Agent-Based Production Control

: Minnesat is the micro-satellite that was designed by the University of Minnesota as part of UNP-4. The mission of Minnesat was to validate the use carrier phase GPS technology to mechanize a compact and accurate attitude determination system. In a broader sense, the objective of this work was to also demonstrate a design methodology for miniature spacecraft navigation, guidance and control systems which integrate the vehicle design process with that of the process of designing the avionics. Minnesat was presented to the US Air Force at the UNP-4 flight competition review. Although Minnesat was not the winning design, it accomplished its scientific and educational goals. The research work of designing compact GPS attitude determination systems continues at the University of Minnesota building on the success of Minnesat. On the other hand, students that were participants in the UNP-4 competition are now employed by various aerospace corporations, are enrolled in aerospace engineering graduate programs or are completing advanced degrees in engineering and science programs.

This paper reviews the state of the art of field- programmable gate array (FPGA) design methodologies with a focus on industrial control system applications. This paper starts with an overview of FPGA technology development, followed by a presentation of design methodologies, development tools and relevant CAD environments, including the use of portable hardware description languages and system level programming/design tools. They enable a holistic functional approach with the major advantage of setting up a unique modeling and evaluation environment for complete industrial electronics systems. Three main design rules are then presented. These are algorithm refinement, modularity, and systematic search for the best compromise between the control performance and the architectural constraints. An overview of contributions and limits of FPGAs is also given, followed by a short survey of FPGA-based intelligent controllers for modern industrial systems. Finally, two complete and timely case studies are presented to illustrate the benefits of an FPGA implementation when using the proposed system modeling and design methodology. These consist of the direct torque control for induction motor drives and the control of a diesel-driven synchronous stand-alone generator with the help of fuzzy logic.

ABSTRACTThis work focuses on developing a design methodology for reliable control system instrumentation. This methodology aims at optimising the instrumentation using a dependability criterion based on reliability and cost. The design methodology is primarily based on three processes: structural analysis of the relations between different variables and physical quantities that govern the system; evaluation of the reliability of the main mission of the control system instrumentation using the structural links, which depends on a graphical tree representation of the system in the design phase; and optimisation, which allows us to determine the best architecture for instrumentation. This methodology is illustrated for the design of an electric vehicle.

Purpose The paper presents the results of work on control systems of bearingless electric motors. Authors proposed the applications of bearingless electric machines for aircraft actuation system. Suggested solution characterizes novel concept of on-board equipment design such as More Electric Aircraft. Magnetic suspension technology allows elimination of friction force and the negative performance features of classic bearing system. However, to achieve all these purposes appropriately, dedicated control system must be also applied. Design/methodology/approach The development of a control system of bearingless electric machine is presented in detail. Mathematical model and construction of induction bearingless motor are widely discussed. Then, proportional–integral-derivative controller algorithm designing for BEM control system was presented using the well pole placement method. Simulation model of BEM control system with use of Matlab-Simulink software was shown. Finally, experimental studies on laboratory stand were introduced. The paper presents design methodology of conventional and advanced control system of bearingless motor. Findings The presented concept of the bearingless electric machines could be applied in the on-board actuation system. During research, full control system of bearingless electric motor was designed and tested. This system consisted of two subsystems. The first responded for rotary speed stabilization and second one was designed for position control of the rotor in the air gap. Practical implications The presented concept of the bearingless electric machines could be applied in the on-board actuation system. During research, full control system of bearingless electric motor was designed and tested. This system consisted of two subsystems. The first responded for rotary speed stabilization and second one was designed for position control of the rotor in the air gap. Originality/value The idea of active magnetic suspension system will be implemented for aviation on technology readiness level V. The paper presents unique laboratory stand with bearingless electric motor and experimental studies. The stable time responses of designed control system were presented and discussed. In addition, preliminary considerations of advanced control system with robust controller were introduced as well.

Input-output-linearization via state feedback offers the potential to serve as a practical and systematic design methodology for nonlinear control systems. Nevertheless, its widespread use is delayed due to the fact that developing an accurate plant model based on physical principles is often too costly and time consuming. Data-based dynamic modeling using neural networks offers a cost-effective alternative. The work describes the methodology of input-output-linearization using neural process models and gives an extended simulative case study of its application to trajectory tracking of a batch polymerization reactor.

