This paper considers an optimal stabilization problem of quantitative discrete event systems (DESs) under the influence of disturbances. We model a DES by a deterministic weighted automaton. The control cost is concerned with the sum of the weights along the generated trajectories reaching the target state. The region of weak attraction is the set of states of the system such that all trajectories starting from them can be controlled to reach a specified set of target states and stay there indefinitely. An optimal stabilizing controller is a controller that drives the states in this region to the set of target states with minimum control cost and keeps them there. We consider two control objectives: to minimize the worst-case control cost (1) subject to all enabled trajectories and (2) subject to the enabled trajectories starting by controllable events. Moreover, we consider the disturbances which are uncontrollable events that rarely occur in the real system but may degrade the control performance when they occur. We propose a linearithmic time algorithm for the synthesis of an optimal stabilizing controller which is robust to disturbances.

In this paper, we formulate an optimal stabilization problem of quantitative discrete event systems (DESs) under partial observation. A DES under partial observation is a system where its behaviors cannot be completely observed by a supervisor. In our framework, the supervisor observes not only masked events but also masked states. Our problem is then to synthesize a supervisor that drives the DES to a given target state with the minimum cost based on the detected sequences of masked events and states. We propose an algorithm for deciding the existence of an optimal stabilizing supervisor, and compute it if it exists.

In this paper we propose a new algorithm to approximate the solution of Hamilton-Jacobi-Bellman equation by using a three layer neural network for affine and general nonlinear systems, and the state feedback controller can be obtained which make the closed-loop systems be suboptimal within a restrictive training domain. Matrix calculus theory is used to get the gradients of training error with respect to the weight parameter matrices in neural networks. By using pattern mode learning algorithm, many examples show the effectiveness of the proposed method.

In this paper a cyclic place-timed controlled marked graph (PTCMG), which is an extended class of a cyclic controlled marked graph (CMG), is presented as a model of discrete event systems (DESs). In a PTCMG, time constraints are attached to places instead of transitions. The time required for a marked place to be marked again is represented in terms of time constraints attached to places. The times required for an unmarked place to be marked under various controls, are calculated. The necessary and sufficient condition for a current marking to be in the admissible marking set with respect to the given forbidden condition is provided, as is the necessary and sufficient condition for a current marking to be out of the admissible marking set with respect to the forbidden condition in one transition. A maximally permissive state feedback control is synthesized in a PTCMG to guarantee a larger admissible marking set than a CMG for most forbidden state problems. Practical applications are illustrated for a railroad crossing problem and an automated guided vehicle (AGV) coordination problem in a flexible manufacturing facility.

We study state feedback control of discrete event systems described by the Golaszewski-Ramadge model. We derive a necessary and sufficient condition for the existence of a balanced state feedback controller under partial observations.

H control theory provides a systematic frequency shaping method of control systems. The existence condition of the solution for so called standard H control problem and the form of the H controller are derived in 1989. One of the most important feature of the controller is that it has many free parameters which can be chosen independently of the H control. On the other hand there is a criticism that the time response of prototypal H control systems is slow. Therefore in this paper we introduce an H2 control optimization by taking advantage of the free parameters to expect the improvement of the time response of the H control system. In this paper we consider state feedback case. The free parameters of state feedback H controller are not directly obtained from well known results since this problem is a kind of non-standard problem. Therefore, in the first place, we will get them via reviewing the FI (ful information) case. Then the free parameters of the state feedback H controller are shown based on the corrected FI solution. These parameters are next used to optimize an H2 control objective in the H control system.