Robert Turney, Ph.D.

Associate Professor | Milwaukee School of Engineering

Milwaukee, WI, UNITED STATES

Dr. Robert Turney is an expert in the areas of digital image and video processing, real-time embedded systems and products.

Education, Licensure and Certification (3)

M.S.: Electrical Engineering, University of Wisconsin-Milwaukee 1992

Ph.D.: Electrical Engineering, University of Wisconsin-Milwaukee 2005

B.S.: Electrical Engineering, University of Wisconsin-Milwaukee 1989

Biography

Dr. Robert Turney is an associate professor in the Electrical, Computer and Biomedical Engineering Department at MSOE. He is an engineering fellow and team lead for the Advanced Development Group at Johnson Controls, where he previously was a lead staff engineer.

Areas of Expertise (5)

Hardware Architecture

Electrical Engineering

Algorithms

Digital Signal Processing

Engineering Education

Accomplishments (4)

Johnson Controls Merit Award (professional)

2018 Model Based Design for VRF Systems

Inventor of the year (professional)

2017 Johnson Controls

Johnson Controls Chairman’s Award (professional)

2016 Stanford Energy Facility, EOS optimization system

Best of the Best (professional)

2016 Engineering News Record, Stanford Central Energy facility

Affiliations (2)

Institute of Electrical and Electronics Engineers (IEEE) : Senior Member
UWM Industrial Liaison Board : Member

Patents (5)

Systems and methods for rapid disturbance detection and response

US9568204B2

2017 A method for detecting and responding to disturbances in a HVAC system using a noisy measurement signal and a signal filter is provided. The method includes detecting a deviation in the noisy measurement signal, resetting the filter in response to a detected deviation exceeding a noise threshold, filtering the noisy measurement signal using the signal filter to determine an estimated state value, and determining that a disturbance has occurred in response to the estimated state value crossing a disturbance threshold.

Systems and methods for cascaded model predictive control

US9852481B1

2017 Methods and systems to minimize energy cost in response to time-varying energy prices are presented for a variety of different pricing scenarios. A cascaded model predictive control system is disclosed comprising an inner controller and an outer controller. The inner controller controls power use using a derivative of a temperature setpoint and the outer controller controls temperature via a power setpoint or power deferral.

Systems and methods for energy cost optimization in a building system

US9436179B1

2016 Methods and systems to minimize energy cost in response to time-varying energy prices are presented for a variety of different pricing scenarios. A cascaded model predictive control system is disclosed comprising an inner controller and an outer controller. The inner controller controls power use using a derivative of a temperature setpoint and the outer controller controls temperature via a power setpoint or power deferral.

System identification and model development

US9235657B1

2016 Methods for system identification are presented using model predictive control to frame a gray-box parameterized state space model. System parameters are identified using an optimization procedure to minimize a first error cost function within a range of filtered training data. Disturbances are accounted for using an implicit integrator within the system model, as well as a parameterized Kalman gain.

Low level central plant optimization

US10101731B2

2015 Systems and methods for low level central plant optimization are provided. A controller for the central plant uses binary optimization to determine one or more feasible on/off configurations for equipment of the central plant that satisfy operating constraints and meet a thermal energy load setpoint.

Selected Publications (5)

A mixed-integer linear programming model for real-time cost optimization of building heating, ventilation, and air conditioning equipment

Energy and Buildings

2017 In this paper, we present a framework for the formulation and solution of mixed-integer linear programming (MILP) models for operational planning of HVAC systems in commercial buildings. We introduce the general concepts of generators (e.g., chillers, boilers, cooling towers) and resources (e.g., electricity, chilled water), which allow us to model a wide range of central plants.

Distributed economic model predictive control for large-scale building temperature regulation

IEEE

2016 Although recent research has suggested model predictive control as a promising solution for minimizing energy costs of commercial buildings, advanced control systems have not been widely deployed in practice. Large-scale implementations, including industrial complexes and university campuses, may contain thousands of air handler regions each with tens of zones.

Closed-Loop Scheduling for Cost Minimization in HVAC Central Plants

International High Performance Buildings Conference

2016 In this paper, we examine closed-loop operation of an HVAC central plant to demonstrate that closed-loop receding-horizon scheduling provides robustness to inaccurate forecasts, and that economic performance is not seriously impaired by shortened prediction horizons or inaccurate forecasts when feedback is employed.

System Identification for Model Predictive Control of Building Region Temperature

International High Performance Buildings Conference

2016 Model predictive control (MPC) is a promising technology for energy cost optimization of buildings because it provides a natural framework for optimally controlling such systems by computing control actions that minimize the energy cost while meeting constraints.

Cost optimization of combined building heating/cooling equipment via mixed-integer linear programming

IEEE

2015 In this paper, we propose a mixed-integer linear program to economically optimize equipment usage in a central heating/cooling plant subject to time-of-use and demand charges for utilities. The optimization makes both discrete on/off and continuous load decisions for equipment while determining utilization of thermal energy storage systems.