Evolutionary algorithms in control systems engineering: a survey

Mobile robots are increasingly used in automated industrial environments. There are also other applications like planet exploration, surveillance, landmine detection, etc. In all these applications, in order that the mobile robots perform their tasks, collision-free path planning is a prerequisite. This article provides an overview of the research progress in path planning of a mobile robot for off-line as well as on-line environments. Commonly used classic and evolutionary approaches of path planning of mobile robots have been addressed. Review shows that evolutionary optimization algorithms are computationally efficient and hence are increasingly being used in tandem with classic approaches while handling Non-deterministic Polynomial time hard (NP-hard) problems. Also, challenges involved in developing a computationally efficient path planning algorithm are addressed.

	 

	Key words: Path planning, mobile robot, off-line environment, on-line environment, classic, evolutionary algorithms.

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Optimal path planning of mobile robots: A review

The identification of non-linear systems using only observed finite datasets has become a mature research area over the last two decades. A class of linear-in-the-parameter models with universal approximation capabilities have been intensively studied and widely used due to the availability of many linear-learning algorithms and their inherent convergence conditions. This article presents a systematic overview of basic research on model selection approaches for linear-in-the-parameter models. One of the fundamental problems in non-linear system identification is to find the minimal model with the best model generalisation performance from observational data only. The important concepts in achieving good model generalisation used in various non-linear system-identification algorithms are first reviewed, including Bayesian parameter regularisation and models selective criteria based on the cross validation and experimental design. A significant advance in machine learning has been the development of the support vector machine as a means for identifying kernel models based on the structural risk minimisation principle. The developments on the convex optimisation-based model construction algorithms including the support vector regression algorithms are outlined. Input selection algorithms and on-line system identification algorithms are also included in this review. Finally, some industrial applications of non-linear models are discussed.

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Model selection approaches for non-linear system identification: a review

Damage detection in vibrating bodies using genetic algorithms

Modified genetic algorithm strategy for structural identification

In this paper, a genetic-algorithm-based non-linear autoregressive with exogenous inputs system identification (GANARXSI) algorithm was developed to identify non-linear systems and was successfully applied to both non-linear continuous-time and discrete-time systems with acceptable accuracy. This algorithm can achieve robustness and efficiency in identifying complex non-linear systems. The effects of different combinations of operators of genetic algorithms are also studied and an operator of truncate mutation was proposed to improve greatly the convergence rate in identification process. An identified non-linear coupled liquid-level system is used to evaluate the performance of the algorithm. An experimental verification is carried out in a dynamometer engine test cell and the identified engine model during the I/M240 driving cycle is also used to validate the proposed methodology. After testing the algorithm for different complex non-linear systems, the GANARXSI algorithm proved to be a practica...

Non-linear system identification using genetic algorithms

This paper proposes a potential field immune network (PFIN) for dynamic motion planning of mobile robots in an unknown environment with moving obstacles and a fixed/moving goal. The velocity obstacle method is utilized to determine the imminent collision obstacle. Subsequently, PFIN is implemented to guide robots to avoid collision with the most dangerous objects at every time instant. The overall response of the immune network is calculated with the aid of fuzzy logic and genetic algorithms. Simulation results are presented to verify the effectiveness of the proposed architecture in dynamic environments with single and multiple fixed/moving obstacles.

Motion planning for mobile robots in dynamic environments using a potential field immune network

The inverse dynamics approach has been widely utilized in the control problem of various practical non-linear systems in recent years. This paper demonstrates a feedforward-feedback control...

Inversion control of non-linear systems with an inverse NARX model identified using genetic algorithms:

In this paper, a novel non-linear system identification methodology is developed employing the features of the artificial immune system. A simplified incremental approach integrated with the maximum entropy principle and an instantaneous feedback mechanism is proposed to reorganize the system's parameters simultaneously. To verify and demonstrate the effectiveness of the proposed algorithm, a simulation example on a two-link robot was studied. This algorithm can achieve robustness and efficiency in identifying complex non-linear systems. The simulation results show that the identified immune models are robust to noise and various uncertainties in the robot dynamics.

Non-linear system identification using an artificial immune system

In this paper, a novel approach to model-based fault detection for non-linear systems is presented. An immune model of the system is used for the generation of residual. The orthogonal least-squares method is implemented to select the significant receptor vectors of the immune model. After the model identification, the filtered residual scheme and the fault alarm concentration are applied for the fault detection. To verify and demonstrate the performance of the proposed methodology, a simulation example on a two-link robot was studied. The results show the effectiveness and robustness in both system identification and fault detection.

G-C Luh

Papers

Non-linear system identification using genetic algorithms

Motion planning for mobile robots in dynamic environments using a potential field immune network

Inversion control of non-linear systems with an inverse NARX model identified using genetic algorithms:

Non-linear system identification using an artificial immune system

Identification of immune models for fault detection