The problem of controlling sensory perception for use in discrete event feedback control systems is addressed in this thesis. The sensory perception controller (SPC) is formulated as a sequential Markov decision problem. The SPC has two main objectives; 1) to collect perceptual information to identify discrete events with high levels of confidence and 2) to keep the sensing costs low. Several event recognition techniques are available where each of the event recognisers produces confidence levels of recognised events. For a discrete event control system running in normal operation, the confidence levels are typically large and only a few event recognisers are needed. Then, as the event recognition becomes harder, the confidence levels will decrease and additional event recognisers are utilised by the SPC. The final product is an intelligent architecture with the ability to actively control the use of sensory input and perception to achieve high performance discrete event recognition.

The discrete event control framework is chosen for several reasons. First, the theory of discrete event systems is applicable to a wide range of systems. In particular, manufacturing, robotics, communication networks, transportation systems and logistic systems all fall within the class of discrete event systems. Second, the dynamics of the sensing signals used by the event recognisers are often strong and contain a large amount of information at the occurrence of discrete events. Third, because of the discrete nature of events, feedback information is not required continuously. Hence, valuable processing time is available between events. Fourth, the discrete events are a natural common representational format for the sensors. A common sensor format aids the decision process when dealing with different sensor types. Fifth, the sensing aspect of discrete event systems has often been neglected in the literature. In this thesis we present a unique approach to on-line discrete event identification.

The thesis contains both theoretical results and demonstrated real-world applications. The main theoretical contributions of the thesis are 1) the development of a sensory perception controller for the dynamic real-time selection of event recognisers. The proposed solution solves the Markov decision process using stochastic dynamic programming (SDP). SDP guarantees cost-efficiency of the real-time SPC by solving a sequential constrained optimisation problem. 2) A sensitivity analysis method for the sensory perception controller has been developed by exploring the relationship between Markov decision theory and linear programming. The sensitivity analysis aids in the robust tuning of the SPC by finding low sensitivity areas for the controller parameters.

Two real-world applications are presented. First, several event recognition techniques have been developed for a robotic assembly task. Robotic assembly fits particularly well in the discrete event framework, where discrete events correspond to changes in contact states between the workpiece and the environment. Force measurements in particular contain a significant amount of information when the contact state changes. Second, the sensory perception control theory and the sensitivity analysis have been demonstrated for a mobile navigation problem. The cost-efficient use of sensory perception reduces the need for mobile robots to carry heavy computational resources.