Control of machines that exhibit flexibility becomes important when designers attempt to push the state of the art with faster, lighter machines. Three steps are necessary for the control of a flexible planet. First, a good model of the plant must exist. Second, a good controller must be designed. Third, inputs to the controller must be constructed using knowledge of the system dynamic response. There is a great deal of literature pertaining to modeling and control but little dealing with the shaping of system inputs. Chapter 2 examines two input shaping techniques based on frequency domain analysis. The first involves the use of the first deriviate of a gaussian exponential as a driving function template. The second, acasual filtering, involves removal of energy from the driving functions at the resonant frequencies of the system. Chapter 3 presents a linear programming technique for generating vibration-reducing driving functions for systems. Chapter 4 extends the results of the previous chapter by developing a direct solution to the new class of driving functions. A detailed analysis of the new technique is presented from five different perspectives and several extensions are presented. Chapter 5 verifies the theories of the previous two chapters with hardware experiments. Because the new technique resembles common signal filtering, chapter 6 compares the new approach to eleven standard filters. The new technique will be shown to result in less residual vibrations, have better robustness to system parameter uncertainty, and require less computation than other currently used shaping techniques.

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New control methods are developed for the unconstrained motion and constrained force and motion control of flexible robots. The dynamic equations of the flexible robots are partitioned as pseudostatic equilibrium equations and deviations from them. The pseudostatic equilibrium considered here is defined as a hypothetical state where the tip point variables have their desired values while the modal variables are instantaneously constant. Then, the control torques for the pseudostatic equilibrium and for the stabilization of the deviation equations are formed in terms of tip point coordinates, modal variables and contact force components. The performances of the proposed methods are illustrated on a planar two-link robot and on a spatial three-link robot. Unmodeled dynamics and measurement noises are also taken into consideration. Performance of the proposed motion control method is compared with the computed torque method.