Abstract
Complete state vector information is necessary for implementing the state feedback control via algebraic Riccati equation (ARE). However, all the states are usually not available for feedback because it is often expensive and impractical to include a sensor for each variable. Hence, to estimate the unmeasured variables, a state estimation technique is formulated to estimate all the states of the process. One of the major problems of closed-loop optimal control design is the choice of weighted matrices, which will result in optimal response. The conventional approach involves trial-and-error method to choose the weighted matrices in the cost function to determine the state feedback gain. Some of the drawbacks of this method are as follows: it is tedious, time-consuming, optimal response is not obtained, and manual selection of weighting matrices is also not straightforward. To overcome the above shortcomings, swarm intelligence is used to obtain the optimal weights, which provide superior performance than the conventional trial-and-error approach. The proposed approach performance is assessed by weight selection using PSO, which is compared with manual tuning that satisfies the closed-loop stability criteria. Further, the proposed controller performance is evaluated not only for stabilizing the disturbance rejection, but also for tracking the given reference temperature in a continuous stirred tank reactor (CSTR).
Keywords
This is a preview of subscription content, log in via an institution.
References
Omer, O., Levent, C., Erol, U.: A novel method of selection of Q and R matrices in the theory of optimal control. Int. J. Syst. Control 1(2), 84–92 (2010)
Fleming, P., Purshouse, R.: Evolutionary algorithms in control systems engineering: a survey. Control Eng. Pract. 10(11), 1223–1241 (2002)
Sanchez, G., Villasana, M., Strefezza, M.: Multi-objective Pole Placement with Evolutionary Algorithms. Lecture Notes in Computer Science, vol. 44, pp. 417–425 (2007)
Gaing, Z.L.: A particle swarm optimization approach for optimum design of PID controller in AVR system. IEEE Trans. Energy Convers. 19(2), 384–391 (2004)
Onnen, C., Babuska, R., Kaymak, U., Sousa, J.M., Verbruggen, H.B., Isermann, R.: Genetic algorithms for optimization in predictive control. Control Eng. Pract. 5(10), 1363–1372 (1997)
Tsai, S.J., Huo, C.L., Yang, Y.K., Sun, T.Y.: Variable feedback gain control design based on particle swarm optimizer for automatic fighter tracking problems. Appl. Soft Comput. 13(1), 58–75 (2013)
Solihin, M.I., Akmeliawati, R.: Particle swam optimization for stabilizing controller of a self-erecting linear inverted pendulum. Int. J. Electr. Electron. Syst. Res. 3, 410–415 (2010)
Geetha, M., Jerome, J., Arun Kumar, P.: Critical evaluation of non-linear filter configurations for the state estimation of continuous stirred tank reactor. Appl. Soft Comput. 25, 452–460 (2014)
Naidu, D.S.: Optimal Control Systems. CRC Press, Florida (2003)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Geetha Mani, Sivaraman, N., Sanjeevikumar, P. (2018). Particle Swarm Optimization-Based Closed-Loop Optimal State Feedback Control for CSTR. In: Konkani, A., Bera, R., Paul, S. (eds) Advances in Systems, Control and Automation. Lecture Notes in Electrical Engineering, vol 442. Springer, Singapore. https://doi.org/10.1007/978-981-10-4762-6_45
Download citation
DOI: https://doi.org/10.1007/978-981-10-4762-6_45
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-4761-9
Online ISBN: 978-981-10-4762-6
eBook Packages: EngineeringEngineering (R0)