A Sampling Approach to Modeling and Control of a Large-Size Robot Population

2010 ◽  
Author(s):  
Dejan Milutinovic´ ◽  
Devendra P. Garg
Keyword(s):  
Stochastic Process ◽  
Optimal Control ◽  
Close Relation ◽  
Modeling And Control ◽  
Stochastic Sampling ◽  
Large Size ◽  
Continuous Components ◽  
Estimation And Control ◽  
The Individual ◽  
And Control

Motivated by the close relation between estimation and control problems, we explore the possibility to utilize stochastic sampling for computing the optimal control for a large-size robot population. We assume that the individual robot state is composed of discrete and continuous components, while the population is controlled in a probability space. Utilizing a stochastic process, we can compute the state probability density function evolution, as well as use the stochastic process samples to evaluate the Hamiltonian defining the optimal control. The proposed method is illustrated by an example of centralized optimal control for a large-size robot population.

Applied Optimal Control: Optimization, Estimation, and Control

1979 ◽  
Vol 9 (6) ◽  
pp. 366-367 ◽  
Author(s):  
Arthur E. Bryson ◽  
Yu-Chi Ho ◽  
George M. Siouris
Keyword(s):  
Optimal Control ◽  
Control Optimization ◽  
Estimation And Control ◽  
And Control

Strongly consistent estimation in a controlled Markov renewal model

1982 ◽  
Vol 19 (03) ◽  
pp. 532-545 ◽  
Author(s):  
Michael Kolonko
Keyword(s):  
Optimal Control ◽  
Dynamic Models ◽  
Consistent Estimation ◽  
Unknown Parameters ◽  
Control Approach ◽  
Renewal Model ◽  
Countable State ◽  
Estimation And Control ◽  
And Control ◽  
Strongly Consistent

The optimal control of dynamic models which are not completely known to the controller often requires some kind of estimation of the unknown parameters. We present conditions under which a minimum contrast estimator will be strongly consistent independently of the control used. This kind of estimator is appropriate for the adaptive or ‘estimation and control' approach in dynamic programming under uncertainty. We consider a countable-state Markov renewal model and we impose bounding and recurrence conditions of the so-called Liapunov type.

ENSURING REACH ABILITY AND STABILITY IN THE SYNTHESIS OF ROBUST DISCRETE MODEL PREDICTIVE CONTROL IN CONDITIONS OF INCOMPLETE INFORMATION

2020 ◽  
Vol 7 (2) ◽  
pp. 29-33
Author(s):  
NGUYEN KHAC TUNG ◽  
◽  
ANTON ZHILENKOV ◽  
DANG BINH KHAC ◽  
Keyword(s):  
Optimal Control ◽  
Current Control ◽  
Initial State ◽  
Modeling And Control ◽  
Nanoscale Structures ◽  
Current State ◽  
Control Scheme ◽  
Methods Of Synthesis ◽  
And Control ◽  
Time Systems

Methods of synthesis of control of multiscale processes with predictive models for linear discrete time systems are considered. A description is given of a control scheme in which the current control action is obtained by solving at each instant of the sample the optimal control problem with a finite horizon without feedback and using the current state of the object as an initial state. An optimization problem is described that gives an optimal control sequence when the control obtained for the first step of the subsequent sequence is applied to the object. The analysis of the reachability and stability problems of synthesized controls with a predictive model under conditions of disturbances and uncertainties is given. As well as the problems of providing preset indicators of the quality of management and comparing indicators in the management of MPC in open and closed systems. The urgent issues requiring research in the framework of the considered management system are identified. The proposed solutions are extremely relevant to the problems of modeling and control of technological processes of growing nanoscale structures.

Dynamic Modeling and Control of Dual Wheeled Mobile Robots Compliantly Coupled to a Common Payload

1999 ◽  
Vol 121 (3) ◽  
pp. 457-461 ◽  
Author(s):  
Thurai Vinay ◽  
Bradley Postma ◽  
Theo Kangsanant
Keyword(s):  
Mobile Robots ◽  
Pole Placement ◽  
Nonlinear Controller ◽  
Wheeled Mobile Robots ◽  
Adaptive Controllers ◽  
Modeling And Control ◽  
Self Tuning ◽  
The Individual ◽  
And Control ◽  
Placement Technique

Lagrange formalism is applied to derive a dynamic model, and design a nonlinear controller for two nonholonomic, differentially steered, wheeled mobile robots compliantly linked to a common payload. The resulting multivariable system model is of a large order and can be block decoupled by selective state feedback into five independent subsystems, two of which effectively represent the deviation dynamics of the individual robots from a prescribed path; two others represent their forward motion dynamics; while the fifth describes the payload dynamics. Controllers for each of the robot subsystems, including self-tuning adaptive controllers for the nonlinear deviation dynamics subsystems, are designed by the pole-placement technique. System performance is then evaluated via simulation for the case where each robot is undergoing curvilinear motion.

