Double inverted pendulum
About: Double inverted pendulum is a research topic. Over the lifetime, 1255 publications have been published within this topic receiving 14028 citations.
Papers published on a yearly basis
29 Oct 2001
TL;DR: Geometric nature of trajectories under the 3D-LIPM and a method for walking pattern generation are discussed, and a simulation result of a walking control using a 12-DOF biped robot model is shown.
Abstract: For 3D walking control of a biped robot we analyze the dynamics of a 3D inverted pendulum in which motion is constrained to move along an arbitrarily defined plane. This analysis yields a simple linear dynamics, the 3D linear inverted pendulum mode (3D-LIPM). Geometric nature of trajectories under the 3D-LIPM and a method for walking pattern generation are discussed. A simulation result of a walking control using a 12-DOF biped robot model is also shown.
TL;DR: In this paper, the authors presented the concept of energy control and showed how robust strategies for swinging up an inverted pendulum are obtained using this idea, and the behavior obtained with the strategy depends critically on the ratio of the maximum acceleration of the pivot to the acceleration of gravity.
Abstract: This paper presents the concept of energy control and shows how robust strategies for swinging up an inverted pendulum are obtained using this idea. The behavior obtained with the strategy depends critically on the ratio of the maximum acceleration of the pivot to the acceleration of gravity. A comparison with with minimum time strategies gives interesting insights into the robustness issues.
16 May 1998
TL;DR: Simulation results show that the biped robot is more stable with the walking pattern generated by the proposed method combined with the controller than with the one by the inverted pendulum mode.
Abstract: This paper proposes a model called the gravity-compensated inverted pendulum mode (GCIPM) to generate a biped locomotion pattern that is similar to the one generated by the linear inverted pendulum mode, but accommodates the free leg dynamics based upon its predetermined trajectory. When the biped locomotion based upon the linear inverted pendulum mode is applied to real biped robots, the stability of the robot is disturbed due to the fact that the neglected dynamics of free legs is not actually negligible, moving the ZMP (zero moment point) away from the presumed fixed point. The GCIPM includes the effect of the dynamics of the free leg in a simple manner This paper also presents a control method for biped robots based upon the computed torque. Simulation results show that the biped robot is more stable with the walking pattern generated by the proposed method combined with the controller than with the one by the inverted pendulum mode.
TL;DR: In this article, a composite control system is designed to stabilize the inverted pendulum and swing the pendulum from the natural pendent position up to the inverted position, which is actually made.
Abstract: This paper relates to the design of a control system for a mechanical system which contains an unstable mode and to an experiment for demonstrating that the control theory may be applied to practical real systems. In this paper the object treated is the control of the pendulum-cart system, which has been studied by many control theorists and engineers as an inverted pendulum problem, and a composite control system is designed not only to stabilize the inverted pendulum but to swing the pendulum from the natural pendent position up to the inverted position, which is actually made. The experimental results are presented.
••12 Jul 2017
TL;DR: In this paper, the authors present a new method of learning control policies that successfully operate under unknown dynamic models by leveraging a large number of training examples that are generated using a physical simulator.
Abstract: We present a new method of learning control policies that successfully operate under unknown dynamic models. We create such policies by leveraging a large number of training examples that are generated using a physical simulator. Our system is made of two components: a Universal Policy (UP) and a function for Online System Identification (OSI). We describe our control policy as universal because it is trained over a wide array of dynamic models. These variations in the dynamic model may include differences in mass and inertia of the robots' components, variable friction coefficients, or unknown mass of an object to be manipulated. By training the Universal Policy with this variation, the control policy is prepared for a wider array of possible conditions when executed in an unknown environment. The second part of our system uses the recent state and action history of the system to predict the dynamics model parameters mu. The value of mu from the Online System Identification is then provided as input to the control policy (along with the system state). Together, UP-OSI is a robust control policy that can be used across a wide range of dynamic models, and that is also responsive to sudden changes in the environment. We have evaluated the performance of this system on a variety of tasks, including the problem of cart-pole swing-up, the double inverted pendulum, locomotion of a hopper, and block-throwing of a manipulator. UP-OSI is effective at these tasks across a wide range of dynamic models. Moreover, when tested with dynamic models outside of the training range, UP-OSI outperforms the Universal Policy alone, even when UP is given the actual value of the model dynamics. In addition to the benefits of creating more robust controllers, UP-OSI also holds out promise of narrowing the Reality Gap between simulated and real physical systems.