Long Range Predictive Optimal Control Law with Guaranteed Stability for Process Control Applications

1992 ◽  
Author(s):  
M.J. Grimble
Keyword(s):  
Optimal Control ◽  
Process Control ◽  
Long Range ◽  
Control Law ◽  
Control Applications ◽  
Guaranteed Stability

Long Range Predictive Optimal Control Law With Guaranteed Stability for Process Control Applications

1993 ◽  
Vol 115 (4) ◽  
pp. 600-610 ◽  
Author(s):  
M. J. Grimble
Keyword(s):  
Process Control ◽  
Predictive Control ◽  
Industrial Applications ◽  
Control Law ◽  
Control Applications ◽  
Nonminimum Phase ◽  
Control Signals ◽  
Guaranteed Stability ◽  
The Cost ◽  
And Control

The Generalized Predictive Control law has been successfully applied in industrial applications but has limitations on nonminimum phase processes for some of the most obvious choices of cost-functions. The Weighted Predictive Control law proposed here is new and avoids these difficulties while maintaining a similar philosophy. It ensures guaranteed stability when the control weighting tends to zero, even if the system is nonminimum phase. The solution is relatively straightforward and is very suitable for process control applications. The cost-function includes dynamic weighting terms on both output and control signals so that robustness properties can be frequency shaped.

The Simulation and Design Applications of Grinding and Classification Process Control System

2012 ◽  
Vol 203 ◽  
pp. 221-225 ◽  
Author(s):  
De Yan Wang
Keyword(s):  
Optimal Control ◽  
Process Control ◽  
Quality Indicators ◽  
Distributed Simulation ◽  
Control Law ◽  
Regulation Mechanism ◽  
Grinding Process ◽  
Simulation Platform ◽  
Feedback Correction ◽  
The Impact

Based on the grinding and classification process dynamic model, the distributed simulation platform for semi-physical grinding process was analyzed. Based on the feedback correction and dynamic optimal control and optimization model calculated the optimal control law, the quality indicators to feedback regulation mechanism was introduced to eliminate the impact of process disturbances and other uncertainties. Intelligent control unit according to the deviation between the artificial test and expectations of quality indicators can feedback correction of the optimal control law. The field experiment results show that the program to stabilize the process of product quality, to achieve the process of saving energy. The grinding process of the optimal control of distributed simulation platform for the optimal control method and system design provide effective, convenient, reliable and intuitive engineering lab environment. Also it has important reference value to other metallurgical optimization of industrial process control engineering verification and simulation.

scholarly journals Adaptive Optimal Robust Control for Uncertain Nonlinear Systems Using Neural Network Approximation in Policy Iteration

2021 ◽  
Vol 11 (5) ◽  
pp. 2312
Author(s):  
Dengguo Xu ◽  
Qinglin Wang ◽  
Yuan Li
Keyword(s):  
Neural Network ◽  
Optimal Control ◽  
Robust Control ◽  
Nonlinear Systems ◽  
Policy Iteration ◽  
Control Law ◽  
Globally Asymptotically Stable ◽  
Control Approach ◽  
Input Uncertainties ◽  
Theoretical Results

In this study, based on the policy iteration (PI) in reinforcement learning (RL), an optimal adaptive control approach is established to solve robust control problems of nonlinear systems with internal and input uncertainties. First, the robust control is converted into solving an optimal control containing a nominal or auxiliary system with a predefined performance index. It is demonstrated that the optimal control law enables the considered system globally asymptotically stable for all admissible uncertainties. Second, based on the Bellman optimality principle, the online PI algorithms are proposed to calculate robust controllers for the matched and the mismatched uncertain systems. The approximate structure of the robust control law is obtained by approximating the optimal cost function with neural network in PI algorithms. Finally, in order to illustrate the availability of the proposed algorithm and theoretical results, some numerical examples are provided.

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