Modern complex and flexible manufacturing systems require new types of control system architectures and design methodologies. An essential new applied architectural approach is the distributed control architecture and an essential new methodology within automation is model-based engineering. There is a wide range of modeling approaches available from traditional automation, domain-oriented systems engineering, and software engineering. Unfortunately, there are very few reports on the pros and cons of these modeling approaches for the practical design tasks along the engineering process for distributed control systems (DCSs). This paper gives a first evaluation for the suitability of models for engineering DCSs to provide the researcher and practitioner with combinations of models that are well suited for their engineering projects.

It is time consuming to design a complex system which has multi-function and mutual exclusion. In this paper, the design which flows of complex control system with Mutual Exclusion is divided into abstraction route and embodiment route. In the abstraction route, functions of the system are transferred to processes by using obtained or innovated algorithms and the processes are divided into modules by hardware/software partitioning. In the embodiment route, these modules are transferred to hardware objects which execute the modules. The whole design methodology is illustrated by the hardware implementation of the Philosophers’ dining question. The hardware object is designed and is simulated by using FPGA. The emphasis is focused on the design of a hardware system with distributed mutual exclusion.

The paper presents proof of concept design and validation methodology of the flight recovery control system. The flight recovery control system algorithm is based on the model predictive control. Adaptive control is considered for situations where the airplane changes its parameters during the flight. The acceptability of maneuvers automatically conducted by flight recovery control system by pilots can be validated in the hardware in the loop simulation on the aircraft stand with the projection of the pilot view from the cabin. Matlab Simulink is used for models and the control law design. A special block set has been designed for interface Simulink models to the hardware in the loop simulator.

This Paper presents a novel hardware design methodology of digital control systems. For this, instead of synthesizing the control system using Very high speed integration circuit Hardware Description Language (VHDL), LabVIEW FPGA module from National Instrument (NI) is used to design the whole system that include analog capture circuit to take out the analog signals (set point and process variable) from the real world, PID controller module, and PWM signal generator module to drive the motor. The physical implementation of the digital system is based on Spartan-3E FPGA from Xilinx. Simulation studies of speed control of a D.C. motor are conducted and the effect of a sudden change in reference speed and load are also included.

Digital Controller Implementation for Electrohydraulic Motion Simulator Based on MAS and RCP

A combined experience and model based design methodology of a fuzzy control system for mean arterial pressure and cardiac output

A systematic design methodology for DC-link voltage control of single phase grid-tied PV systems

In this paper, we propose a new generalized design methodology of intelligent robust fuzzy control systems based on quantum genetic algorithm (QGA) called quantum fuzzy model that enhance robustness of fuzzy logic controllers. The QGA is adopted because of their capabilities of directed random search for global optimization to find the parameters of the shape and width of membership functions and rule set of the FLC to obtain the optimal fuzzy controller simultaneously. We test the optimal FLC obtained by the quantum computing applied on the control of dynamic balance and motion of cart-pole balancing system. We compare the proposed technique with existing mamdani fuzzy logic controller which is designed through conventional genetic algorithm. Simulation results reveal that QGA performs better than conventional GA in terms of running speed and optimizing capability.

The presented contribution introduces a simple methodology for robust control system design in case of the limitations on a manipulated variable. For this task efficient simulation and optimization tools of the MATLAB environment are fruitfully exploited. The control system design is based on the polynomial approach resulting in the pole-placement problem to be solved. This task is addressed numerically by means of the standard MATLAB functions for nonlinear constrained optimization to meet both the constraints on the control input signal and robustness of the resultant closed-loop. Some new control quality criteria and procedure are introduced for this purpose. The suggested methodology is illustrated practically on a simulation example with a classical feedback control configuration and one optimized parameter. The presented results of robust constrained control of a magnetic levitation system show applicability of the suggested approach.

This paper develops a novel control system design methodology that uniquely combines: concepts from thermodynamic exergy and entropy; Hamiltonian systems; Lyapunov's direct method and Lyapunov optimal analysis; electric AC power concepts; and power flow analysis. Relationships are derived between exergy/entropy and Lyapunov optimal functions for Hamiltonian systems. The methodology is demonstrated with a few fundamental numerical simulation examples: 1) a Duffing oscillator/Coulomb friction nonlinear model that employs PID regulator control; and 2) a linear PID tracking servo control design for a translational single robot link system. The control system performances are partitioned and evaluated based on exergy generation and exergy dissipation terms. This novel nonlinear control methodology results in both necessary and sufficient conditions for stability of nonlinear systems