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This article presents an adaptive scheme for controlling the end‐effector impedance of robot manipulators. The proposed control system consists of three subsystems: a simple “filter” that characterizes the desired dynamic relationship between the end‐effector position error and the end‐effector/environment contact force, an adaptive controller that produces the Cartesian‐space control input required to provide this desired dynamic relationship, and an algorithm for mapping the Cartesian‐space control input to a physically realizable joint‐space control torque. The controller does not require knowledge of either the structure or the parameter values of the robot dynamics and is implemented without calculation of the robot inverse kinematic transformation. As a result, the scheme represents a general and computationally efficient approach to controlling the impedance of both nonredundant and redundant manipulators. Furthermore, the method can be applied directly to trajectory tracking in free‐space motion by removing the impedance filter. Computer simulation results are given for a planar four degree‐of‐freedom redundant robot under adaptive impedance control. These results demonstrate that accurate end‐effector impedance control and effective redundancy utilization can be achieved simultaneously by using the proposed controller.

Abstract-This two-part paper presents a new approach to predictive analysis for social processes. In Part I, we begin by identifying a class of social processes which are simultaneously important in applications and difficult to predict using existing methods. It is shown that these processes can be modeled within a multi-scale, stochastic hybrid system framework that is sociologically sensible, expressive, illuminating, and amenable to formal analysis. Among other advantages, the proposed modeling framework enables proper characterization of the interplay between the intrinsic aspects of a social process (e.g., the "appeal" of a political movement) and the social dynamics which are its realization; this characterization is key to successful social process prediction. The utility of the modeling methodology is illustrated through a case study involving the global SARS epidemic of 2002-2003. Part II of the paper then leverages this modeling framework to develop a rigorous, computationally tractable approach to social process predictive analysis.

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This article presents two adaptive schemes for compliant motion control of dexterous manipulators. The first scheme is developed using an adaptive impedance control approach for torque-controlled manipulators, whereas the second strategy is an adaptive admittance controller for position-controlled manipulators. The proposed controllers are very general and computationally efficient, as they do not require knowledge of the manipulator dynamic model or parameter values of the manipulator or the environment and are implemented without calculation of the inverse dynamics or inverse kinematic transformation. It is shown that the control strategies are globally stable in the presence of bounded disturbances and that in the absence of disturbances the ultimate bound on the size of the system errors can be made arbitrarily small. The capabilities of the proposed control schemes are illustrated through both computer simulations and laboratory experiments with a dexterous Robotics Research Corporation seven-degrees-of-freedom (DOF) manipulator.