Manipulator maneuvering by feedback linearization with saturating inputs

Recent research on fast exact maneuvering strategies for manipulators has employed acceleration commands as control variables. The forces and torques can then be synthesized, either in software or with dedicated hard-wired interfaces. Among the difficulties that occur when such maneuver techniques are employed is the fact that actuator saturation constraints are related to acceleration bounds in a state-dependent way. Recent work in the literature relies on generating a correction to the acceleration inputs, by pointwise constrained acceleration error minimization. This technique works best for infinite time horizons and highly coupled manipulator geometries, but not for terminal control or when the forces and torques enter the dynamics multiplied by a diagonal control influence matrix. An alternative technique discussed in the present paper consists of running actuators at saturation levels between sampling instants, then re-initializing an exact optimal regulator algorithm whenever the inputs drop below saturation range. Examples of such a maneuver strategy are given, both for asymptotic regulation and terminal control.