scholarly journals On the Optimal Adhesion Control of a Vortex Climbing Robot

2021 ◽  
Vol 102 (3) ◽  
Author(s):  
Andreas Papadimitriou ◽  
George Andrikopoulos ◽  
George Nikolakopoulos
Keyword(s):  
Adhesion Force ◽  
Moving Average ◽  
External Input ◽  
Lookup Table ◽  
System Stability ◽  
State Transitions ◽  
Geometrical Properties ◽  
Ducted Fan ◽  
Time Optimal ◽  
Adhesion Control

AbstractThis article tackles the challenge of negative pressure adhesion control of a Vortex Robotic (VR) platform, which utilizes a modified Electric Ducted Fan (EDF)-based design for successfully adhering to surfaces of variable curvature. The resulting Vortex Actuation (VA) system is estimated through a switching Autoregressive-Moving-Average with eXternal input (ARMAX) identification, for accurately capturing the throttle to adhesion force relationship throughout its operating range. For safe attachment of the robot on a surface, the critical adhesion is modeled based on the geometrical properties of the robotic platform for providing the required reference forces. Within this work, an explicit controller via the use of a Constraint Finite Time Optimal Control (CFTOC) approach is designed in an offline manner, which results in a lookup table realization that ensures overall system stability in all state transitions. In an effort to further improve the tracking response for arbitrary setup orientations, the adhesion control scheme is extended through a switching EMPC framework. The resulting frameworks are tested through both dynamic simulation and experimental sequences involving: a) adhesion control on a rotating test curved surface and, b) adhesion and locomotion sequences on a water pipe.

Design of a wheeled wall climbing robot based on the performance of bio-inspired dry adhesive material

Robotica ◽  
2021 ◽  
pp. 1-14
Author(s):  
Hongkai Li ◽  
Xianfei Sun ◽  
Zishuo Chen ◽  
Lei Zhang ◽  
Hongchao Wang ◽  
...  
Keyword(s):  
Flat Surface ◽  
Thrust Force ◽  
Adhesion Force ◽  
Structure Design ◽  
Design Parameters ◽  
Climbing Robot ◽  
Adhesive Material ◽  
Ducted Fan ◽  
The Stability ◽  
Belt System

Abstract Inspired by gecko’s adhesive feet, a wheeled wall climbing robot is designed in this paper with the synchronized gears and belt system acting as the wheels by considering both motion efficiency and adhesive capability. Adhesion of wheels is obtained by the bio-inspired adhesive material wrapping on the outer surface of wheels. A ducted fan mounted on the back of the robot supplies thrust force for the adhesive material to generate normal and shear adhesion force whilemoving on vertical surfaces. Experimental verification of robot climbing on vertical flat surface was carried out. The stability and the effect of structure design parameters were analyzed.

Biologically Modeled Noise Stabilizing Neurodynamics for Pattern Recognition

1998 ◽  
Vol 08 (02) ◽  
pp. 321-345 ◽  
Author(s):  
Hung-Jen Chang ◽  
Walter J. Freeman ◽  
Brian C. Burke
Keyword(s):  
Gaussian Noise ◽  
Olfactory System ◽  
Additive Noise ◽  
Neural System ◽  
External Input ◽  
State Transitions ◽  
Anterior Olfactory Nucleus ◽  
Significant Stimulus ◽  
Unpredictable Environments ◽  
Hybrid Dynamics

We present a distributed KIII model for the olfactory neural system. Low-level Gaussian noise is introduced to the receptors and anterior olfactory nucleus, which biologically models the peripheral and central sources of noise. The additive noise numerically makes the model stable and robust in respect to repeated input-induced state transitions, while improving the simulations of EEG potentials and multiunit activity from the olfactory system. This hybrid dynamics generates a 1/f aperiodic state, which provides an unpatterned basal state for every module to stay in while there is no significant stimulus. Any external input may guide the system to a certain patterned state. The mechanism is fast, fully parallel, under modulatory control, and flexible in absorbing new patterns from unpredictable environments.

