Today＇s automation industry is driven by the need for an increased productivity, higher flexibility, and higher individuality, and characterized by tailor-made and more complex control solutions. In the processing in...Today＇s automation industry is driven by the need for an increased productivity, higher flexibility, and higher individuality, and characterized by tailor-made and more complex control solutions. In the processing industry, logic controller design is often a manual, experience-based, and thus an error-prone procedure. Typically, the specifications are given by a set of informal requirements and a technical flowchart and both are used to be directly translated into the control code. This paper proposes a method in which the control program is constructed as a sequential function chart （SFC） by transforming the requirements via clearly defined intermediate formats. For the purpose of analysis, the resulting SFC can be translated algorithmically into timed automata. A rigorous verification can be used to determine whether all specifications are satisfied if a formal model of the plant is available which is then composed with the automata model of the logic controller （LC）.展开更多
Recently, artificial intelligence technique is increasingly receiving attention in solving complex and practical problem and they are widely applying in electrical machine domain. The authors consider also the direct ...Recently, artificial intelligence technique is increasingly receiving attention in solving complex and practical problem and they are widely applying in electrical machine domain. The authors consider also the direct torque control （DTC） as an alternative to conventional methods of control by pulse width modulation （PWM） and by Field oriented control （FOC）, so the application of the DTC based on artificial intelligence can show more advantages and simplified control algorithms with high performance. For this reason, the objectives of this paper can be divided into two parts, the first part is to apply neural networks and fuzzy logic techniques to the DTC control in the presence of a loop speed control comparing to the conventional regulators （as PI） to show the feasibility of these approaches, the second part is to further improve the performance of the neural network by using a neural-fuzzy regulator which combine the advantages of two techniques. Simulation results confirm the validity of the proposed techniques.展开更多
基金the European Union through the Network of Excellence Hybrid Control (HYCON) under contract IST-511368.
文摘Today＇s automation industry is driven by the need for an increased productivity, higher flexibility, and higher individuality, and characterized by tailor-made and more complex control solutions. In the processing industry, logic controller design is often a manual, experience-based, and thus an error-prone procedure. Typically, the specifications are given by a set of informal requirements and a technical flowchart and both are used to be directly translated into the control code. This paper proposes a method in which the control program is constructed as a sequential function chart （SFC） by transforming the requirements via clearly defined intermediate formats. For the purpose of analysis, the resulting SFC can be translated algorithmically into timed automata. A rigorous verification can be used to determine whether all specifications are satisfied if a formal model of the plant is available which is then composed with the automata model of the logic controller （LC）.
文摘Recently, artificial intelligence technique is increasingly receiving attention in solving complex and practical problem and they are widely applying in electrical machine domain. The authors consider also the direct torque control （DTC） as an alternative to conventional methods of control by pulse width modulation （PWM） and by Field oriented control （FOC）, so the application of the DTC based on artificial intelligence can show more advantages and simplified control algorithms with high performance. For this reason, the objectives of this paper can be divided into two parts, the first part is to apply neural networks and fuzzy logic techniques to the DTC control in the presence of a loop speed control comparing to the conventional regulators （as PI） to show the feasibility of these approaches, the second part is to further improve the performance of the neural network by using a neural-fuzzy regulator which combine the advantages of two techniques. Simulation results confirm the validity of the proposed techniques.
