Function Approximation Technique Based Adaptive Control for Chaos Synchronization between Different Systems with Unknown Dynamics

This paper presents a function approximation technique based immersion and invariance adaptive controller for chaos synchronization between nonidentical systems with unknown dynamics. In the proposed control scheme, the control system is reconstructed as the combination of a controllable linear system and a variation term from the original system. The variation term is treated as time-varying uncertainty and approximated by a group of weighted chosen basis functions. The immersion and invariance methodology is employed to design the adaptive control law such that both the synchronization error and uncertainty estimation error converge to zero. Two typical chaos synchronization problems are used in numerical simulations to verify the effectiveness and superiority of the proposed controller.

     

Motion Planning Algorithms for a Rolling Sphere With Limited Contact Area

The paper deals with the motion planning problem for a rolling sphere with limited contact area. The system under consideration is represented by a hemispherical object that can roll without slipping or spinning on the plane. Under the constraints imposed on the size of the contact area, the construction of motion can be regarded as a problem of parallel parking in a finite number of movement steps. A motion strategy, realizing the movement steps by tracing generalized figure eights on the hemisphere, is introduced. Two different algorithms for this motion strategy, the circle-based and the generalized Viviani-curve-based ones, are proposed. The convergence of the algorithms is analyzed, and the computational feasibility of these algorithms is verified under simulation.     

On the dynamic version of the minimum hand jerk criterion

This paper is concerned with the problem of trajectory formation of humanlike reaching movements. First, we review conventional criteria of optimality adopted in robotics and computational neuroscience for the prediction of reaching movements and formulate a dynamic version of the minimum hand jerk criteria. We call it a minimum driving force change criterion and check its performance for the free‐space movements. Next, we test the performance of the new criterion for the movements where the human hand is geometrically constrained by the external environment, and for the movements with flexible objects. The main feature of these movements is that the hand velocity profiles are not always bell shaped. Our simulations and initial experimental results show that the minimum driving force change criterion can roughly capture this feature and, therefore, can be a reasonable candidate for modeling of humanlike reaching movements. © 2005 Wiley Periodicals, Inc.     

Reaching movements in dynamic environments: how do we move flexible objects?

The paper presents an analysis of human reaching movements in manipulation of flexible objects. To predict the trajectory of human hand, a minimum crackle criterion has been recently introduced in literature. A different approach is explored in this paper. To explain the trajectory formation, we resort to the minimum hand jerk criterion. First, we show that this criterion matches well experimental data available in literature. Next, we argue that, contrary to the minimum crackle criterion, the minimum hand jerk criterion produces bounded hand velocity profiles for multimass flexible objects. Finally, we present initial experimental results confirming the applicability of the minimum hand jerk criterion to the prediction of reaching movements with multimass objects.     

Learning control of autonomous robots using an instance-based classifier generator in continuous state space

A classifier system for the reinforcement learning control of autonomous mobile robots is proposed. The classifier system contains action selection, rules reproduction, and credit assignment mechanisms. An important feature of the classifier system is that it operates with continuous sensor and action spaces. The system is applied to the control of mobile robots. The local controllers use independent classifiers specified at the wheel-level. The controllers work autonomously, and with respect to each other represent dynamic systems connected through the external environment. The feasibility of the proposed system is tested in an experiment with a Khepera robot. It is shown that some patterns of global behavior can emerge from locally organized classifiers. This work was presented, in part, at the Third International Symposium on Artificial Life and Robotics, Oita, Japan, January 19–21, 1998     

Dynamic contact sensing by flexible beam

This paper discusses a new dynamic sensing system capable of detecting the contact point between a flexible beam and an object. The proposed sensing system, named dynamic antenna, is simply composed of an insensitive flexible beam, a torque sensor, a joint position sensor, an actuator, and a payload at the tip end of the beam. The contact point can be detected through estimation of the oscillation frequencies of the beam in contact with the object. First, a dynamic model of the sensor is derived. Next, it is shown that information of the fundamental and the second natural frequencies is sufficient for unique determining of the contact point if the beam has uniform mass and stiffness distribution. In practical realization, the fundamental and the second natural frequencies of the beam in contact with the object are extracted from the torque sensor measurements with the use of the maximum entropy method. Then, the frequencies are mapped into the contact-point coordinate. Extraction of the frequencies and mapping them into the contact point constitute a sensing strategy which is tested under experiment