Introduction

       With the deepening economic globalization and the fierce market competition, the living environment of manufacturing enterprises is becoming more and more turbulent. The requirements for flexibility and production capacity of manufacturing systems are increasing, while the predictability of manufacturing processes is declining constantly. Therefore, production managers are required to make timely and accurate responses to the dynamic and changeable manufacturing environment. Workshops production control system is an important method to optimize the operation and improve the system performance for workshops. The dynamic parameters or system model structure of the Workshops system often change due to the change of the actual operating conditions and internal and external conditions of workshops. When considering the production control from the perspective of system theory, it is impossible for conventional controllers to control quality well, and adaptive control is the control theory and method to study the production control systems for workshops with certain degree of uncertainty. Around the topics on production-operation-decision-making for discrete manufacturing workshops, this book focuses on the adaptive control of production capacity from the perspective of control system. The main research is as follows:

       (1) Construct the framework of adaptive production control system based on control theory. The adaptive production control system consists of three key modules: production controller, performance analysis and strategy optimization, and workshop operation decision. The systemuses centralized event-driven mechanism to link the production controller module and the workshop operation decision-making module, and uses decentralized event-driven mechanism to establish the intelligent unit decision-making module for workshops, which can meet the complex and dynamic workshop operation environment. This book proposes an adaptive control strategy based on driving error, which improves the performance of workshop systems and reduces the operating frequency of the controller.

       (2) Establish the production control system model for discrete manufacturing workshops. Based on the basic thought of flow rate control, the production control system model of discrete manufacturing workshops is established in continuous time domain, and the double-layer feedback control link is integrated in the model. In this book, it analyzes the dynamic characteristics of production control system with different parameters in detail in time domain. The key parameters of the system, such as work-in-process (WIP), production lead time and settling time of production capacity, are associated with the damping ratio and natural frequency in control theory, revealing the "damping" characteristics of WIP inventory and the "natural frequency" characteristics of production lead time and settling time of production capacity.

       (3) Study the optimization design of production controller parameters in discrete manufacturing workshops. We embedded the adaptability of biological cybernetics in the parameter design of the production controller. Firstly, analyze the feedback regulation principle of the neuroendocrine system, and study the action function of the hormone steady-state regulation mechanism. On this basis, we designed the double closed-loop controllers with backlog tasks and IVWIP variables as controller parameters, and construct the functional relationship between controller parameters and workshop system performance indicators, and design an adaptive control strategy with driving error as a function.

       (4) Analyze the performance of the production control system in discrete manufacturing workshops under disturbance. Firstly, analyze the internal architecture of discrete manufacturing Workshops, and propose a self-organized production mode, which simplifies the production control process and defines the order flow matrix to represent the internal architecture for workshops. WIP inventory is a key variable to describe the internal dynamic characteristics of the system. The number of WIP directly or indirectly affects the damping ratio and natural frequency of the system. Taking WIP inventory as the state feedback variable of the production control system for discrete workshops, a state feedback control law is designed based on production capacity adjustment time and sampling period. The state equation and output equation of the workshop system are established in the discrete-time domain, taking the order flow matrix as a link. The eigenvalue of state matrix for discrete manufacturing workshops is solved, and the performance of the production control system is evaluated by using the state space method.

       (5) Design the decision-making mechanism of workshop operation, and build an experimental platform for adaptive production control. The objective function of workshop operation decision is defined, which comprehensively considers production rate, cost and key performance indexes of workshop. According to the objective function, the basic facts and decision rules such as attributes, process data and load information of each unit in the workshop are defined, and the decision-making mechanism of workshop operation is constructed. Build the intelligent unit controller by embedded industrial computer, and then construct the experimental platform of the distributed adaptive control, and develop the prototype system of the adaptive production control, which realizes the effective connection between control strategy and execution decision. The key technologies proposed in this book are verified by the experimental platform.