Gongsheng Huang

Robust MPC for temperature control of air-conditioning systems concerning on constraints and multitype uncertainties

This paper presents a robust model-based predictive control (MPC) strategy for temperature control of an air-conditioning system, which consists of multiple local-loop processes and each process su...This paper presents a robust model-based predictive control (MPC) strategy for temperature control of an air-conditioning system, which consists of multiple local-loop processes and each process suffers from different dynamics uncertainties or variations. When an appropriate sampling period is chosen to discretise the system for computer control, a state-space discrete model with an uncertainty polytope is developed to describe the mixing uncertainties of these type of systems. The main benefit of the proposed description is that robust model predictive control can be easily used to design a robust controller for such a system while taking account of constraints associated with this system. A linear matrix inequality-based MPC algorithm is employed for control design. Case study was conducted on a dynamic simulation platform of an air-conditioning system, which evaluated the developed strategy in various simulation tests by comparing with the conventional PID control. Results demonstrated that the developed strategy is able to deal with constraints and allows stable and robust control while maintaining acceptable thermal comfort. Although the strategy is illustrated and validated using a constant air volume air-conditioning system, it can be applied to other constraint HVAC processes suffering from similar uncertainties. Practical applications: The main benefit of the developed strategy (including the proposed description and the adopted robust control algorithm) for practical application is that uncertainties and constraints can be dealt with simultaneously in one framework. Constraints in HVAC systems exist due to the application of actuators, for example, the rate limit considered in the paper. Taking account of constraints is useful to prevent unnecessary damages to equipments andmaintain the system operating in a safe mode. Most importantly, the control objectives can be achieved in a constraint manner. The consideration of uncertainties, probably due to the changes of operating environment, is useful to release the work of accurate modelling of HVAC processes and online tuning of controllers.

Modelling and optimisation for sustainable development policy assessment

AbstractEarlier work on sustainable development devised a policy assessment tool that was based on a static optimisationformulation. Key ingredients in sustainable development problems are the presence of random effects and the conflictbetween different objectives. To accommodate these, the earlier formulation was strongly stochastic and was posed in amulti-objective framework. The purpose of this paper is to consider the extension of the work to a formulation thatdeploys dynamic optimisation. In particular it is the aim here to use simulations based on a large scale model to derivedynamic rather than static representations, to integrate these into the optimisation scheme and to assess the benefits. 2004 Elsevier B.V. All rights reserved. Keywords: Sustainable development; Modelling; Stochastic systems; Optimisation 1. IntroductionSustainable development is progressively becoming an issue of universal concern and of paramountimportance in many aspects of human endeavour. It is therefore not surprising to see an exponential growthin the amount of research effort, yet the unpredictable nature of the problem has meant that most of thisresearch tends to be qualitative and often obtuse. It is of course possible to cast the problem in a proba-bilistic setting and then perform an optimisation of expected values, but that can often lead to meaninglessresults given the strongly stochastic nature of the problem. Optimisation is used in this context to obtain the‘‘best’’ strategy, yet given the many conflicting objectives and political priorities, often the concept of ‘‘best’’is itself meaningless.Recent work [1,2] proposed a formulation which overcame these problems by considering an optimi-sation that retained both the stochastic and multi-objective nature of policy making/assessment for sus-tainable development. The key to this approach was a static model describing the cumulative effects, at theend of a forecast horizon, of a single adjustment in the values of ‘‘actions’’ on ‘‘performance indicators’’.

Robust Model Predictive Control of VAV Air-Handling Units Concerning Uncertainties and Constraints

This paper presents a new strategy for robust temperature control of variable-air-volume (VAV) air-handling units (AHUs) that can deal with dynamic variations and the associated constraints in a straightforward manner. The dynamics of VAV AHUs is described by a first-order-plus-time-delay model, and the dynamic variations of the process gain, time constant, and time delay are described using uncertainty sets. These uncertainty sets are converted into an uncertainty polytope, and then an offline robust model predictive control (MPC) algorithm is employed for robust control design. The design procedure is illustrated step by step. An operating mode identification scheme is also developed based on the operating characteristics of VAV AHUs in order to reduce the size of uncertainty sets and, hence, to improve the control performance. Case studies are performed on a simulated VAV AHU. Results are presented to show that the proposed control strategy is able to enhance robustness without much user intervention, reduce the control activities, and satisfy constraints when implemented in the temperature control of VAV AHUs.

Use of uncertainty polytope to describe constraint processes with uncertain time-delay for robust model predictive control applications

This paper studies the application of robust model predictive control (MPC) in a constraint process suffering from time-delay uncertainty. The process is described using a transfer function and sampled into a discrete model for computer control design. A polytope is firstly developed to describe the uncertain discrete model due to the processs time-delay uncertainty. Based on the proposed description, a linear matrix inequality (LMI) based MPC algorithm is employed and modified to design a robust controller for such a constraint process. In case studies, the effect of time-delay uncertainty on the control performance of a standard MPC algorithm is investigated, and the proposed description and the modified control algorithm are validated in the temperature control of a typical air-handling unit.

Enhancing the reliability of chiller control using fused measurement of building cooling load

This paper presents a general framework of utilizing a fused measurement of the building instantaneous cooling load to improve the reliability of the chiller sequencing control in building automation systems. The fused measurement is obtained by combining the complementary advantages of two different approaches to measuring the building cooling load. One approach is the direct measurement, which calculates the building cooling load directly, using the differential water temperature and water flow rate measurements. The other is the indirect measurement, which calculates building cooling load based on chiller models using the instantaneous chiller electrical power input, etc. The combination strategy is tested using the field data collected from the central plant of the air-conditioning system in a high-rise building in Hong Kong. The confidence degree associated with the fused measurement is systematically evaluated. Periodic update of the fusion algorithm parameters is also developed to improve the performance of the fusion strategy and the chiller sequencing control.

