Catálogo de publicaciones - libros

This chapter formulates a signed real measure of sublanguages of a regular language based on the principles of automata theory and real analysis. The measure allows total ordering of a set of partially ordered sublanguages of the regular language for quantitative evaluation of the controlled behavior of deterministic finite state automata (DFSA) under different supervisors. In the setting of the language measure, a supervisor’s performance is superior if the supervised plant is more likely to terminate at a marked state and/or less likely to terminate at a marked state. The computational complexity of the language measure algorithm is polynomial in the number of DFSA states.

This chapter presents optimal supervisory control of dynamical systems that can be represented by deterministic finite state automaton (DFSA) models. The performance index for the optimal policy is obtained by combining a measure of the supervised plant language with (possible) penalty on disabling of controllable events. The signed real measure quantifies the behavior of controlled sublanguages based on a state transition cost matrix and a characteristic vector as reported in Chapter 1 and earlier publications. Synthesis of the optimal control policy requires at most iterations, where is the number of states of the DFSA model generated from the unsupervised plant language. The computational complexity of the optimal control synthesis is polynomial in . Syntheses of the control algorithms are illustrated with two application examples.

This chapter presents an algorithm for robust optimal control of regular languages under specified uncertainty bounds for the event costs of a language measure that has been recently reported in literature and is presented in Chapter 1. The performance index for the proposed robust optimal policy is obtained by combining the measure of the supervised plant language with uncertainty. The performance of a controller is represented by the language measure of the supervised plant and is minimized over the given range of event cost uncertainties. Synthesis of the robust optimal control policy requires at most iterations, where is the number of states of the deterministic finite state automaton (DFSA) model generated from the regular language of the unsupervised plant behavior. The computational complexity of control synthesis is polynomial in .

The signed real measure of regular languages, introduced in Chapter 1, has been the driving force for quantitative analysis and synthesis of discrete-event supervisory (DES) control systems dealing with finite state automata (equivalently, regular languages). However, this approach relies on memoryless state-based tools for supervisory control synthesis and may become inadequate if the transitions in the plant dynamics cannot be captured by finitely many states. From this perspective, the measure of regular languages needs to be extended to that of non-regular languages, such as Petri nets or other higher level languages in the Chomsky hierarchy [9]. The development of measures for non-regular languages is a topic of future research that has not apparently been reported in open literature. This chapter introduces two research topics in the field of language-measure-based supervisory control. One topic is complex measure of non-regular languages, dealing with . The proposed complex measure reduces to the signed real measure [16] [12] [15], as presented in Chapter 1, if the is degenerated to a regular grammar. The other topic is modification of the (regular) language measure for supervisory control under partial observation. This chapter shows how to generalize the analysis to situations where some of the events may not be observable at the supervisory level.

As an application of the theory of Discrete Event Supervisory (DES) control presented in Chapters 1 and 2, this chapter addresses the design of a robotic system interacting with a dynamically changing environment. The work, reported in this chapter, encompasses the disciplines of control theory, signal analysis, computer vision, and artificial intelligence. Several traditional important methods are first reviewed to substantiate the DES control approach in the design of a mobile robotic system. Design and modelling of the behavior-based mobile robotic system are presented in details. The plant automaton model of the robotic system is identified by making use of the available sensors and actuators. Then, a DES controller is synthesized based on the data collected from experimental scenarios. Through these experiments, performance of the robotic DES control system is quantitatively evaluated in terms of the language measure for both the unsupervised and supervised robotic systems. It is shown that the language measure can indeed be used as a performance index in the design of optimal DES control policies for higher level mission planning for behavior-based mobile robotic systems.

This chapter presents optimal discrete-event supervisory control of robot behavior in terms of the language measure , presented in Chapter 1. In the discrete-event setting, a robot’s behavior is modelled as a regular language that can be realized by deterministic finite state automata (DFSA). The controlled sublanguage of a DFSA plant model could be different under different supervisors that are constrained to satisfy different specifications [6]. Such a partially ordered set of sublanguages requires a quantitative measure for total ordering of their respective performance. The language measure [10] [8] serves as a common quantitative tool to compare the performance of different supervisors and is assigned an event cost matrix, known as the -matrix and a state characteristic vector, -vector. Event costs (i.e., elements of the -matrix) are based on the plant states, where they are generated; on the other hand, the -vector is chosen based on the designer’s perception of the individual state’s impact on the system performance. The elements of the -matrix are conceptually similar to the probabilities of the respective events conditioned on specific states; these parameters can be identified either from experimental data or from the results of extensive simulation, as they are dependent on physical phenomena related to the plant behavior. Since the plant behavior is often slowly time-varying, there is a need for on-line parameter identification to generate up-to-date values of the -matrix within allowable bounds of errors. The results of simulation experiments on a robotic test bed are presented to demonstrate efficacy of the proposed optimal control policy.

This chapter presents an application of the recently developed theory of optimal Discrete Event Supervisory (DES) control that is based on a signed real measure of regular languages described in Chapter 1. The DES control techniques are validated on an aircraft gas turbine engine simulation test bed. The test bed is implemented on a networked computer system in which two computers operate in the client-server mode. Several DES controllers have been tested for engine performance and reliability. Extensive simulation studies on the test bed show that the optimally designed supervisor yields the best performance.

This chapter presents a new paradigm to control software systems based on the Supervisory Control Theory (SCT). The proposed method uses SCT to model the execution process of a software application by restricting the actions of the OS with little or no modifications in the underlying OS. This approach can be generalized to other software applications as the interactions of an application with the Operating System (OS) are modelled at the level as a Deterministic Finite State Automaton (DFSA), called as the “plant”. A “supervisor” that controls the plant is also a DFSA that represents a set of control specifications. The supervisor operates synchronously with the plant to restrict the language accepted by the plant to satisfy the control specifications. As a proof-of-concept for software fault management, two supervisors have been implemented under the Redhat Linux 7.2 OS to mitigate overflow and segmentation faults in five different programs. The performance of the unsupervised plant and that of the supervised plant are quantified by using the Language Measure, described in Chapter 1.

This chapter models the execution of a software process as a discrete event system that can be represented by a Deterministic Finite State Automaton (DFSA) in the discrete event setting. Supervisory Control Theory (SCT) is applied for on-line detection of malicious executables and prevention of their spreading. The language measure theory, described in Chapter 1, is adapted for performance evaluation and comparison of the unsupervised process automaton and five different supervised process automata. Simulation experiments under different scenarios show the rate of correct detection of malicious executables to be 88.75%.