Thesis
Advanced control, identification and optimisation of energy systems
- Creator
- Rights statement
- Awarding institution
- University of Strathclyde
- Date of award
- 2013
- Thesis identifier
- T13577
- Qualification Level
- Qualification Name
- Department, School or Faculty
- Abstract
- An increased emphasis is being put on energy efficiency and reduction of carbon emissions. The importance of comfort and energy savings to building designers and occupants has driven the development of more efficient building technologies. This has led to building control engineers and researchers developing more advanced and efficient control strategies in attempts to maximize the potential of these technologies and improve the efficiency of existing technologies. The buildings industry has been slow to adopt these advanced control strategies however, due to complexity issues or increases in cost and commissioning time. In order to bridge the gap between advanced controller design and the buildings industry, it is necessary that the controller designs that are being developed remain low cost and simple to implement. Ultimately, the objective for the design of a controller for use in industry is a robust high performance controller that is easy to implement, requires minimal user input and has low implementation and installation costs i.e. does not require extra/excessive costly hardware. The research presented in this thesis is focused on this problem and delivers solutions that satisfy these criteria. This thesis considers the development of a robust high performance heating, ventilation and air-conditioning (HVAC) control strategy that is capable of dealing with the complexities associated with modern building control whilst being a low cost solution which is easy to implement. When designing a HVAC control system, a number of difficulties are encountered which the control system must be able to deal with in order to ensure accurate set-point tracking (thermal comfort) and efficient energy usage. These include dealing with time delay, parameter uncertainty, nonlinearity, interacting multi-input multi-output (MIMO) systems, unknown system parameters (identification) and tuning/re-tuning. Throughout this thesis a number of novel contributions to knowledge and controller design methodologies have been developed in order to deliver solutions which overcome these problems whilst remaining practical enough for use in industrial applications. The methods developed are based on a practical approach to inverse dynamics controller design and come together as two distinct main controller design methodologies. Firstly, a novel Genetic Algorithm (GA) based auto-tuning algorithm for the Robust Inverse Dynamics Estimation (RIDE) controller design which accounts for modelling parameter uncertainty. Secondly, a novel robust system identification based inverse dynamics controller design which incorporates a novel dead-time compensation methodology within the inverse dynamics controller structure. This controller design is termed System Identification based Rate Compensated Inverse Dynamics (SI-RCID). The SI-RCID controller algorithm is an advanced controller design which is relatively easy to implement, requiring a similar level of user input as the initial design procedure for the popular self-tuning proportional integral derivative (ST-PID) controller designs. When applied to a nonlinear MIMO HVAC system, the SI-RCID controller shows a significant performance improvement when compared to traditional ST-PID control and a more advanced controller design recently developed in the literature.
- Resource Type
- DOI
- Date Created
- 2013
- Former identifier
- 1001726
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