Wheel Dynamics Fundamentals for Agile Tire Slippage Modeling and Control

2014 ◽  
Author(s):  
Vladimir V. Vantsevich
Keyword(s):  
Friction Coefficient ◽  
Rolling Resistance ◽  
Resistance Coefficient ◽  
Rate Of Change ◽  
Technical Problems ◽  
Modeling And Control ◽  
Wheel Torque ◽  
Control Decision ◽  
Estimation And Control ◽  
And Control

One of the technical problems in wheel dynamics is to establish and control the relationship between the tire kinematic and force characteristics related to tire slippage and thus to tire-soil power losses and wheel mobility estimation. This problem has been attracting a lot attention from the research community for decades. The electronization of modern vehicles can enhance their performance in complex and severe vehicle-road/terrain environments by implementing agile control decision within the scale of milliseconds. Thus, agility requires new approaches when considering and analyzing the tire slippage process. This paper presents an analysis of the tire slippage process in stochastic terrain conditions for the purpose of agile tire slip modeling, estimation and control. Based on the introduced relations between the rolling radii of the tire, circumferential wheel force/wheel torque, wheel kinematic parameters and tire slippage, a set of agile tire-terrain characteristics is offered in the paper. The proposed characteristics take in consideration the rate of change of the listed parameters and thus allow a user to estimate the agile dynamics of the tire slip and evidence the closeness to the peak friction coefficient and hence estimate potential mobility loss. The characteristics establish relationships between the stochastic peak friction coefficient, rolling resistance coefficient, and wheel kinematic/force parameters. The characteristics are illustrated by computer simulation results in several terrain conditions.

Development of a Hybrid Dynamic Model and Experimental Identification of Robotic Bulldozing

2012 ◽  
Vol 135 (2) ◽  
Author(s):  
Scott G. Olsen ◽  
Gary M. Bone
Keyword(s):  
Dynamic Model ◽  
Degrees Of Freedom ◽  
Control Strategies ◽  
Nonlinear Differential Equations ◽  
Model Framework ◽  
Modeling And Control ◽  
Low Level ◽  
Proposed Model ◽  
The Individual ◽  
And Control

The low-level modeling and control of mobile robots that interact forcibly with their environment, such as robotic excavation machinery, is a challenging problem that has not been adequately addressed in prior research. This paper investigates the low-level modeling of robotic bulldozing. The proposed model characterizes the three primary degrees-of-freedom (DOF) of the bulldozer, the blade position, the material accumulation on the blade, and the material distribution in the environment. It includes discrete operation modes contained within a hybrid dynamic model framework. The dynamics of the individual modes are represented by a set of linear and nonlinear differential equations. An instrumented scaled-down bulldozer and environment are developed to emulate the full scale operation. Model parameter estimation and validation are completed using experimental data from this system. The model is refined based on a global sensitivity analysis. The refined model is suitable for simulation and design of robotic bulldozing control strategies.

Strongly consistent estimation in a controlled Markov renewal model

1982 ◽  
Vol 19 (3) ◽  
pp. 532-545 ◽  
Author(s):  
Michael Kolonko
Keyword(s):  
Optimal Control ◽  
Dynamic Models ◽  
Consistent Estimation ◽  
Unknown Parameters ◽  
Control Approach ◽  
Renewal Model ◽  
Countable State ◽  
Estimation And Control ◽  
And Control ◽  
Strongly Consistent

The optimal control of dynamic models which are not completely known to the controller often requires some kind of estimation of the unknown parameters. We present conditions under which a minimum contrast estimator will be strongly consistent independently of the control used. This kind of estimator is appropriate for the adaptive or ‘estimation and control' approach in dynamic programming under uncertainty. We consider a countable-state Markov renewal model and we impose bounding and recurrence conditions of the so-called Liapunov type.

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