1,431 research outputs found

    Adaptive Control Based On Neural Network

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    A Tutorial on Learning Human Welder\u27s Behavior: Sensing, Modeling, and Control

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    Human welder\u27s experiences and skills are critical for producing quality welds in manual GTAW process. Learning human welder\u27s behavior can help develop next generation intelligent welding machines and train welders faster. In this tutorial paper, various aspects of mechanizing the welder\u27s intelligence are surveyed, including sensing of the weld pool, modeling of the welder\u27s adjustments and this model-based control approach. Specifically, different sensing methods of the weld pool are reviewed and a novel 3D vision-based sensing system developed at University of Kentucky is introduced. Characterization of the weld pool is performed and human intelligent model is constructed, including an extensive survey on modeling human dynamics and neuro-fuzzy techniques. Closed-loop control experiment results are presented to illustrate the robustness of the model-based intelligent controller despite welding speed disturbance. A foundation is thus established to explore the mechanism and transformation of human welder\u27s intelligence into robotic welding system. Finally future research directions in this field are presented

    Neuro-Fuzzy Speed Tracking Control of Traveling-Wave Ultrasonic Motor Drives Using Direct Pulsewidth Modulation

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    The traveling-wave ultrasonic motor (TWUM) drive offers many distinct advantages but suffers from severe system nonlinearities and parameter variations, especially during speed control. This paper presents a new speed tracking control system for the TWUM drive, which newly incorporates neuro-fuzzy control and direct pulsewidth modulation to solve the problem of nonlinearities and variations. The proposed control system is digitally implemented by a low-cost digital-signal-processor-based microcontroller, hence reducing the system hardware size and cost. Experimental results confirm that the proposed speed tracking controller can offer good steady-state and transient performance.published_or_final_versio

    State of the art of control schemes for smart systems featuring magneto-rheological materials

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    This review presents various control strategies for application systems utilizing smart magneto-rheological fluid (MRF) and magneto-rheological elastomers (MRE). It is well known that both MRF and MRE are actively studied and applied to many practical systems such as vehicle dampers. The mandatory requirements for successful applications of MRF and MRE include several factors: advanced material properties, optimal mechanisms, suitable modeling, and appropriate control schemes. Among these requirements, the use of an appropriate control scheme is a crucial factor since it is the final action stage of the application systems to achieve the desired output responses. There are numerous different control strategies which have been applied to many different application systems of MRF and MRE, summarized in this review. In the literature review, advantages and disadvantages of each control scheme are discussed so that potential researchers can develop more effective strategies to achieve higher control performance of many application systems utilizing magneto-rheological materials

    Design of Power System Stabilizer

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    A power system stabilizer (PSS) installed in the excitation system of the synchronous generator improves the small-signal power system stability by damping out low frequency oscillations in the power system. It does that by providing supplementary perturbation signals in a feedback path to the alternator excitation system. In our project we review different conventional PSS design (CPSS) techniques along with modern adaptive neuro-fuzzy design techniques. We adapt a linearized single-machine infinite bus model for design and simulation of the CPSS and the voltage regulator (AVR). We use 3 different input signals in the feedback (PSS) path namely, speed variation(w), Electrical Power (Pe), and integral of accelerating power (Pe*w), and review the results in each case. For simulations, we use three different linear design techniques, namely, root-locus design, frequency-response design, and pole placement design; and the preferred non-linear design technique is the adaptive neuro-fuzzy based controller design. The MATLAB package with Control System Toolbox and SIMULINK is used for the design and simulations

    Real-Time Machine Learning Based Open Switch Fault Detection and Isolation for Multilevel Multiphase Drives

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    Due to the rapid proliferation interest of the multiphase machines and their combination with multilevel inverters technology, the demand for high reliability and resilient in the multiphase multilevel drives is increased. High reliability can be achieved by deploying systematic preventive real-time monitoring, robust control, and efficient fault diagnosis strategies. Fault diagnosis, as an indispensable methodology to preserve the seamless post-fault operation, is carried out in consecutive steps; monitoring the observable signals to generate the residuals, evaluating the observations to make a binary decision if any abnormality has occurred, and identifying the characteristics of the abnormalities to locate and isolate the failed components. It is followed by applying an appropriate reconfiguration strategy to ensure that the system can tolerate the failure. The primary focus of presented dissertation was to address employing computational and machine learning techniques to construct a proficient fault diagnosis scheme in multilevel multiphase drives. First, the data-driven nonlinear model identification/prediction methods are used to form a hybrid fault detection framework, which combines module-level and system-level methods in power converters, to enhance the performance and obtain a rapid real-time detection. Applying suggested nonlinear model predictors along with different systems (conventional two-level inverter and three-level neutral point clamped inverter) result in reducing the detection time to 1% of stator current fundamental period without deploying component-level monitoring equipment. Further, two methods using semi-supervised learning and analytical data mining concepts are presented to isolate the failed component. The semi-supervised fuzzy algorithm is engaged in building the clustering model because the deficient labeled datasets (prior knowledge of the system) leads to degraded performance in supervised clustering. Also, an analytical data mining procedure is presented based on data interpretability that yields two criteria to isolate the failure. A key part of this work also dealt with the discrimination between the post-fault characteristics, which are supposed to carry the data reflecting the fault influence, and the output responses, which are compensated by controllers under closed-loop control strategy. The performance of all designed schemes is evaluated through experiments
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