Intelligent module for planning/control of master-dependent systems

Abstract

An intelligent module is proposed in this paper which is capable of performing a joint recursive planning/control operation which propagates through the intelligent module at all planning/control levels simultaneously. Each of the actuators is equipped by an intelligent module, and all of these modules are working independently and concurrently. The model of the world is being constantly updated based upon vision and a multiplicity of other available sensors, and at various resolutions is submitted to each of the intelligent modules as applied to particular properties of the link being controlled by this module. Together these intelligent modules are working as a team of the actuators controllers, and all decisions are constantly negotiated among the members of the team. Each of the resolutional levels within the actuator intelligent modules is using the following set of planning/control tools: learning rule base, learning heuristic search, and situational decision generator which are first applied simultaneously, and then one of them takes over. A neural network is collecting information about the progress and the results of planning/control processes, and is modifying heuristics, as well as enriching the system of rules. The same neural network is used for supplying the provisional analytical model required for the lowest levels of execution control. Provisional analytical models are applied in a simple form which allows for simple real-time controller operation, and the parameters of this provisional model are constantly being updated by the neural network.

Reducing problem-solving variance to improve predictability

“…Other large real-time systems are required to operate in environments that are not fully characterized [2]. The lack of information and the uncer- [12], and a need to integrate knowledge-based approaches to handle non-linearities and problem-solving behavior in control systems [3,11,17,22]. Some of the early attempts to build such systems resulted in coincidentally real-time systems, which were difficult to analyze and predict [12].…”