Inferential networked control with accessibility constraints in both the sensor and actuator channels

Abstract

The predictor and controller design for an inferential control scheme over a network is addressed. A linear plant with disturbances and measurement noise is assumed to be controlled by a controller that communicates with the sensors and the actuators through a constrained network. An algorithm is proposed such that the scarce available outputs are used to make a prediction of the system evolution with an observer that takes into account the amount of lost data between successful measurements transmissions. The state prediction is then used to calculate the control actions sent to the actuator. The possibility of control action drop due to network constraints is taken into account. This networked control scheme is analysed and both the predictor and controller designs are addressed taking into account the disturbances, the measurement noise, the scarce availability of output samples and the scarce capability of control actions update. The time-varying sampling periods that result for the process inputs and outputs due to network constraints have been determined as a function of the probability of successful transmission on a specified time with a Bernoulli distribution. For both designs performance has been established and linear matrix inequality (LMI) design techniques have been used to achieve a numerical solution.

Keywords:

• networked control
• inferential control
• networked estimation
• linear matrix inequalities

Acknowledgements

This work was supported by MICINN project number DPI2011-27845-C02-02.

Notes

The dependence on N_k would have been possible if a Markov chain were considered, as in the recent works (Yu et al. 2009; Shi and Yu 2011; Wang et al. 2011). This idea will be developed in future work.

Additional information

Notes on contributors

I. Peñarrocha

Ignacio Peñarrocha was born in Castelló, Spain in 1978. He received the MSc degree in Industrial Engineering from the Universitat Jaume I de Castelló (UJI), Spain, in 2002, and his PhD in Computering and Control Engineering from Universitat Politècnica de València (UPV), Spain, in 2006. He has been working since 2004 at the Universitat Jaume I de Castelló. His current position is as Associate Professor at the Department of Industrial Systems Engineering and Design. He has participated in several local and national research projects. His research interests include identification, estimation and control on networked control systems, and semi-definite programming applied to control system design.

D. Dolz

Daniel Dolz was born in Castelló, Spain in 1988. He received the MSc degree in Industrial Engineering from the Universitat Jaume I de Castelló (UJI) and the MSc degree in Automatic and Electronic Engineering from Institut National des Sciences Appliquées de Toulose (INSA) in 2011. He received the MSc degree in Computational Mathematics from the Universitat Jaume I de Castelló (UJI) in 2012. Currently he is with the Department of Industrial Systems Engineering and Design at the Universitat Jaume I de Castelló, Spaing, pursuing a PhD degree with a pre-doctoral grant from Universitat Jaume I. His current research interests include robust stability, time-delay systems, networked control systems and wind turbine control.

R. Sanchis

Roberto Sanchis was born in Genovés, Valencia, Spain in 1968. He received the MSc degree in Electrical Engineering in 1993 and his PhD in Control Engineering in 1999 from the Polytechnic University of Valencia (UPV), Spain. He was awarded the first national prize for university graduation in 1993. During 1994 and 1995, he was a teaching assistant at the Systems and Control Engineering Department of the UPV. He has been working since 1996 at the University Jaume I of Castellón, Spain. His current position is as Associate Professor (tenure lecturer) at the Department of Industrial Systems Engineering and Design, where he is the leader of the Control Systems and Automation group. His research interests include missing-data estimation, identification and control, tuning and auto-tuning of PID controllers and applications (ceramic industry and waste water treatment).