An Input-Output Optimal Control Model under Uncertain Influence and its Solution

2012 ◽  
Vol 433-440 ◽  
pp. 2974-2979
Author(s):  
Shu Rong Li ◽  
Feng Wang ◽  
Xiao Yu He
Keyword(s):  
Optimal Control ◽  
Coefficient Matrix ◽  
Control Model ◽  
Chance Constrained Programming ◽  
State Variables ◽  
Input Output ◽  
Bound Constraints ◽  
Uncertain Model ◽  
Calculation Results ◽  
Constrained Programming

An input-output optimal control model is established under uncertain influence in environment. The objective function, terminal constraint of state variables and bound constraints of control variables are considered with fuzziness. The direct consumption coefficient matrix and investment coefficient matrix are regarded as stochastic variables. Membership function and chance constrained programming are applied to convert the uncertain model to a definite one. Penalty function and Particle Swarm Optimization are used to solve the model. The calculation results of an example demonstrate that the uncertain model has more practical value to decision makers compared to a definite one.

System Representation and Optimal Control in Input-Output Data Space

2004 ◽  
Vol 37 (11) ◽  
pp. 185-190 ◽  
Author(s):  
Yasumasa Fujisaki ◽  
Yiran Duan ◽  
Masao Ikeda
Keyword(s):  
Optimal Control ◽  
Input Output ◽  
Output Data ◽  
Data Space ◽  
System Representation

Approximation of the Step-to-Step Dynamics Enables Computationally Efficient and Fast Optimal Control of Legged Robots

2020 ◽  
Author(s):  
Pranav A. Bhounsule ◽  
Myunghee Kim ◽  
Adel Alaeddini
Keyword(s):  
Optimal Control ◽  
Regression Model ◽  
Closed Form ◽  
Legged Robots ◽  
Input Output ◽  
Output Data ◽  
Region Of Stability ◽  
Step Dynamics ◽  
Step Control ◽  
Closed Form Approximation

Abstract Legged robots with point or small feet are nearly impossible to control instantaneously but are controllable over the time scale of one or more steps, also known as step-to-step control. Previous approaches achieve step-to-step control using optimization by (1) using the exact model obtained by integrating the equations of motion, or (2) using a linear approximation of the step-to-step dynamics. The former provides a large region of stability at the expense of a high computational cost while the latter is computationally cheap but offers limited region of stability. Our method combines the advantages of both. First, we generate input/output data by simulating a single step. Second, the input/output data is curve fitted using a regression model to get a closed-form approximation of the step-to-step dynamics. We do this model identification offline. Next, we use the regression model for online optimal control. Here, using the spring-load inverted pendulum model of hopping, we show that both parametric (polynomial and neural network) and non-parametric (gaussian process regression) approximations can adequately model the step-to-step dynamics. We then show this approach can stabilize a wide range of initial conditions fast enough to enable real-time control. Our results suggest that closed-form approximation of the step-to-step dynamics provides a simple accurate model for fast optimal control of legged robots.

Optimal control of unstable input/output time-delayed systems

2011 ◽  
Vol 33 (4) ◽  
pp. 445-460 ◽  
Author(s):  
Pedro Albertos ◽  
Pedro García
Keyword(s):  
Optimal Control ◽  
Input Output ◽  
Delayed Systems ◽  
Output Time

scholarly journals Input-Output Analysis and Decentralized Optimal Control of Inter-Area Oscillations in Power Systems

2016 ◽  
Vol 31 (3) ◽  
pp. 2434-2444 ◽  
Author(s):  
Xiaofan Wu ◽  
Florian Dorfler ◽  
Mihailo R. Jovanovic
Keyword(s):  
Optimal Control ◽  
Power Systems ◽  
Input Output ◽  
Output Analysis ◽  
Input Output Analysis

scholarly journals A System Representation and Optimal Control in Input-Output Data Space

1998 ◽  
Vol 34 (12) ◽  
pp. 1845-1853 ◽  
Author(s):  
Yasumasa FUJISAKI ◽  
Yiran DUAN ◽  
Masao IKEDA ◽  
Misako FUKUDA
Keyword(s):  
Optimal Control ◽  
Input Output ◽  
Output Data ◽  
Data Space ◽  
System Representation
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