Control of knowledgeable manufacturing cell with an unreliable agent

Aiming at the task control problems existing in the knowledgeable manufacturing system, the concept of state jump system is proposed to analyze a knowledgeable manufacturing cell with an unreliable agent. The uncertain factors of the knowledgeable manufacturing cell are addressed in the task control model by utilizing a self-learning method of probability distribution parameters of stochastic events. With the state jump system given, the task control problem is greatly simplified that the optimal task control strategy of the manufacturing cell can be obtained by the combination of the uniform technology and the stochastic dynamic programming. The objective function can be stabilized to a certain extent for different initial conditions, which verifies the feasibility of the control strategy. Compared to the random control and maximum control principles, the objective function value of the optimal control strategy in this paper is relatively low, which confirms the validity of the control strategy.