{"title":"Numerical Simulations on Feasibility of Stochastic Model Predictive Control for Linear Discrete-Time Systems with Random Dither Quantization","authors":"Taiki Baba, Tomoaki Hashimoto","volume":129,"journal":"International Journal of Electrical and Information Engineering","pagesStart":1029,"pagesEnd":1034,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/10007864","abstract":"The random dither quantization method enables us\r\nto achieve much better performance than the simple uniform\r\nquantization method for the design of quantized control systems.\r\nMotivated by this fact, the stochastic model predictive control\r\nmethod in which a performance index is minimized subject to\r\nprobabilistic constraints imposed on the state variables of systems\r\nhas been proposed for linear feedback control systems with random\r\ndither quantization. In other words, a method for solving optimal\r\ncontrol problems subject to probabilistic state constraints for linear\r\ndiscrete-time control systems with random dither quantization has\r\nbeen already established. To our best knowledge, however, the\r\nfeasibility of such a kind of optimal control problems has not\r\nyet been studied. Our objective in this paper is to investigate the\r\nfeasibility of stochastic model predictive control problems for linear\r\ndiscrete-time control systems with random dither quantization. To\r\nthis end, we provide the results of numerical simulations that verify\r\nthe feasibility of stochastic model predictive control problems for\r\nlinear discrete-time control systems with random dither quantization.","references":"[1] R. Morita, S. Azuma, T. Sugie, Performance Analysis of Random Dither\r\nQuantizers in Feedback Control Systems, SICE Journal of Control,\r\nMeasurement, and System Integration, Vol. 6, No. 1, pp. 21-27, 2013.\r\n[2] T. 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