This paper proposes walk pattern modeling for powered exoskeleton based on complex-valued neural network and reports its validity through experiments. We have been developing a powered exoskeleton to support workers at a nuclear power plant in time of hazard. The objective of the powered exoskeleton is to support a worker wearing a heavy radiation protection suit. We believe that conventional reactive power assist control based on EMG sensors is not feasible because they fail to measure the worker muscle activity robustly in the radiation protection suit as the worker has a lot of sweat in the high temperature and the humidity in the suit. Therefore, we have developed feed-forward control to assist the worker's motion based on the recognition of the worker's motion. Our previous studies use a simple k-nearest neighbor algorithm to model the motion of the powered exoskeleton, however, the algorithm is not for online learning and the estimated trajectory is not as smooth as we expected. This paper proposes a new modeling of walk motion of the powered exoskeleton based on a complex-valued neural network. The complex-valued neural network generally has good properties on learning speed and stability. This paper shows its validity for the modeling of powered exoskeleton's walk through experiments.

When an endpoint of a manipulator is moving in touch with a rigid smooth surface, the manipulator should be controlled to achieve both a desired position trajectory of the endpoint and a desired contact force at the contact point. In this paper, we deal with a hybrid controller of repetitive learning control and model-based adaptive control for geometrically constrained robot manipulator. The convergence of joint angular errors and contact force errors is proved under an appropriate initial condition and the smoothness of the constraint surface. Furthermore, by the simulation experiment, we show that the proposed hybrid controller makes 2-link constrained manipulator track a desired position and a desired constraint force trajectory

The paper is aimed at presenting a physical interpretation of practice-based learning (so-called "iterative learning control") for robotic tasks from the viewpoint of "bettering impedance matching". At first, the concepts of impedance and impedance matching that are inherent to linear electric circuits are generalized for a class of nonlinear dynamics including robotic tasks by means of passivity. It is then shown in the simplest case when the tool endpoint is free to move that a simple iterative scheme of learning enables robots to make a progressive advance in a sense of zero-impedance matching at every trial of operation. In the case of impedance control when a soft and deformable finger-tip presses a rigid object or environment, it is shown that, for a given desired periodic force, physical interaction between the soft fingertip and the rigid object, the robot learns steadily the desired task by monotonously increasing the grade of impedance matching pertaining to dynamics of the robot task with controller dynamics.

Robot dynamics under static and Coulomb frictions are shown to be equivalent to a nonlinear position-dependent circuit including a set of on-off switches and analyzed as a variable structure system. It is shown that, under the existence of static and Coulomb frictions at each joint, an ordinary PD feedback with gravity compensation, for set-point position control leads to a trapping of motion at some immovable state within a finite time without reaching the given target position. On the contrary, by introducing regressors for uncertain parameters of gravity forces and static and Coulomb frictions it is possible to show that a proper update law of such regressors together with an adequate PD feedback renders the target state of the robot system globally, asymptotically stable without incurring any offset and without measuring any force/torque signals. It is shown that regressors can be treated as an operator with positivity and thereby regarded as a time-varying capacitor. These observations suggest a proposal of robots that are subject to only the law of inertia, that is, a proposal of inertia-only robots or gravity/friction-free robots.

Learning control and model-based adaptive control schemes are proposed for coordinated control of multiple manipulators when physical parameters such as inertia moments and position of the mass center of a target object are unknown. The proposed schemes are constructed on the equation of motion of the coordinated multiple manipulators, which is derived in terms of joint angle coordinates of manipulators and includes the dynamics of the target object. Since this equation does not explicitly include the position of the mass center of the object, the proposed schemes can achieve exact tracking of given joint angle trajectories and given trajectories of "internal force" without using physical parameters of the manipulators and the object. The effectiveness of the proposed schemes is illustrated by the numerical simulation results on a model of two 3-DOF manipulators holding a single object.

Learning control and model-based adaptive control schemes are proposed for coordinated control of multiple manipulators holding a geometrically constrained object whose physical parameters such as inertia moments and position of the mass center are unknown. The proposed schemes are constructed on the equation of motion of the coordinated multiple manipulators which is derived in terms of joint angle coordinates of manipulators and includes the dynamics of the target object. Since this equation does not explicitly include the position of the mass center of the object, the proposed schemes can achieve exact tracking of given joint angle trajectories and given trajectories of "internal force" and external constraint force without using physical parameters of the manipulators and the object.

This paper reports comparative experiments with a new model-based adaptive force control algorithm for robot arms. This controller provides simultaneous position and force trajectory tracking of a robot arm whose tool tip is in point contact with a smooth rigid surface. The algorithm is provably stable with respect to the commonly accepted rigid-body nonlinear dynamical model for robot arms. Comparative experiments show the new adaptive model-based controller to provide performance superior to that of both nonmodel-based controllers and nonadaptive controllers over a wide range of operating conditions.

A learning controller for coordination of multiple manipulators holding an object rigidly is proposed based on the principle of joint-space orthogonalization. It is devised by referring to (1) the fact that dynamics of multiple manipulators resembles that of a single robot under geometric endpoint constraint except the term of the sum of forces exerted from manipulators to the object, (2) the passivity of the dynamics of multiple manipulators, (3) the use of feedback signals of velocity error and force error which are projected to their respective manifolds to be orthogonal to each other in joint-space, and (4) the passivity of the total error system. The convergence of tracking position and force errors is proved theoretically provided that multiple manipulators hold the object rigidly and are initialized at the same posture at the beginning of each learning trial. The effectiveness of this controller is ascertained by computer simulation by using two manipulators with three joints.

