## 1 Introduction
System identification was performed for each layer, and the response of melt pool size to the laser power was represented by a first-order transfer function that had different coefficients for different layers. A PID controller was used to adjust the laser power; but instead of having constant parameters, its PID parameters were tuned for each layer using the corresponding transfer function. This strategy allowed the controller to be adaptable to changes in the heat conduction mode and cooling rate as the part height increased. However, the layer-by-layer system identification and controller tuning process was time-consuming and lacked automation, which made the above control strategy less user-friendly for industry applications. [PERSON] et al. [14] proposed a two-input single-output hybrid controller that consisted of a rule-based height controller and a closed-loop temperature controller. The laser power was reduced by the height controller until the melt pool height was below the preset layer thickness threshold. Afterward, the temperature controller took over and adjusted the laser power based on the pyrometer feedback. Another hybrid control strategy was proposed for a laser-wire DED system by [PERSON] et al. [15]. The laser power was controlled by the melt pool geometry using thermal camera feedback, while the printing speed and wire feeding rate were controlled by the part height on a per-layer basis. As the part height increased, both the printing speed and wire feeding rate increased. Hence, the average laser energy density decreased, which was allowed due to the heat accumulation in the freshly built layers below the melt pool. This approach could maintain the process stability and improve the productivity at the same time. However, since the heat accumulation effect strongly depended on the material, geometry, area, and maximum height of the part, the selection of controller parameters for a specific product might not be suitable for another product. One of the limitations in the existing control strategies that extensive experimentation is required to find the optimal controller parameters. The system identification and parameter tuning processes are cumbersome and time-consuming. Besides, although some of the aforementioned controllersconsidered interlayer changes in DED process conditions, they did not adapt to the intralayer variation of melt pool dynamics. Therefore, in this research, we propose a novel data-driven adaptive control strategy with automatic parameter tuning instead of using a prior plant model or static controller parameters. During the laser-based DED process, the sensor-captured melt pool size and the laser voltage signal are recorded in each time frame as the system input and output (I/O) data, respectively. The I/O data collected within a periodic time interval are stored in a buffer before they are fed into an autotuning unit to compute the optimal PID controller parameters. The PID controller with the updated parameters is used to adjust the laser power in the next time interval while the new sets of I/O data are being collected to overwrite the buffer. The controller parameters are reoptimized once again at the end of the cycle, using the updated I/O data that reflect the varying response dynamics of the melt pool. The virtual reference feedback tuning (VRFT) algorithm [16] is implemented in the autotuning unit for PID parameter optimization. The data-driven controller update is performed periodically throughout the entire DED process regardless of the present time, layer, material, size, or shape. Prior and interlayer system identification experiments are no longer needed, thus saving time and cost. Besides, since the proposed adaptive controller is dynamically set by the time-dependent process data, it can be applied to parts with any materials, geometries, and sizes without modification, which makes its adoption convenient for industry end-users.