Parametric drifts and fluctuations occur during plasma spraying. These drifts and fluctuations originate especially from the electrode wear and intrinsic plasma jet instabilities: the plasma net power varies in this case modifying significantly its thermodynamic properties and transport coefficients hence modifying the momentum and heat transfers to the particles. It is possible to control the in-flight particle characteristics by adjusting continuously the operating parameters, in particular the power parameters. Due to the large amplitudes of these drifts and fluctuations, the strategy to adopt will depend on the required corrections to apply to the particle characteristics. Developing a reliable controller requires: (i) implementation of reliable sensors to accurately diagnose in-flight particle characteristics but also some other parameters, such as surface temperature during spraying; (ii) development of a robust command, to insure the stability of the control system. Fuzzy logic permits to define parametric correction rules and the command can be based on these algorithms; (iii) linking of the robust command to a predictive model. Artificial neural networks, among other artificial intelligence protocols such as genetic algorithms, proved to be able to predict in-flight particle and coating characteristics; (iv) validation of the corrections with an extensive database used as a reference. Coupling neural protocols to fuzzy logic should permit the development of such an independent controller which could adjust in real time the operating parameters as a function of the measured in-flight particles to manufacture a coating with identical conditions among its entire thickness. This paper aims at presenting the methodology and the controller and at simulating the spray operation.

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