This paper reports comparative experiments with a provably correct model-based adaptive robot control algorithm for simultaneous position and force trajectory tracking of a robot arm whose gripper is in point contact with a smooth surface. The experiments show the new adaptive model-based offers performance superior to that of its non-model-based counterpart over a wide variety of operating conditions.

A learning control scheme for a class of robot manipulators whose endpoint is moving under geometrical constraints on a surface is proposed. In this scheme, the input torque command is composed of two different signals updated separately at every trial by different ways. One is updated by the angular velocity error vector which is projected to the tangent plane of the constraint surface in joint space. The other is updated by the magnitude of contact force error at the manipulator endpoint. Not only the uniform boundedness of position and velocity trajectory errors but also the uniform convergence of position and velocity trajectories to their desired ones with repeating practices are proved theoretically. In addition, it is shown that the contact force itself converges to the desired one in the sense of L/sup 2/-norm with repeating practices. Computer simulation results by using a 3 DOF manipulator are presented to demonstrate the effectiveness of the proposed method and to examine the speed of convergence of force trajectories besides position and velocity trajectories.>

A learning control scheme for robot manipulators whose endpoint is moving under geometrical constraints on a surface is proposed. In this scheme, the input torque command is composed of two signals updated separately at every trial by different laws. One is updated by the angular velocity error vector which is projected to the tangent plane of the constraint surface in joint space. The other is updated by the magnitude of contact force error at the endpoint. A theoretical proof of the uniform boundedness of velocity and position trajectories and the convergence of these to their desired ones is given, together with the convergence of force errors. Computer simulation remits by using a 3-DOF manipulator are presented to demonstrate the effectiveness of the proposed method.>

When multiple manipulators are going to manipulate one target object coordinately, it is necessary to control both the motion of the object and the forces exerted at the endpoints of manipulators. It is important particularly to control the so-called internal force which does not affect the motion of the object. In this paper, a model-based adaptive control method for such coordinated control of multiple manipulators is proposed. The proposed method is a natural extension of the adaptive control scheme for geometrically endpoint constrained manipulators. It is proved theoretically that both the desired position trajectory of the object and the internal force converge to the desired ones respectively. The effectiveness of the proposed method is demonstrated by computer simulation using two planar 3-DOF manipulators.>

A learning control scheme for class of robot manipulators whose endpoint is moving under geometrical constraints on a surface is proposed. In this scheme, the input torque command is composed of two different signals updated separately at every trial by different laws. One is updated by the angular velocity error vector which is projected to the tangent plane of the constraint surface in joint space. The other is updated by the magnitude of contact force error at the manipulator endpoint. A theoretical proof of the uniform boundedness of velocity and position trajectory errors and the convergence of their desired trajectories is given. In addition, a theoretical proof of the convergence of force errors when velocity and position trajectories are in a neighborhood of the desired ones is presented. Computer simulation results for a 3 DOF manipulator are given.

A theory of training-based learning control is developed for a class of robotic tasks under geometric endpoint constraints. An algorithm for updating the control input which makes the next input consist of the previous input plus modified terms of previous velocity and force errors at the robot endpoint constrained on a surface is proposed. Simulation results are presented to demonstrate the convergence of position and force tracking to a desired path with force specified on the surface. It is shown that the robot dynamics satisfies the passivity condition regarding the joint torque input vector versus the joint velocity vector, even in the case of geometric constraints. A theoretical proof of the convergence of position and force errors is given. In the proof, a relaxed concept of passivity of error dynamics of robot arms plays a crucial role.>

A class of learning control algorithms with a forgetting factor 1> alpha >0 and without differentiation of velocity signals is proposed, which updates the input by u/sub k+1/=(1- alpha ) u/sub k/+ alpha u/sub 0/+ Phi e/sub k/, where u/sub k/ and e/sub k/ stand for command input and velocity error at kth exercise, respectively. The robustness of this learning control with respect to reinitialization errors, fluctuation of dynamics, and measurement noise is studied. It is shown that the exponential passivity of displacement robot dynamics plays a crucial role in the uniform boundedness of transient behaviors and the convergence in the progress of learning. A method called selective learning, which updates u/sub 0/ in the long-term memory by selecting the best command among the past several trials, is proposed. It is claimed that this method accelerates the speed of convergence.>

When multiple mobile robots are working in the same environment, planning of collision-free coordinated motion is necessary; here, an algorithm for planning such a motion of two mobile robots, no matter how crude the constraints of obstacles are, is proposed. The situation is modeled as a Petri net, which is considered as a useful model for describing and analyzing a system in which it is possible for some events to occur concurrently but there are constraints on the concurrence. In the Petri net, all motion constraints of robots in their paths are arranged as its firing rules, and hence collision-free coordination between the robots can be easily planning by manipulation of the firing rules. The algorithm always finds a collision-free coordinated path of two robots if there actually exists such a path in the environment. Moreover, because the algorithm does not use any knowledge of movement of the robots, precise time-varying trajectory control is not required and realization of the coordination is easy. The algorithm works efficiently even in a complex environment, indebted to the generic properties of geographical quadtree modeling for the environment. The usefulness of the algorithm is shown by several simulations.>

A motion-planning algorithm is proposed which fulfils its function fast even if shapes of the robot and its obstacles are complicated. Considering the global obstacle allocation in the robot workspace, the proposed algorithm selects intermediate positions where the mobile robot should pass from a start position to a goal position. Using a systematic motion generation method based on the closest points between the robot and its obstacles, the algorithm generated collision-free robot motions to joint the intermediate positions successively. The algorithm runs on the quadtree representation, obtained from fast conversion of a real image taken through a camera on the ceiling of the workspace. The algorithm can generate collision-free motions while following a change of obstacle allocation. In a comparison with several motion-planning algorithms, it is shown that the proposed algorithm generates fast collision-free robot motions.>