Jinbo Fu

Unconstrained optimal control of regular languages

This paper formulates an unconstrained optimal policy for control of regular languages realized as deterministic finite state automata (DFSA). A signed real measure quantifies the behavior of controlled sublanguages based on a state transition cost matrix and a characteristic vector as reported in an earlier publication. The state-based optimal control policy is obtained by selectively disabling controllable events to maximize the measure of the controlled plant language without any further constraints. Synthesis of the optimal control policy requires at most n iterations, where n is the number of states of the DFSA model. Each iteration solves a set of n simultaneous linear algebraic equations. As such, computational complexity of the control synthesis is polynomial in n.

Optimal supervisory control of finite state automata

This paper 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 behaviour of controlled sublanguages based on a state transition cost matrix and a characteristic vector as reported in earlier publications. Synthesis of the optimal control policy requires at most n iterations, where n 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 n. Syntheses of the control algorithms are illustrated with two application examples.

Optimal control of regular languages with event disabling cost

This paper presents an algorithm for optimal control of regular languages with penalty on event disabling. The performance index for the proposed optimal policy is obtained by combining the measure of the supervised plant language with the cost of disabled event(s). Synthesis of this optimal control policy requires at most n iterations, where n is the number of states of the DFSA model generated from the (open loop) regular language. The computational complexity of control synthesis is of a polynomial order in n ,

Robust optimal control of regular languages

This paper presents an algorithm for robust optimal control of regular languages under specified uncertainty bounds on the event cost parameters of the language measure that has been recently reported in literature. 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 supervisory control policy requires at most n iterations, where n 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 the control synthesis method is polynomial in n.

Optimal control of robot behaviour using language measure

This paper presents optimal control of robot behaviour in the discrete event setting. Real signed measure of the language of supervised robot behaviour serves as the performance index for synthesis of the optimal policy. The computational complexity of control synthesis is polynomial in the number of states of the deterministic finite state automaton model that is generated from the regular language of the unsupervised robot behaviour. The results of simulation experiments on a robotic test bed are presented to demonstrate the efficacy of the proposed optimal control policy.

Load scheduling and health management of electric power generation systems

This paper presents a concept of information-integrated decision and control to improve the efficiency, operational reliability, and availability for power generation units, especially the more flexible fossil fuel power plants. The proposed approach simultaneously addresses load dispatch and control, maintenance scheduling, and market decision-making that incorporate sensor-based health monitoring, damage prognosis and life-extension of the generating units in the market-driven environment of energy deregulation.

Supervisory control of software systems for fault mitigation

This paper develops a novel technique of discrete-event supervisory control for fault mitigation in software applications. It models the interactions between a software application and a computer operating system (OS) as a deterministic finite state automation. The supervisor restricts the language of the OS to correct deviations such as CPU exceptions for the controlled execution of software applications. Feasibility of this supervisory control concept is demonstrated on process execution under the Red Hat Linux 7.2 operating system. Two supervisory control policies are implemented as proof of the concept.

Optimal Discrete Event Control of Gas Turbine Engines

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.

Optimal Supervisory Control of Regular Languages

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 n iterations, where n 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 n. Syntheses of the control algorithms are illustrated with two application examples.