UC Berkeley

Atmospheric Pressure Plasma Jets (APPJs) are versatile tools for materials processing and medical applications. A key objective for APPJ treatment is the delivery of cumulative and spatially distributed treatment effects, i.e., a plasma dose. However, variability in APPJ operation and their sensitivity to exogenous disturbances can compromise reliable treatment. Particularly, for medical applications, it is necessary to ensure that APPJ effects are delivered safely and repeatably. Feedback control strategies can be crucial in ensuring the APPJ effects are regulated and maintained below critical limits instantaneously and cumulatively.

The APPJ dynamics are nonlinear, multivariable and coupled across multiple timescales. Moreover, the dose delivery problem is spatially distributed. This presents a significant challenge for regulation of APPJ characteristics with basic control strategies based on proportional-integral type controllers. Optimization-based advanced control strategies, such as model predictive control, can systematically account for the APPJ dynamics and the complexities of the dose delivery problem. This work experimentally demonstrates the use of advanced control strategies for regulation of APPJ effects and dose delivery in the presence of common disturbances, including variations in substrate characteristics and changes in jet-tip-to-substrate separation distance. In particular, the thermal effects of the APPJ on treated substrate and thermal dose delivery is investigated. An experimental setup of a kHz-excited APPJ in He is constructed to allow implementation of control algorithms. The key issues of modeling, problem formulation, and control synthesis are undertaken for the development of control strategies.

Development of control-oriented models of APPJs, which describe the dynamic response of controlled outputs to manipulated variables, are required for model-based control strategies. A lumped-parameter physics-based model is developed to describe dynamics of power dissipation in the APPJ and the consequent thermal effects on treated substrates. A fluid model of the transport phenomena in the APPJ in COMSOL informs the structure of the lumped-parameter model, which take the form of a set of differential-algebraic equations. The lumped-parameter model is found to adequately describe the experimentally observed APPJ dynamics despite its simplicity. Moreover, a linear data-driven modeling strategy is shown to be a viable alternative in describing the aspects of APPJ operation that are difficult to model based on physics, albeit for a limited range of operating conditions.

Experimental investigation reveals that basic proportional-integral (PI) strategies tuned using internal model control rules allow rejection of a range of disturbances in a single-input-single-output setting. However, basic control strategies are found to be ineffective in simultaneous control of multiple APPJ effects and for dose delivery. In contrast, MPC strategies are shown to be capable of systematically addressing the multi-variable APPJ dynamics as well as the cumulative nature of the dose delivery problem. With MPC strategies simultaneous regulation of multiple APPJ effects as well as the delivery of a point-wise multi-component dose is made possible in the presence of disturbances. For spatially uniform dose delivery, a hierarchical control strategy is developed. Lower-level basic controllers are found to allow disturbance rejection in fast timescales. On the other hand, the MPC framework allows systematically addressing the different aspects of the dose delivery problem, including the total treatment time, spatially distributed APPJ effects, the multivariable system dynamics, and the translation trajectory of the APPJ over the treated substrate.

Results presented in this work indicate that advanced feedback control strategies are crucial in enabling reliable and reproducible operation of APPJs, particularly in medical applications where safety considerations are stringent and high performance operation is required. The effective regulation of APPJ characteristics can create opportunities for new APPJ applications and the study of APPJ effects by creating controlled environments. Emerging areas for future work include the application of data analytics and machine learning methods for real-time diagnostics, modeling, and control of the complex time-varying APPJ phenomena.