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Robust PID controller tuning based on the heuristic kalman algorithm

AUTOMATICA 2009年第9期45卷 2099-2106页

作者： Toscano, Rosario Lyonnet, Patrick Univ Lyon ECL ENISE CNRS Lab Tribol & Dynam SystUMR5513 F-42023 St Etienne 2 France

This paper presents a simple but effective tuning strategy for robust PID controllers satisfying multiple H-infinity performance criteria. Finding such a controller is known to be computationally intractable via the conventional techniques. This is mainly due to the non-convexity of the resulting control problem which is of the fixed order/structure type. To solve this kind of control problem easily and directly, without using any complicated mathematical manipulations and without using too many "user defined" parameters, we utilize the heuristic kalman algorithm (HKA) for the resolution of the underlying constrained non-convex optimization problem. The resulting tuning strategy is applicable both to stable and unstable systems, without any limitation concerning the order of the process to be controlled. Various numerical studies are conducted to demonstrate the validity of the proposed tuning procedure. Comparisons with previously published works are also given. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： PID controller H-infinity control Robustness Non-convex optimization problems heuristic kalman algorithm

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A new heuristic approach for non-convex optimization problems

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INFORMATION SCIENCES 2010年第10期180卷 1955-1966页

作者： Toscano, R. Lyonnet, P. Univ Lyon Lab Tribol & Dynam Syst CNRS ECLENISEUMR5513 F-42023 St Etienne 2 France

In this work a new optimization method, called the heuristic kalman algorithm (HKA), is presented. This new algorithm is proposed as an alternative approach for solving continuous, non-convex optimization problems. The principle of HKA is to explicitly consider the optimization problem as a measurement process designed to give an estimate of the optimum. A specific procedure, based on the kalman estimator, was developed to improve the quality of the estimate obtained through the measurement process. The main advantage of HKA, compared to other metaheuristics, lies in the small number of parameters that need to be set by the user. Further, it is shown that HKA converges almost surely to a near-optimal solution. The efficiency of HKA was evaluated in detail using several non-convex test problems, both in the unconstrained and constrained cases. The results were then compared to those obtained via other metaheuristics. The numerical experiments show that HKA is a promising approach for solving non-convex optimization problems, particularly in terms of computation time and success ratio. (C) 2010 Elsevier Inc. All rights reserved.

关键词： heuristic kalman algorithm Non-convex optimization Metaheuristic Objective function Almost sure convergence

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heuristic kalman optimized particle filter for remaining useful life prediction of lithium-ion battery

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MICROELECTRONICS RELIABILITY 2018年 81卷 232-243页

作者： Pham Luu Trung Duong Raghavan, Nagarajan SUTD Engn Prod Dev EPD Pillar Singapore 487372 Singapore

Accurate prediction of the remaining useful life of a faulty component is important to the prognosis and health management of any engineering system. In recent times, the particle filter algorithm and several variants of it have been used as an effective method for this purpose. However, particle filter suffers from sample degeneracy and impoverishment. In this study, we introduce the heuristic kalman algorithm, a metaheuristic optimization approach, in combination with particle filtering to tackle sample degeneracy and impoverishment. Our proposed method is compared with the particle swarm optimized particle filtering technique, another popular meta heuristic approach for improvement of particle filtering. The prediction accuracy and precision of our proposed method is validated using several Lithium ion battery data sets from NASA (R) Ames research center.

关键词： Prognostic health management Particle filtering heuristic kalman algorithm Particle swarm optimization Remaining useful life

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Robust structured controllers for piezoelectric microactuators

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ISA TRANSACTIONS 2014年第6期53卷 1857-1864页

作者： Toscano, Rosario Ivan, Ioan Alexandru Univ Lyon ECL ENISE LTDS UMR CNRS 5513 F-42023 St Etienne 2 France Valahia Univ Targoviste FIE ICSTM Targoviste 130082 Romania

This paper introduces a new control scheme which incorporates the concept of shaping filter together with the use of the v-gap metric and the robust design of a structured controller. The main motivation in doing this is related to the development of efficient control laws for small size actuators. Designing a structured controller is known to be computationally intractable via the traditional Ho. method. This is mainly due to the non-convexity of the resulting control problem which is of fixed order or structure type. To solve this kind of control problem easily and directly, without using any complicated mathematical manipulations and without using too many "user defined" parameters, we utilize the heuristic kalman algorithm (HKA) for the resolution of the underlying constrained non-convex optimization problem. The experimental results validate the proposed technique and demonstrate its convenience for the development of fast and precise positioning systems. (C) 2014 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Piezoelectric actuators Structured controller Shaping filter H-infinity control Uncertain systems Non-convex optimization problems heuristic kalman algorithm

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A Metaheuristic Approach to Remaining Useful Life Estimation of Systems Subject to Multiple Degradation Mechanisms

A Metaheuristic Approach to Remaining Useful Life Estimation...

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IEEE International Conference on Prognostics and Health Management (ICPHM)

作者： Duong, Pham L. T. Raghavan, Nagarajan SUTD EPD Pillar Singapore Singapore

ISBN: (纸本)9781509057108

It is common to assume that there is only one degradation mechanism in the system in recent works on prognostics focusing on estimation of the remaining useful life (RUL) of an electromechanical system. However, there are cases in which the system may be subjected to more than one failure (degradation) mechanisms due to different stress factors, types of components and their interactions with one another. Recently, we proposed an approach for estimation of RUL of the system with multiple failure mechanisms using the particle filter algorithm and Akaike Information Criteria (AIC). However, it is well known that standard particle filter suffers from sample degeneracy and impoverishment. In this study, we introduce the heuristic kalman algorithm (HKA), a metaheuristic optimization approach, in combination with particle filtering to tackle sample degeneracy and impoverishment issues and use it for improved prediction /estimation the RUL distribution of any system with multiple failure (degradation) mechanisms.

关键词： heuristic kalman algorithm Impoverishment Multiple degradation mechanisms Particle filter Remaining useful life (RUL) Sample degeneracy

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