Model-based robust temperature control for VAV air-conditioning system

VAV air-conditioning systems are widely used in modern buildings and cascaded PIDcontrollers, including an inner loop and an outer loop, are usually equipped to control zone temperature to track a set point for thermal comfort. Since the outer-loop PID controller with fixed parameters lacks robustness to deal with cooling load variations, this paper proposes a model-based robust temperature control strategy as an alternative and addresses its year-round application. The proposed strategy has three control modes, each of which is used to realize different control objective. The integration of the three modes is developed to deal with directly the bilinear relationship between the zone temperature and the supply air flow rate, the constraint on the operation range of the supply air flow rate and the year-round variations of the zone cooling load. Case studies are performed to show that without online tuning of its control parameters, the triple-mode control can track the set point well and achieve a good robustness under different load conditions.

Model-based optimal start control strategy for multi-chiller plants in commercial buildings

Chiller start control is to schedule chillers start-up operation, including how many chillers to be put into operation and when to start these chillers, for cooling down building internal temperature to a desired level before occupying. Chiller optimal start aims to find the most efficient way to schedule the chiller start operation for energy savings. Previous studies on chiller optimal start mainly focus on determining the pre-cooling lead time based only on initial outdoor and indoor temperatures without considering the impact of the recovery ability, i.e. the ability of the chillers to return the indoor air temperature to its set-point value. Different from previous studies, a new optimal start strategy is proposed in this article, which considers both the recovery ability and the cooling load condition as the optimizing variables. The new strategy is model-based and realized in two steps. The first step is to predict the building cooling load using a simplified building model, and identify a feasible set for the operating chiller number. The second step is to estimate the pre-cooling lead time using the simplified building model for each number inside the feasible range identified in the first step, and calculate the corresponding energy consumption. The number and its corresponding pre-cooling lead time which yields the least energy consumption constitute the optimal start operation. Practical application: The proposed strategy, validated through case studies, can be used in practical applications to select the optimal number of operating chillers and determine the associated pre-cooling lead time in order to achieve minimum energy consumption in the pre-cooling period.Chiller start control is to schedule chillers start-up operation, including how many chillers to be put into operation and when to start these chillers, for cooling down building internal temperature to a desired level before occupying. Chiller optimal start aims to find the most efficient way to schedule the chiller start operation for energy savings. Previous studies on chiller optimal start mainly focus on determining the pre-cooling lead time based only on initial outdoor and indoor temperatures without considering the impact of the recovery ability, i.e. the ability of the chillers to return the indoor air temperature to its set-point value. Different from previous studies, a new optimal start strategy is proposed in this article, which considers both the recovery ability and the cooling load condition as the optimizing variables. The new strategy is model-based and realized in two steps. The first step is to predict the building cooling load using a simplified building model, and identify a feasible...

Two-loop robust model predictive control for the temperature control of air-handling units

This paper presents a temperature control method for air-handling units (AHUs) using a two-loop robust model predictive control (MPC). A two-loop robust MPC employs an inner-loop controller to ensure robust stability and an outer-loop controller to improve the control performance by taking uncertainties and constraints into account. A two-loop robust MPC with multiple overlapping operating modes is developed to deal with the control of systems associated with high nonlinearities. The basic idea is to separate the operating range of these systems into several operating modes such that the local systems in each operating mode suffer less from uncertainties. The overlapping operating modes are adopted in the control scheme to avoid the controller frequently switching among these operating modes. The control scheme is used to design a robust controller for a typical AHU. Simulation results are presented and compared with that of an anti-windup proportional-integral (PI) controller.

Investigation of the ageing effect on chiller plant maximum cooling capacity using Bayesian Markov Chain Monte Carlo method

Ageing inevitably leads to capacity degradation in a chiller plant. Hence in the life-cycle performance analysis of a chiller plant, ageing always represents a crucial consideration for designers. Ageing is normally quantified using maintenance factor. A conventional analysis recommends that the maintenance factor should be 0.01 for systems that undergo annual professional maintenance, and 0.02 for those that are seldom maintained. However, this recommendation is mainly based on a rule of thumb, and may not be accurate enough to describe the ageing for a given chiller plant. This research therefore proposes a method of identifying the chiller maintenance factor using a Bayesian Markov Chain Monte Carlo method, which can take account of the uncertainties that exist in the estimation of the ageing. Details of the identification will be provided by applying the proposed method to a real chiller plant, and results will be compared with that of the conventional analysis.

A study of pre-cooling impacts on peak demand limiting in commercial buildings

Peak demand cost usually contributes a considerable amount to the monthly electricity bills of commercial buildings, even up to 50%. Peak demand limiting is an effective way to minimize the bill, which has received attention from both academia and industry. However, existent studies do not systematically analyze the impacts of pre-cooling on the peak demand limiting in commercial buildings. This article presents a study to discuss the impacts of pre-cooling temperature and duration in two different types of buildings (i.e., a passive building and a building with phase change material) with and without use of the developed peak demand limiting method (i.e., proportional-integral-derivative peak demand limiting algorithm). The study compares the energy increase and peak demand reduction with a reference case as the benchmark at different pre-cooling temperatures and durations. A demand reduction effective index is introduced to evaluate the effectiveness of peak demand limiting in cost of energy rise. The results from the studies are shown to be useful for selecting proper pre-cooling temperature and duration for high-demand reduction effective index in peak demand limiting controls and for supporting the application of phase change material in reducing peak demand in commercial buildings.