Wide main

The WIDE project will face the following key issues:

• Develop a unified, distributed, and multilayer modelling and goals/constraints specification framework that ensures cross-layer and inter-layer compatibility, responsiveness to structural changes in the process, consistency with measured data.
• Develop new techniques for designing and coordinating a network of MPCs to achieve the best performance of the system and robustness under uncertainty and possible physical and topological constraints.
• Develop methods of cooperating WSN and advanced process control by developing transmission and networking technologies for reliable wide-area wireless sensor networks, and new methods of MPC design that, aware of communication and power-consumption aspects of the network, ensure an optimized controller/wireless-sensor operation.
• Demonstrate experimentally the viability and efficiency of the general approach on the water distribution network of the city of Barcelona.

The WIDE toolbox is assembled basing on Decentralized and Hierarchical Model Predictive Control (DHMPC), Large Scale Model Management (LSMM) and Networked Control Systems (NCS), toolboxes, developed by UNISI/UNITN, Honeywell and TU/e, respectively.

Table of contents:

• Decentralized and Hierarchical Model Predictive Control : DHMPC aim is to provide facilities for distributed model predictive control, which include hierarchical MPC, along with lower level linear regulator synthesis, and network aware MPC. In particular, energy aware is treated in simulation and facilities for real experiments are provided by a Matlab to wireless sensors network interface. The DHMPC components are listed below:
  • TrueTime Code Genration for quick setup of Wireless Sensor Feedback Control Loop : The class Automatic Code Generation (ACG) creates a ready-to-use setup networked control system simulation by generating a Simulink model based on TrueTime blocks and corresponding configuration m-files. The Network model is specified by the number of actuators and sensors, while controller and plant should be customized by the user fulfilling the respective subsystem.
  • Decentralized Linear Regulator Synthesis Class : The purpose of the DLR is to synthesize a decentralized feedback gain that is capable of robustly stabilize a linear plant while enforcing a set of constraint on input and state variables under the effect of unknown but bounded disturbances. The synthesis is carried out by solving a semi-definite programming problem, SDP, which can efficiently be solved by means of linear matrix inequalities, LMI.
  • Decentralized Model Predictive Control Class : The class Decentralized LINear CONstrained (dlincon) is an extension of the object lincon from Hybrid toolbox (by A. Bemporad) which allows to easily handle a set of decentralized linear MPC controllers. Moreover the a-posteriori stability test described in "Barcelli and Bemporad Decentralized Model Predictive Control of Dynamically-Coupled Linear Systems: Tracking under Packet Loss". Below we briefly recall the basic concepts of that contribution so as to describe the trems of applicability.
  • Energy Aware Model Predictive Control : This class provides an implementation of an explicit MPC controller, where communications between controller and sensor nodes are subject to an energy-aware policy intended to lower the number of transmissions and, utlimately, save sensor nodes battery.
  • Decentralized and Hierarchical Model Predictive Control Class Assuming the plant stabilized by a linear lower level regulator, (e.g. obtained by means of DLRC) unable to enforce linear constraints on state and reference, this class computes restrictions on the reference that guarantees constraint satisfaction. Thus the user provided reference should be filtered so as to restrict both its magnitude and rate of variation, effectively imposing constraints on it. Then the class uses MPC, which is a constraint handling control scheme, for the supervisor layer (being it a single controller or a set of decentralized) and guarantee state constraint enforcement.
  • ESenza to Matlab Communication External devices communication class. Permit to connect the Matlab environment to a real implantation so as to perform closed loop control.
• Large Scale Model Management
• Networked Control Systems

For further information and toolbox download please visit the Official website: http://www.wide.dii.unisi.it/