Modelling and PIP control of a glasshouse micro-climate

Young, Peter C.; Lees, Matthew; Chotai, A.; Tych, Włodek; Chalabi, Zaid

doi:10.1016/0967-0661(94)90003-5

Cited by 66 publications

(28 citation statements)

References 9 publications

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“…PIP controllers have been successfully designed for a range of difficult applications, including the control of CO 2 in Open Top Chamber (OTC) and Free Air Carbon dioxide Enrichment (FACE) systems (Lees et al, 1998;Taylor et al, 2000b); control of microclimate in horticultural glasshouses (Young et al, 1994); control of ventilation rate in animal houses and other agricultural buildings (Price et al, 1999); and the automatic positioning of an industrial piling rig system (Dixon et al, 1997).…”

Section: Introductionmentioning

confidence: 99%

Proportional‐Integral‐Plus Control of an Intelligent Excavator

Taylor

Seward

2003

Computer aided Civil Eng

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This article considers the application of Proportional-Integral-Plus (PIP) control to the Lancaster University Computerised Intelligent Excavator (LUCIE), which is being developed to dig foundation trenches on a building site. Previous work using LUCIE was based on the ubiquitous PI/PID control algorithm, tuned on-line, and implemented in a rather ad hoc manner. By contrast, the present research utilizes new hardware and advanced model-based control system design methods to improve the joint control and so provide smoother, more accurate movement of the excavator arm. In this article, a novel nonlinear simulation model of the system is developed for MATLAB/SIMULINK C , allowing for straightforward refinement of the control algorithm and initial evaluation. The PIP controller is compared with a conventionally tuned PID algorithm, with the final designs implemented on-line for the control of dipper angle. The simulated responses and preliminary implementation results demonstrate the feasibility of the approach.

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Section: Introductionmentioning

confidence: 99%

Proportional‐Integral‐Plus Control of an Intelligent Excavator

Taylor

Seward

2003

Computer aided Civil Eng

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“…Research on the NMSS model-based control has been reported in many different areas (Young and Willems 1972;Young, Behzadi, Wang, and Chotai 1987;Young, Lee, Chotai, Tych, and Chalabi 1994;Chotai, Young, Mckenna and Tych 1998;Lee, Taylor, Young and Chotai 1998;Taylor, McCabe, Young and Chotai 2000;Young 2000, 2001;Maciejowski 2002;Pannocchia and Rawlings 2003;Taylor and Shaban 2006;Shead, Muske, and Rossiter 2010;Dubljevic 2010;Grossmann, Stro¨hlein, Morari, and Morbidelli 2010;Gonzalez, Adam, Marcovecchio, and Odloak 2011;Jacob and Dhib 2011;Scheu and Marquardt 2011). It is one of the most widely recognised approaches for controller design.…”

Section: Introductionmentioning

confidence: 98%

“…It is one of the most widely recognised approaches for controller design. Built upon previous work in NMSS model representation by Young et al (1994) and direct digital control framework by Wang and Young (1988), an NMSS model-based predictive control has been developed in Wang and Young (2006) and Exadaktylos, Taylor, Wang, and Young (2009) and further extended to integral-of-error (IOE) case by Exadaktylos and Taylor (2010). These methods offer the advantages of both transfer function design and state-space model design, such as simple framework and ease of analysis, and have been tested through both single-input singleoutput and multi-input multi-output cases, where improved control performance was demonstrated over previous state-space-based predictive control methods.…”

Section: Introductionmentioning

confidence: 99%

An improved state-space model structure and a corresponding predictive functional control design with improved control performance

Zhang

Xue

Wang

et al. 2012

International Journal of Control

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Conventional state-space model predictive control requires a state estimator/observer to access the state information for feedback controller design. Its drawbacks are the numerical convergence stability of the observer and closed-loop control performance deterioration with activated plant input/output constraints. The recent direct use of measured input and output variables to formulate a non-minimal state-space (NMSS) model overcomes these problems, but the subsequent controller is too sensitive to model mismatch. In this article, an improved structure of NMSS model that incorporates the output-tracking error is first formulated and then a subsequent predictive functional control design is proposed. The proposed controller is tested on both model match and model mismatch cases for comparison with previous controllers. Results show that control performance is improved. In addition, a linear programming method for constraints dealing and a closed form of transfer function representation of the control system are provided for further insight into the proposed method. IntroductionModel predictive control (MPC) has been a very active research area in both control theory and control engineering since it first appeared in the 1970s. The main focus is on the three most general MPC design methods: finite impulse response (FIR) or step response models, transfer function models and state-space models. The earlier FIR model-based MPC represented by dynamic matrix control (DMC) (Cutler and Ramaker 1980) and quadratic DMC (Garcia and Morshedi 1986) can only deal with stable processes and the design model order is usually high. The representative article of transfer function model-based MPC can be seen in generalised predictive control (GPC) by Clarke, Mohtadi, and Tuffs (1987), which can deal with both stable and unstable processes. The main disadvantage is that it is considered to be less effective for multi-variable processes and real-time application. The third category is MPC based on state-space models, and it has attracted much interest from researchers since it can effectively use the states' information for state feedback, constraints consideration and even for providing important information for the controlled process. A number of state-space model-based predictive control methods have been developed

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“…Young et al (1994) describe an application for greenhouse temperature control, and also describe a multivariable expansion to control relative humidity and CO 2 as well. Preliminary results were reported that show that RH can be controlled within 3% ± and temperature to within 0.5 ± o C.…”

Section: Proportional-integral-plus Control (Pip)mentioning

confidence: 99%

Optimal Greenhouse Cultivation Control: Survey and Perspectives

Straten

Henten

2010

IFAC Proceedings Volumes

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Abstract:A survey is presented of the literature on greenhouse climate control, positioning the various solutions and paradigms in the framework of optimal control. A separation of timescales allows the separation of the economic optimal control problem of greenhouse cultivation into an off-line problem at the tactical level, and an on-line problem at the operational level. This paradigm is used to classify the literature into three categories: focus on operational control, focus on the tactical level, and truly integrated control. Integrated optimal control warrants the best economical result, and provides a systematic way to design control systems for the innovative greenhouses of the future. Research issues and perspectives are listed as well.

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Modelling and PIP control of a glasshouse micro-climate

Cited by 66 publications

References 9 publications

Proportional‐Integral‐Plus Control of an Intelligent Excavator

Proportional‐Integral‐Plus Control of an Intelligent Excavator

An improved state-space model structure and a corresponding predictive functional control design with improved control performance

Optimal Greenhouse Cultivation Control: Survey and Perspectives

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