produced good performance with steady state response within 5 seconds and SSE percentage lower than 5%. The end results show that, NARMA-L2 neural speed controller gives the best performance with SSE percentage of 0.91% and smallest MSE value of 3.28