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.

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.

Experimental validation on the integrated design and control of a parallel robot

Robotica ◽  
2005 ◽  
Vol 24 (2) ◽  
pp. 173-181 ◽  
Author(s):  
Qing Li
Keyword(s):  
Dynamic Model ◽  
High Speed ◽  
Dynamic Models ◽  
Integrated Design ◽  
Parallel Robot ◽  
Parallel Robots ◽  
Approximation Techniques ◽  
Control Approach ◽  
Modeling Errors ◽  
And Control

Due to the demands from the robotic industry, robot structures have evolved from serial to parallel. The control of parallel robots for high performance and high speed tasks has always been a challenge to control engineers. Following traditional control engineering approaches, it is possible to design advanced algorithms for parallel robot control. These approaches, however, may encounter problems such as heavy computational load and modeling errors, to name it a few. To avoid heavy computation, simplified dynamic models can be obtained by applying approximation techniques, nevertheless, performance accuracy will suffer due to modeling errors. This paper suggests applying an integrated design and control approach, i.e., the Design For Control (DFC) approach, to handle this problem. The underlying idea of the DFC approach can be illustrated as follows: Intuitively, a simple control algorithm can control a structure with a simple dynamic model quite well. Therefore, no matter how sophisticate a desired motion task is, if the mechanical structure is designed such that it results in a simple dynamic model, then, to design a controller for this system will not be a difficult issue. As such, complicated control design can be avoided, on-line computation load can be reduced and better control performance can be achieved. Through out the discussion in the paper, a 2 DOF parallel robot is redesigned based on the DFC concept in order to obtain a simpler dynamic model based on a mass-balancing method. Then a simple PD controller can drive the robot to achieve accurate point-to-point tracking tasks. Theoretical analysis has proven that the simple PD control can guarantee a stable system. Experimental results have successfully demonstrated the effectiveness of this integrated design and control approach.

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

scholarly journals Estimation and Control for Industrial Products Reliability under Failure Trials

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Kui Wang ◽  
Lili Ding
Keyword(s):  
Confidence Interval ◽  
Control Method ◽  
Reliability Estimation ◽  
Point Estimate ◽  
Industrial Products ◽  
Dynamic Reliability ◽  
Control Approach ◽  
The Status ◽  
Estimation And Control ◽  
And Control

In practices, most industrial products are subject to sudden failure and only failure information can be collected, which presents a great challenge for reliability prediction of modern devices. To address this issue, our paper proposes a dynamic reliability estimation and control for industrial products under regular failure trials. The failure trial is performed at different operational time points of the products, which provides sole data source for evaluating the status of industrial products. We use Bayesian approach to dynamically estimate the industrial products when the failure trial is available. The estimated reliability is updated using a point estimate with new available data. To maintain the reliability of products at a desirable status, a reliability control method is presented to monitor the confidence interval of reliability distribution. The lower limit of confidence interval is maintained above a control limit, which indicates that a corresponding quality-assurance action is preferable. The proposed reliability estimation and control approach is demonstrated using a case of light-emitting diodes under failure trials at production process. The obtained results indicate the effectiveness of our estimation and control model.

A near-optimal decentralized control approach for polynomial nonlinear interconnected systems

2020 ◽  
pp. 014233122097743
Author(s):  
Mohamed Sadok Attia ◽  
Mohamed Karim Bouafoura ◽  
Naceur Benhadj Braiek
Keyword(s):  
Optimal Control ◽  
Closed Loop ◽  
Sufficient Conditions ◽  
Open Loop ◽  
System Stability ◽  
Gronwall Lemma ◽  
Interconnected System ◽  
Control Approach ◽  
State Dependent ◽  
And Control

This article tackles the decentralized near-optimal control problem for the class of nonlinear polynomial interconnected system based on a shifted Legendre polynomials direct approach. The proposed method converts the interconnected optimal control problems into a nonlinear programming one with multiple constraints. In light of the formulated NLP optimization, state and control coefficients are used to design a nonlinear decentralized state feedback controller. Overall closed-loop system stability sufficient conditions are investigated with the help of Grönwall lemma. The triple inverted pendulum case is considered for simulation. Satisfactory results are obtained in both open-loop and closed-loop schemes with comparison to collocation and state-dependent Riccati equation techniques.

scholarly journals Light Field Estimation and Control Using a Graphical Rendering Engine

2014 ◽  
Author(s):  
Sina Afshari ◽  
Li Jia ◽  
Richard J. Radke ◽  
Sandipan Mishra
Keyword(s):  
Feedback Control ◽  
Control Systems ◽  
Light Field ◽  
Model Building ◽  
Control Approach ◽  
Lighting Control ◽  
Estimation Algorithms ◽  
Estimation And Control ◽  
Graphics Software ◽  
And Control

State-of-the-Art feedback control of lighting depends on point sensor measurements for light field generation. However, since the occupant’s perception depends on the entire light field in the room instead of the illumination at a limited set of points, the performance of these lighting control systems may be unsatisfactory. Therefore, it is critical to reconstruct the light field in the room from point sensor measurements and use it for feedback control of lights. This paper presents a framework for using graphical rendering tools along with point sensor measurements for the estimation of a light field and using these estimates for feedback control. Computer graphics software is used to efficiently and accurately model building spaces, while a game engine is used to render different lighting conditions for the space on the fly. These real-time renderings are then used together with sensor measurements to estimate and control the light field in the room using an optimization-based feedback control approach. We present a set of estimation algorithms for this purpose and analyze their convergence and performance limitations. Finally, we demonstrate closed loop lighting control systems that use these estimation algorithms and compare their relative performance, highlighting their benefits and disadvantages.

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