The Balancing Cube: A Dynamic Sculpture As Test Bed for Distributed Estimation and Control

The balancing cube is a dynamic sculpture that can balance autonomously on any of its edges or corners (see Figures 14). When standing on a corner, the cube represents a three-dimensional (3-D) inverted pendulum with multiple actuation, sensing, and control units that are interconnected over a communication network. The main structural components are the cube body (a rigid aluminum structure with a cubic shape) and six identical rotating arms located on each of the cube's inner faces. The rotating arms are self-contained units carrying sensors, actuation, a computer, and a battery. Due to their modular design, these units are referred to as modules. As they rotate, they shift the overall center of mass of the system, exert forces on the cube structure, and can, as a result, influence the cube's motion. The modules constitute the agents in the distributed and networked control system; their joint objective is the stabilization of the cube. A video of the cube can be found on the project Web site [1].

[1]  Dennis S. Bernstein,et al.  Stabilization of a 3D axially symmetric pendulum , 2008, Autom..

[2]  Denis V. Efimov,et al.  Robust and Adaptive Observer-Based Partial Stabilization for a Class of Nonlinear Systems , 2009, IEEE Transactions on Automatic Control.

[3]  Dennis S. Bernstein,et al.  Asymptotic Smooth Stabilization of the Inverted 3-D Pendulum , 2009, IEEE Transactions on Automatic Control.

[4]  Mark W. Spong,et al.  Underactuated mechanical systems , 1998 .

[5]  Raffaello D'Andrea,et al.  An Experimental Demonstration of a Distributed and Event-Based State Estimation Algorithm , 2011 .

[6]  A. Goldsmith,et al.  Wireless network design for distributed control , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[7]  J.M. Fowler,et al.  A formation flight experiment , 2003, IEEE Control Systems.

[8]  George W. Irwin,et al.  Wireless networked control systems with QoS-based sampling , 2007 .

[9]  Raffaello D'Andrea,et al.  The Distributed Flight Array , 2011 .

[10]  Junmin Li,et al.  Decentralized Output-Feedback Neural Control for Systems With Unknown Interconnections , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[11]  Seul Jung,et al.  Control Experiment of a Wheel-Driven Mobile Inverted Pendulum Using Neural Network , 2008, IEEE Transactions on Control Systems Technology.

[12]  João Pedro Hespanha,et al.  A Survey of Recent Results in Networked Control Systems , 2007, Proceedings of the IEEE.

[13]  Rong-Jong Wai,et al.  Design of Cascade Adaptive Fuzzy Sliding-Mode Control for Nonlinear Two-Axis Inverted-Pendulum Servomechanism , 2008, IEEE Transactions on Fuzzy Systems.

[14]  Iven M. Y. Mareels,et al.  Decentralized control design of interconnected chains of integrators: A case study , 2008, Autom..

[15]  P. Horacek Laboratory Experiments for Control Theory Courses: A Survey , 2000 .

[16]  Thierry Floquet,et al.  Second‐order sliding mode control of underactuated mechanical systems I: Local stabilization with application to an inverted pendulum , 2008 .

[17]  Tongwen Chen,et al.  A new method for stabilization of networked control systems with random delays , 2005 .

[18]  Daniela Rus,et al.  Modular Robot Systems , 2010, IEEE Robotics & Automation Magazine.

[19]  Nandit Soparkar,et al.  Trading computation for bandwidth: reducing communication in distributed control systems using state estimators , 2002, IEEE Trans. Control. Syst. Technol..

[20]  A. Stubbs,et al.  Multivehicle systems control over networks: a hovercraft testbed for networked and decentralized control , 2006, IEEE Control Systems.

[21]  R.M. Murray,et al.  MVWT-II: the second generation Caltech Multi-Vehicle Wireless Testbed , 2004, Proceedings of the 2004 American Control Conference.

[22]  Raffaello D'Andrea,et al.  Numerical models and controller design parameters for the balancing cube , 2012 .

[23]  N. McClamroch,et al.  Mathematical Models for the Triaxial Attitude Control Testbed , 2003 .

[24]  Dominique Bonvin,et al.  Global stabilization of an inverted pendulum-Control strategy and experimental verification , 2009, Autom..

[25]  J. Wen,et al.  Nonlinear Model Predictive Control for the Swing-Up of a Rotary Inverted Pendulum , 2004 .

[26]  Raffaello D'Andrea,et al.  Reduced communication state estimation for control of an unstable networked control system , 2011, IEEE Conference on Decision and Control and European Control Conference.

[27]  A. Nazli Gündes,et al.  Low order decentralized stabilizing controller design for a mobile inverted pendulum robot , 2009, 2009 American Control Conference.

[28]  J. Farrell,et al.  The global positioning system and inertial navigation , 1999 .

[29]  Sebastian Trimpe,et al.  A limiting property of the matrix exponential with application to multi-loop control , 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.

[30]  Rolf Findeisen,et al.  Stabilizing nonlinear predictive control over non deterministic communication networks , 2008 .

[31]  J. E. Luntz,et al.  A distributed control system for flexible materials handling , 1997 .

[32]  Vijay Kumar,et al.  Experimental Testbed for Large Multirobot Teams , 2008, IEEE Robotics Autom. Mag..

[33]  Rong-Jong Wai,et al.  Adaptive stabilizing and tracking control for a nonlinear inverted-pendulum system via sliding-mode technique , 2006, IEEE Trans. Ind. Electron..

[34]  John J. Craig Zhu,et al.  Introduction to robotics mechanics and control , 1991 .

[35]  Katsuhisa Furuta,et al.  Swinging up a pendulum by energy control , 1996, Autom..

[36]  Sebastian Trimpe,et al.  Accelerometer-based tilt estimation of a rigid body with only rotational degrees of freedom , 2010, 2010 IEEE International Conference on Robotics and Automation.

[37]  Russ Tedrake,et al.  Simulation-based LQR-trees with input and state constraints , 2010, 2010 IEEE International Conference on Robotics and Automation.

[38]  Kaustubh Pathak,et al.  Velocity and position control of a wheeled inverted pendulum by partial feedback linearization , 2005, IEEE Transactions on Robotics.

[39]  Bart De Schutter,et al.  Online least-squares policy iteration for reinforcement learning control , 2010, Proceedings of the 2010 American Control Conference.

[40]  B. Anderson,et al.  Optimal control: linear quadratic methods , 1990 .

[41]  D. Luenberger An introduction to observers , 1971 .

[42]  Raffaello D'Andrea,et al.  Optimization-based iterative learning control for trajectory tracking , 2009, 2009 European Control Conference (ECC).

[43]  N. Harris McClamroch,et al.  Asymptotic Stabilization of the Inverted Equilibrium Manifold of the 3-D Pendulum Using Non-Smooth Feedback , 2009, IEEE Transactions on Automatic Control.

[44]  Huijun Gao,et al.  Stabilization of Networked Control Systems With a New Delay Characterization , 2008, IEEE Transactions on Automatic Control.

[45]  Brett Ninness,et al.  Nonlinear model predictive control of an inverted pendulum , 2009, 2009 American Control Conference.