This paper addresses the problem of adaptive trajectory control of space manipulators that exhibit elastic vibrations in their joints and that are subject to parametric uncertainties and modeling errors. First, it presents a comprehensive study of rigid and linear flexible-joint stiffness models, to propose a dynamic formulation that includes nonlinear effects such as soft-windup and time-varying joint stiffness. Second, it develops an adaptive composite control scheme for tracking the end effector of a two-link flexible-joint manipulator. The control scheme consists of a direct model reference adaptive system designed to stabilize the rigid dynamics and a linear correction term to improve damping of vibrations at the joints. Numerical simulations compare the performance of the adaptive controller with its nonadaptive version in the context of a 12:6 12:6 m square trajectory tracking. Results obtained with the adaptive control strategy show an increased robustness to modeling errors and uncertainties in joint stiffness coefficients, and greatly improved tracking performance, compared with the nonadaptive strategy.

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Operational problems with robot manipulators in space relate to several factors, most importantly, structural flexibility and subsequent difficulties with their position control. In this paper we present control methods for endpoint tracking of a 1216 1 1216 m 2 trajectory by a two-link robot manipulator. Initially, a manipulator with rigid links is modeled using inverse dynamics, a linear quadratic regulator and fuzzy logic schemes actuated by a Jacobian transpose control law computed using dominant cantilever and pinnedpinned assumed mode frequencies. The inverse dynamics model is pursued further to study a manipulator with flexible links where nonlinear rigid-link dynamics are coupled with dominant assumed modes for cantilever and pinned-pinned beams. A time delay in the feedback control loop represents elastic wave travel time along the links to generate non-minimum phase response. A time delay acting on control commands ameliorates non-minimum phase response. Finally, a fuzzy logic system outputs a variable to adapt the control law in response to elastic deformation inputs. Results show greater endpoint position control accuracy using a flexible inverse dynamics robot model combined with a fuzzy logic adapted control law and time delays than could be obtained for the rigid dynamics models.

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Low cost automation often requires accurate positioning. This happens whenever a vehicle or robotic manipulator is used to move materials, parts or minerals on the factory floor or outdoors. In last few years, such vehicles and devices are mostly autonomous. This paper presents the method of sensor fusion based on the Adaptive Fuzzy Kalman Filtering. This method has been applied to fuse position signals from the Global Positioning System (GPS) and Inertial Navigation System (INS) for the autonomous mobile vehicles. The presented method has been validated in 3-D environment and is of particular importance for guidance, navigation, and control of mobile, autonomous vehicles. The Extended Kalman Filter (EKF) and the noise characteristic have been modified using the Fuzzy Logic Adaptive System and compared with the performance of regular EKF. It has been demonstrated that the Fuzzy Adaptive Kalman Filter gives better results (more accurate) than the EKF. The presented method is suitable for real-time control and is relatively inexpensive. Also, it applies to fusion process with sensors different than INS or GPS.