@article{(Open Science Index):https://publications.waset.org/pdf/10006341,
	  title     = {Stochastic Model Predictive Control for Linear Discrete-Time Systems with Random Dither Quantization},
	  author    = {Tomoaki Hashimoto},
	  country	= {},
	  institution	= {},
	  abstract     = {Recently, feedback control systems using random dither
quantizers have been proposed for linear discrete-time systems.
However, the constraints imposed on state and control variables
have not yet been taken into account for the design of feedback
control systems with random dither quantization. Model predictive
control is a kind of optimal feedback control in which control
performance over a finite future is optimized with a performance
index that has a moving initial and terminal time. An important
advantage of model predictive control is its ability to handle
constraints imposed on state and control variables. Based on the
model predictive control approach, the objective of this paper is to
present a control method that satisfies probabilistic state constraints
for linear discrete-time feedback control systems with random dither
quantization. In other words, this paper provides a method for
solving the optimal control problems subject to probabilistic state
constraints for linear discrete-time feedback control systems with
random dither quantization.},
	    journal   = {International Journal of Electrical and Information Engineering},
	  volume    = {11},
	  number    = {2},
	  year      = {2017},
	  pages     = {182 - 186},
	  ee        = {https://publications.waset.org/pdf/10006341},
	  url   	= {https://publications.waset.org/vol/122},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 122, 2017},
	}