Theoretical study of the Nonlinear Quadratic Optimal Control implementation

MACRo 2015 ◽  
2017 ◽  
Vol 2 (1) ◽  
pp. 47-55
Author(s):  
Katalin György
Keyword(s):  
Optimal Control ◽  
Estimation Algorithm ◽  
Nonlinear Process ◽  
Identification Algorithm ◽  
Linear Quadratic ◽  
Time Optimal ◽  
Nonlinear Process Control ◽  
On Line ◽  
Linear Quadratic Regulators ◽  
Control Implementation

AbstractIn this brief, I study the finite and infinite nonlinear discrete time optimal control. The quadratic control problem for nonlinear case can be solved with different methods such as: linearization of the system model around each operation point or some different methods, where should be used an on-line parameter identification algorithm. In this paper, I study some properties of these algorithms in order to improve the control efficiency of the nonlinear process control. In this paper, I supposed the all states are accessible, so there is not necessary any state estimation algorithm for the implementation of the proposed optimal control (LQR - Linear Quadratic Regulators) methods.

Kalman Filter Control Embedded into the Reinforcement Learning Framework

2004 ◽  
Vol 16 (3) ◽  
pp. 491-499 ◽  
Author(s):  
István Szita ◽  
András Lőrincz
Keyword(s):  
Optimal Control ◽  
Kalman Filter ◽  
Reinforcement Learning ◽  
Learning Rule ◽  
Slight Modification ◽  
Linear Quadratic ◽  
Filter Model ◽  
Learning Framework ◽  
Value Estimation ◽  
On Line

There is a growing interest in using Kalman filter models in brain modeling. The question arises whether Kalman filter models can be used on-line not only for estimation but for control. The usual method of optimal control of Kalman filter makes use of off-line backward recursion, which is not satisfactory for this purpose. Here, it is shown that a slight modification of the linear-quadratic-gaussian Kalman filter model allows the on-line estimation of optimal control by using reinforcement learning and overcomes this difficulty. Moreover, the emerging learning rule for value estimation exhibits a Hebbian form, which is weighted by the error of the value estimation.

Adaptive Time Optimal Control for Ship Steering

1986 ◽  
Vol 108 (3) ◽  
pp. 233-239 ◽  
Author(s):  
Masato Fukino ◽  
Masayoshi Tomizuka
Keyword(s):  
Optimal Control ◽  
Estimation Algorithm ◽  
Equation Model ◽  
Time Optimal Control ◽  
Ship Steering ◽  
Control Scheme ◽  
Course Changes ◽  
Parameter Estimation Algorithm ◽  
Time Optimal ◽  
Adaptive Time

This paper proposes an adaptive time optimal control algorithm for ship steering in course changes. Dynamics of the ship are presented by a third order differential equation model. The time optimal control law is obtained by applying the Successive Order Hightening Method. It is combined with a least squares type parameter estimation algorithm to obtain the adaptive time optimal controller. The proposed control scheme is evaluated by a computer simulation study.

Time Optimal Control of Balanced Manipulators

1989 ◽  
Vol 111 (2) ◽  
pp. 187-193 ◽  
Author(s):  
W. S. Newman ◽  
N. Hogan
Keyword(s):  
Optimal Control ◽  
Efficient Algorithm ◽  
Difficult Problem ◽  
Time Optimal Control ◽  
Restricted Class ◽  
Moving Targets ◽  
Problem Of Time ◽  
Time Optimal ◽  
On Line ◽  
Point To Point

An efficient algorithm is presented for solving time-optimal control for a restricted class of manipulators: balanced, decoupled manipulators. It is shown how the dynamics of such manipulators are sufficiently simple that optimal control solutions for point-to-point moves can be found analytically. A change of coordinates is proposed in which the more difficult problem of time-optimal interception of moving targets can be solved efficiently. A fast algorithm is described for computing and updating optimal interception solutions on line.

An Auto-Adaptive GA-PID Control Method Based on CMAC Net

2011 ◽  
Vol 219-220 ◽  
pp. 1139-1144
Author(s):  
Wei Qiang Yue ◽  
Li Qiang Jin ◽  
Chuan Xue Song
Keyword(s):  
Process Control ◽  
Pid Control ◽  
Pid Controller ◽  
Control Method ◽  
Nonlinear Process ◽  
High Accuracy ◽  
Pattern Control ◽  
Nonlinear Process Control ◽  
On Line ◽  
Feed Forward Controller

This paper aimed at solving the difficulty of nonlinear process control by classical PID controller. The author structured a GA-PID controller taking advantage of the multipoint optimizing and fast compute speed of GA, which can get the optimal PID parameters by on-line turning. At the same time, the author introduced a CMAC feed-forward controller which make full use of the high precision to approach nonlinearly object of CMAC. Combine them, a concurrent pattern control method appear, which synthesize advantages of two controllers and is more fit for nonlinear process. The simulation result indicated that the method has high accuracy and good robustness.

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