Predictive Algorithm of Network Delay Based on Robust Kalman Filter

2014 ◽  
Vol 631-632 ◽  
pp. 937-940
Author(s):  
Fang He ◽  
Hui Zhang ◽  
Bing Bing Li
Keyword(s):  
Network Performance ◽  
Moving Average ◽  
Arma Model ◽  
Estimation Algorithm ◽  
Networked Control System ◽  
System Stability ◽  
Network Delay ◽  
Predictive Algorithm ◽  
Parameter Estimation Algorithm ◽  
Set Up

Due to irregular information flow for NCS (Networked Control System), network delay has performance of random and variability. It reduces system stability, network performance and control performance. This paper focuses on research of predictive algorithm of network delay. Network delay data is obtained in PROFIBUS-DP. Based on network delay data, the ARMA (Auto-Regressive and Moving Average) model of delay is set up. The parameter estimation algorithm of Robust Kalman is used to estimate parameters of proposed ARMA model of network delay. A simulation example is given and verifies efficiency of predictive algorithm proposed.

scholarly journals Stability Analysis of a Proximate Time Optimal Controlled Electrostatic Suspension System Using Piezo Actuator via Lyapunov Functional Approach

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
T. T. Le
Keyword(s):  
Optimal Control ◽  
Silicon Wafer ◽  
Actuator Saturation ◽  
Functional Approach ◽  
Time Optimal Control ◽  
System Stability ◽  
Piezo Actuator ◽  
Constant Voltage ◽  
Mechanical Contact ◽  
Time Optimal

An electrostatic suspension of silicon wafer using proximate time optimal control has been successfully developed. In this system, the movable electrodes which are supplied by constant voltage and actuated by the piezoelectric (PZT) actuator are used instead of stationary electrodes like previous systems. The changing of the gap length between movable electrodes and the suspended object will create varying capacitances that can control the electrostatic forces. To overcome the problem of actuator saturation of the piezo actuator, a proximate time optimal control is used to stabilize the system. The system stability is theoretically investigated using Lyapunov’s function. The constant voltage supplying to the electrode is an important parameter, and it is also determined. The paper presents series of the simulation and experimental results that prove completely suspension of 4-inch silicon wafer without any mechanical contact.

scholarly journals Recurrent Infomax Generates Cell Assemblies, Neuronal Avalanches, and Simple Cell-Like Selectivity

2009 ◽  
Vol 21 (4) ◽  
pp. 1038-1067 ◽  
Author(s):  
Takuma Tanaka ◽  
Takeshi Kaneko ◽  
Toshio Aoyagi
Keyword(s):  
Learning Algorithm ◽  
Recurrent Network ◽  
External Input ◽  
Point Of View ◽  
State Transitions ◽  
Natural Scenes ◽  
Wide Range ◽  
Theoretic Point

Recently multineuronal recording has allowed us to observe patterned firings, synchronization, oscillation, and global state transitions in the recurrent networks of central nervous systems. We propose a learning algorithm based on the process of information maximization in a recurrent network, which we call recurrent infomax (RI). RI maximizes information retention and thereby minimizes information loss through time in a network. We find that feeding in external inputs consisting of information obtained from photographs of natural scenes into an RI-based model of a recurrent network results in the appearance of Gabor-like selectivity quite similar to that existing in simple cells of the primary visual cortex. We find that without external input, this network exhibits cell assembly–like and synfire chain–like spontaneous activity as well as a critical neuronal avalanche. In addition, we find that RI embeds externally input temporal firing patterns to the network so that it spontaneously reproduces these patterns after learning. RI provides a simple framework to explain a wide range of phenomena observed in in vivo and in vitro neuronal networks, and it will provide a novel understanding of experimental results for multineuronal activity and plasticity from an information-theoretic point of view.

