High-performance mechatronic systems are widely used in precision manufacturing equipment such as CNC machine tools, 3D-Printers, photolithography systems, industrial robots, and Coordinate Measuring Machines (CMMs). These equipment are utilized in producing parts and components for aviation, semiconductor, optics, and many other emerging industries, with geometric features and surface properties within micrometer-, or even in some cases nanometer-level accuracy. To keep up with the rapidly increasing productivity and accuracy demands, it is crucial that mechatronic systems of these manufacturing equipment deliver high-speed motion with high precision. In this dissertation, motion control strategies are presented to increase dynamic positioning accuracy and productivity of such mechatronic systems. First, a novel trajectory generation method is presented to avoid exciting low frequency structural vibration modes of machine tools and 3D-Printers, without compromising from productivity. The trajectory generation problem is posed as a convex optimization problem, and a practical windowing method is presented to implement the proposed strategy in real-time for long and realistic manufacturing scenarios. The proposed algorithm is validated on an industrial 3-Axis machine tool, and 4-6 times attenuation of the column vibration mode is achieved with 1[g] acceleration commands, without increasing the cycle time compared to state-of-the-art trajectory generation methods. This is followed by proposition of a data-driven trajectory shaping algorithm designed to eliminate dynamic positioning errors induced by flexible motion transmission components (such as ball-screw drives) and nonlinear friction forces typically caused by mechanical bearings and guiding units. The proposed algorithm is used for optimizing trajectory pre-filters through machine-in-the-loop iterations, in a datadriven fashion, and therefore it can be applied on a wide variety of systems without requiring elaborate dynamic modeling. Effectiveness of the proposed technique is validated on a linear-motor-driven planar motion stage and an industrial 3-Axis machine tool, and it is shown that dynamic errors are reduced by 3-5 times compared to industry-standard approaches. Finally, an active tool position control strategy is proposed to mitigate self-excited (chatter) vibrations for improving stability margins of turning processes. Two motion control algorithms are developed to control the dynamic process defined by the interaction of the tool and the workpiece. An industrial lathe (turning center) is utilized for validating the effectiveness of proposed algorithms. A piezo-actuator driven tool-assembly is utilized to control tool position during the machining process, utilizing tool acceleration feedback, and the experiments show that 4-5 times increase in productivity (widths of cut) is achieved by the proposed strategy