Abstract
This paper proposes a scheme for online identification and indirect adaptive control of dynamical systems based on intelligent radial basis function network (RBFN). The need to use intelligent control techniques arises as the conventional control methods like PID fails to perform when there is a non linearity in the system or system is affected by parameter variations and disturbance signals. In order to show the effectiveness of the proposed scheme, the mathematical models of the dynamical systems considered in this paper were assumed to be unknown. Since most real-world systems are highly complex and their precise mathematical descriptions are not available which further makes their control more difficult. These factors laid the foundation for the development of control schemes based on intelligent tools so that such systems can be controlled. One such scheme, based on RBFN, is presented in this paper. The key part of the scheme is the selection of inputs for the controller and in the proposed scheme; the inputs to the controller were taken to be the past values of plant’s as well as of the controller’s outputs along with the externally applied input. A separate RBFN identification model was also setup to operate in parallel with the controller and plant. Simulation study was performed on two dynamical systems and the results obtained show that the proposed scheme was able to provide the satisfactory online control and identification under the effects of both parameter variations and disturbance signals.
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Kumar, R., Srivastava, S., Gupta, J.R.P. (2016). Soft Computing Technique Based Online Identification and Control of Dynamical Systems. In: Corchado Rodriguez, J., Mitra, S., Thampi, S., El-Alfy, ES. (eds) Intelligent Systems Technologies and Applications 2016. ISTA 2016. Advances in Intelligent Systems and Computing, vol 530. Springer, Cham. https://doi.org/10.1007/978-3-319-47952-1_38
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DOI: https://doi.org/10.1007/978-3-319-47952-1_38
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