Stability analysis of a time-optimally controlled electrostatic suspension system and suspension experiments in a vacuum

2010 ◽  
Vol 225 (1) ◽  
pp. 88-100 ◽  
Author(s):  
T T Le ◽  
J U Jeon
Keyword(s):  
Low Cost ◽  
Electromagnetic Levitation ◽  
Dielectric Materials ◽  
Suspension System ◽  
System Stability ◽  
Suspension Systems ◽  
Control Scheme ◽  
Time Optimal ◽  
Bang Bang Control ◽  
Work First

Electrostatic suspension permits conductive, semiconductive, and dielectric materials to be supported without mechanical contact, in contrast to electromagnetic levitation by which only ferromagnetic materials can be levitated. To expand applications of electrostatic suspension systems, a low-cost electrostatic suspension system using a time optimal bang—bang control scheme where linear analogue high-voltage amplifiers that are costly and bulky are not employed has already been implemented. In this article, a time optimal bang—bang control scheme is used to stabilize the system like the previous work. First, the process to find the recoverable set for all the states in which a time optimal bang—bang control exists is described in detail. Then, the switching criterion for the suspension system is derived by using a backward integration technique and the system stability is theoretically investigated using Lyapunov's function as well. To experimentally verify the system stability in vacuum, suspension experiments are carried out with 3.5 in aluminium discs in a vacuum environment. Experiments in the atmosphere are also conducted for comparison with the results in the vacuum. The experimental results show that an aluminium disc has been stably suspended at a reference gap length of 300 μm in a vacuum environment.

Design of Robust High-Speed Motion Controller for a Plant With Actuator Saturation

1998 ◽  
Vol 122 (3) ◽  
pp. 535-541 ◽  
Author(s):  
Hyun-Taek Choi ◽  
Bong Keun Kim ◽  
Il Hong Suh ◽  
Wan Kyun Chung
Keyword(s):  
High Speed ◽  
Actuator Saturation ◽  
System Stability ◽  
Internal Stability ◽  
Control Input ◽  
Motion Controller ◽  
Time Optimal ◽  
Handling Methods ◽  
The Stability ◽  
Integrator Model

A robust high-speed motion controller is proposed. The proposed controller consists of the proximate time optimal servomechanisms (PTOS) for high-speed motion, disturbance observer (DOB) for robustness, friction compensator, and saturation handling element. In the proposed controller, DOB basically provides the chance to apply PTOS to nondouble integrator systems by drastically reducing disturbances as well as unwanted signals due to difference between real system and the double integrator model. But, in DOB-based systems, if control input is saturated due to control input of PTOS and/or DOB, overall system stability cannot be guaranteed, which is first found and analyzed in this paper. To solve this problem, robust stability and internal stability conditions of DOB-based system are derived. It is also shown that DOB could violate the internal stability, when the control input is saturated. Eventually, a simple saturation handling element is inserted to maintain internal stability of overall system. Also, we explain that our two saturation handling methods, i.e., Additional Saturation Element (ASE) and Self Adjusting Saturation (SAS) are the equivalent solutions of saturation problem to maintain internal stability. The stability and performances of the proposed controller are verified through numerical simulations and experiments using a precision linear motor system. [S0022-0434(00)01103-5]

Identification of optimal strategies for state transition of complex biological networks

2017 ◽  
Vol 45 (4) ◽  
pp. 1015-1024 ◽  
Author(s):  
Meichen Yuan ◽  
Weirong Hong ◽  
Pu Li
Keyword(s):  
Biological Networks ◽  
State Transition ◽  
Optimal Strategies ◽  
Optimization Method ◽  
State Transitions ◽  
Decision Variable ◽  
Parameter Perturbation ◽  
Time Optimal ◽  
Method Optimization ◽  
Complex Biological Networks

Complex biological networks typically contain numerous parameters, and determining feasible strategies for state transition by parameter perturbation is not a trivial task. In the present study, based on dynamical and structural analyses of the biological network, we optimized strategies for controlling variables in a two-node gene regulatory network and a T-cell large granular lymphocyte signaling network associated with blood cancer by using an efficient dynamic optimization method. Optimization revealed the critical value for each decision variable to steer the system from an undesired state into a desired attractor. In addition, the minimum time for the state transition was determined by defining and solving a time-optimal control problem. Moreover, time-dependent variable profiles for state transitions were achieved rather than constant values commonly adopted in previous studies. Furthermore, the optimization method allows multiple controls to be simultaneously adjusted to drive the system out of an undesired attractor. Optimization improved the results of the parameter perturbation method, thus providing a valuable guidance for experimental design.

Sign in / Sign up

Export Citation Format

Share Document