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About

Erling Lunde’s research has centered on the control and trajectory generation of robotic manipulators, with a particular focus on kinematically redundant systems. His major contributions lie in developing practical learning control algorithms that do not require acceleration measurements, making them more feasible for real-world implementation. Lunde’s work on linear quadratic optimal tracking for manipulators introduced a method to round corners and generate optimal feedforward control, significantly improving trajectory execution for straight-line and segmented paths. His early papers, such as "Cartesian Control of a Class of Redundant Manipulators" (1986) and "Dynamic Control of Kinematically Redundant Robotic Manipulators" (1987), laid foundational insights into task-space control that accounted for manipulator dynamics. Later, his framework for learning control through parameterized control spaces (1988) demonstrated how stored motion knowledge could be leveraged for global sub-optimal solutions in redundancy resolution. Though his citation counts are modest—ranging from 2 to 10 per paper—his work represents a thoughtful, applied approach to robot control that has informed subsequent developments in learning-based and optimal control strategies for complex manipulator systems.