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Khan Q.,COMSATS Institute of Information Technology | Bhatti A.I.,Mohammad Ali Jinnah University | Akmeliawati R.,Intelligent Mechatronics System Research Units
2015 10th Asian Control Conference: Emerging Control Techniques for a Sustainable World, ASCC 2015 | Year: 2015

This paper presents a fast terminal sliding mode control strategy for a class of underactuated systems. Strategically, this development encompasses those electro mechanical underactuated systems which can be transformed into the so-called regular form. The novelty of this article lies in the hierarchical development of a fast terminal sliding manifold design for the considered class. Having established sliding mode against the designed manifold, the close loop dynamics becomes finite time stable which results in high precession. In addition, the adverse effects of chattering phenomenon are eliminated and the robustness of the system against uncertainties is confirmed theoretically in a couple of theorems. A comprehensive numerical example of the cart pendulum is presented to verify the claims for the considered class. © 2015 IEEE.

Tijani I.B.,Intelligent Mechatronics System Research Units | Akmeliawati R.,Intelligent Mechatronics System Research Units | Legowo A.,Intelligent Mechatronics System Research Units | Budiyono A.,Konkuk University | Muthalif A.G.A.,Intelligent Mechatronics System Research Units
Aircraft Engineering and Aerospace Technology | Year: 2014

Purpose - The purpose of this paper is to develop a hybrid algorithm using differential evolution (DE) and prediction error modeling (PEM) for identification of small-scale autonomous helicopter state-space model. Design/methodology/approach - In this study, flight data were collected and analyzed; MATLAB-based system identification algorithm was developed using DE and PEM; parameterized state-space model parameters were estimated using the developed algorithm and model dynamic analysis. Findings - The proposed hybrid algorithm improves the performance of the PEM algorithm in the identification of an autonomous helicopter model. It gives better results when compared with conventional PEM algorithm inside MATLAB toolboxes. Research limitations/implications - This study is applicable to only linearized state-space model. Practical implications - The identification algorithm is expected to facilitate the required model development for model-based control design for autonomous helicopter development. Originality/value - This study presents a novel hybrid algorithm for system identification of an autonomous helicopter model. © 2014 Emerald Group Publishing Limited.

Tijani I.B.,Intelligent Mechatronics System Research Units | Akmeliawati R.,Intelligent Mechatronics System Research Units | Legowo A.,Intelligent Mechatronics System Research Units | Budiyono A.,Konkuk University
Engineering Applications of Artificial Intelligence | Year: 2014

The need for a high fidelity model for design, analysis and implementation of an unmanned helicopter system (UHS) in various emerging civil applications cannot be underestimated. However, going by a first principle approach based on physical laws governing the dynamics of the system, this task is noted to be highly challenging due to the complex nonlinear characteristics of the helicopter system. On the other hand, the problem of determining network architecture for optimal/sub-optimal performances has been one of the major challenges in the use of the nonparametric approach based on Nonlinear AutoRegressive with eXogenous inputs Network (NARX-network). The performance of the NARX network in terms of complexity and accuracy is largely dependent on the network architecture. The current approach in the literature has been largely based on trial and error, while most of the reported optimization approaches have limited the domain of the problem to a single objective problem. This study proposes a hybrid of conventional back propagation training algorithm for the NARX network and multiobjective differential evolution (MODE) algorithm for identification of a nonlinear model of an unmanned small scale helicopter from experimental flight data. The proposed hybrid algorithm was able to produce models with Pareto-optimal compromise between the design objectives. The performance of the proposed optimized model is benchmarked with one of the previously reported architectures for a similar system. The optimized model outperformed the previous model architecture with up to 55% performance improvement. Apart from the effectiveness of the optimized model, the proposed design algorithm is expected to facilitate timely development of the nonparametric model of the helicopter system. © 2014 Elsevier Ltd.

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