Annual Review in Automatic Programming最新文献

This paper describes a learning algorithm to be applied in process control techniques, covering several topics of control applications such as system identification, or process supervision in a simple and useful way which make the method reliable to be applied on industrial process control. Knowledge acquired by means of a proposed learning algorithm is stored into a DAM or FAM (deterministic or fuzzy associative memory) for finally be applied on model parameter mapping. With such mappings, it is possible to know if the process is being disturbed and the intensity of such excitation inputs when no variation exist in system parameters or it is also possible to know the variation of system parameters when all input forces (control force and disturbances) are known.

In this paper, we study the robust stability problem for a feedback system with a fuzzy controller. The problem is treated using the Small-Gain and Conicity Criteria for nonlinear stability. Robustness is addressed in terms of multiplicative perturbations and stability margins. Compensator design is based on an initial set of rules (the previous design), reflecting an approximate formulation of the desired performance. The previous design is refined and modified to achieve a prescribed robustness margin. We impose in the design procedure that the required changes have to be as small as possible. In this way, the final solution is the better compromise between performance and robust stability. The redesign is based on inequalities on the fuzzy parameters (ouput centroids) that are derived from Small-Gain stability conditions. The proposed method has been successfully tested on the simulated model of an inverted pendulum.

MORSAF is a real-time expert system designed to assist the operator of the SARAS oil refinery in the daily management of the topping plant. The main functionality provided by MORSAF is fault diagnosis, which is performed by an algorithm which reasons upon the faulty behaviour of the topping process represented through causal networks. Moreover, a prevention reasoning module has been designed to detect any unexpected behaviour by monitoring a set of significant process variables. Finally, a prediction functionality has been integrated to allow the filtering of redundant pre-alarms.

Since MORSAF has been developed as the demonstrator for the REAKT Esprit projects, its architecture strongly depends on the one of the REAKT toolkit, which is based on the blackboard model of multi-agents co-operation.

Embedded systems tend to be event driven. The BED kernel supports an event-action programming model for an embedded systems development environment called Testbed. The kernel features deterministic scheduling and message transfer services.

This paper presents an approach to integrated acquistion, evaluation, tuning, and execution of elementary skills in order to ease programming and employment of robots. To allow the use of machine learning techniques as well as real-time execution of the skills, a twofold representation is developed. The individual learning tasks involved in the generation and maintenance of skills are identified and related to proper learning techniques. A programming environment and a low level architecture taking care of the different requirements during all phases of skill acquisition and execution are described.

A Transputer based artificial vision system that allows estimating a mobile robot's absolute position (navigation), it's motion parameters (egomotion) and a depth map of the sight of view is presented. Navigation is achieved by tracking expected features present in basic 3D CAD information of the environment. Egomotion and relative depth are estimated using optic flow calculated on closed contours of points in the scene presenting high spatio-temporal gradients and require no a-priori knowledge.

Fuzzy identification means to find a set of fuzzy if-then rules with well defined attributes, that can describe the given $\text{I}\text{O}\text{-}\text{behaviour}$ of a system. In the identification algorithm proposed here the subject of learning are the rule conclusions i.e. the membership functions of output attributes in form of singletons. For fixed input membership functions learning is shown to be a least squares optimization problem linear in the unknown parameters. Examples show applications of the algorithm to the linguistic formulation of a PI control strategy and to identification of a nonlinear time-discrete dynamic system.

In this paper, we present two different methods to make proofs on the GRAF-CET language. The first one is based on Transition Systems and the second one uses Polynomial Dynamical Systems. Theoretical and pratical aspects are presented.

A hierarchical approach to correctness verification of real-time software specifications is presented. The verification is distributed into successive steps that correspond to the design phases. The three languages: Rule Charts, LACTATRE (graphical specification) and Communicating Real Time State Machines are used for specification of real-time software within corresponding abstraction levels. The correctness is defined as a coincidence of a system specified in a phase w.r.t. requirements established ing the previous phase. This correctness concept leads to an application of the relative correctness methods (developed in former works) for the verification. The approach is examined in Preliminary and Detailed Design phases for the verification of several types of properties: structure, functions and time constraints.

In this paper we discuss issues in real-time image processing, including applications, approaches and hardware. In particular, we discuss the failure of existing programming languages to support these considerations and present requirements for any language that can support real-time image processing.