Dr. Brennan's research activities are broadly concerned the development of control systems that allow manufacturers to quickly respond to change while maintaining stable and efficient operation.
More specifically, research interests and expertise encompass the following areas: manufacturing control architectures, discrete-event simulation, object-oriented and agent-based modelling and computer control.
Recent research projects include:
- Distributed intelligent control modelling and design: This research focuses primarily on how distributed artificial intelligence concepts can be applied to hard real-time control problems. In particular, the emerging IEC 1499 standard for industrial control systems is being used as a general modelling and design approach and its relationship with other modelling approaches is being investigated (e.g., conventional programmable logic controller languages, objects, agents, Petri nets, etc.).
- Fault monitoring and recovery: This research involves the identification of the types and nature of faults that a real-time manufacturing system controller must be deal with, the classification of the ways in which control agents should handle fault recovery (e.g., fail-safe modes, homogeneous and diverse redundancy), as well as the development of preliminary models of control agents using the IEC 1499 function block model.
Manufacturing Control Architectures:
- Control architecture metrics: The identification of metrics for the analysis of alternative control architectures. Metrics include both structural parameters that are used to characterise given control architectures as well as manufacturing and control system performance measures.
- Evaluating alternative control architectures: The development of test cases that can represent a common unbiased platform against which to compare and evaluate the performance of various proposed solutions. This common benchmark will give the research community a unique way to assess the opportunities and highlight the main pitfalls resulting from the adoption of MAS in the real industrial domain.
Modelling and Analysis of Manufacturing Systems:
- Discrete-event Dynamic Systems (DEDS): The application of DEDS techniques to assist the control system in making the best possible decisions that will result in control system behaviour that is responsive to the needs of the manufacturing system.
- Gradient Estimation: The integration of gradient estimation and stochastic optimisation modelling tools with existing manufacturing control system software.
Holonic Manufacturing Systems:
- This work overlaps with each of the three project areas discussed previously. For example, holonic concepts are being used to help understand and describe distributed manufacturing systems control at the production planning and scheduling level as well as at the real-time control level (i.e., where traditional PLC will eventually be replaced by "holonic controllers").