Fast pick-and-place robots are widely used in industry, e.g., in food-packaging and microelectronics. A parallel architecture, composed of one moving platform and one base platform, connected by two serial limbs, was designed and prototyped at McGill University’s Centre for Intelligent Machines. The prototype is dubbed the Peppermill Carrier (PMC). The objective of the work reported in this thesis is to improve the speed of this parallel-kinematics machine (PKM), intended for high-speed operations, while considering its elastodynamics, via modelling, and control. The generalized spring, supported with the finite element method, are used to obtain the elastodynamics model of the robot. The natural frequencies of the robot are obtained along a test trajectory, the Adept test cycle, which serves to identify the poor-stiffness postures; the natural frequencies are further applied to build an enhanced mathematical model of the robot. Along the way, stiffness indices are defined, to help both the designer and the control engineer meet performance requirements. The mathematical model of the robot, which takes into account the flexible nature of the limbs, is formulated. Moreover, a gain-scheduling linear quadratic regulator combined with an extended Kalman filter is designed and applied to control fast pick-andplace operations. Based on the gain-scheduling controller, a constant-gain linear quadratic regulator, combined with a constant-gain extended Kalman filter, is found to be effective at controlling the robot during fast operations, while tracking a prescribed, representative trajectory. Inspired by the simulation results obtained by this regulator, a feed-forward proportional-derivative (PD) controller based on the sliding-mode scheme is also proposed. The use of these linear controllers helps decrease the computational complexity. The feasibility of a linear controller for a nonlinear system such as the PMC is also discussed. Finally, a comparison between four control schemes is conducted to analyze their pros and cons.