Adaptive Weighting Decoupling Design for Constrained Generalized Predictive Control Using Improved Genetic Algorithm

2013 ◽  
Vol 441 ◽  
pp. 829-832
Author(s):  
Yong Bin Dai
Keyword(s):  
Genetic Algorithm ◽  
Predictive Control ◽  
Shape Control ◽  
Control Method ◽  
Dynamic Performance ◽  
Main Idea ◽  
Generalized Predictive Control ◽  
Multivariable System ◽  
Improved Genetic Algorithm ◽  
Error Weight

The paper proposes a new method for decoupling multivariable system based on generalized predictive control (GPC) with constrains. It is the main idea of proposed control method that the error weight can change with output deviation caused by reference changes in order to reduce interactions in the system and improve dynamic performance of coupling loops. With improved genetic algorithm to optimize the performance index of GPC, the algorithm is applied to auto shape control and auto gauge control (ASC-AGC). The simulation results demonstrate the efficiency and correctness of approach proposed.

Model predictive control method of a parallel electromagnetic isolation system based on the improved genetic algorithm

2020 ◽  
Vol 26 (21-22) ◽  
pp. 2001-2012
Author(s):  
Lei Zhang ◽  
Xiangtao Zhuan
Keyword(s):  
Genetic Algorithm ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Vibration Isolation ◽  
Control Method ◽  
Improved Genetic Algorithm ◽  
Isolation System ◽  
Control Approach ◽  
Control Objective ◽  
Vibration Isolation Performance

To improve the vibration isolation performance for a parallel electromagnetic isolation system, an improved genetic algorithm to optimize the Q and R matrices in the control objective function for a model predictive control approach is proposed. In this study, a parallel electromagnetic isolation system with two electromagnetic isolation units is designed to expand the vibration isolation range to isolate the large object. The dynamical equation and state equation of the parallel electromagnetic isolation system are built. The nonlinear relationship among electromagnetic force, coil current, and gap is calculated by COMSOL Multiphysics to design the model predictive control controller. Meanwhile, an improved genetic algorithm by the variable chromosome length coevolutionary method is presented to tackle two issues. The first issue is that the parameters of Q and R matrices in the control objective function are mainly selected by trial and error. The other issue is that the model predictive control approach needs to determine prediction steps which may lead to the model predictive control approach suffering from heavy computation or an inaccurate prediction model. Simulation and experimental results demonstrate that the parallel electromagnetic isolation system with model predictive control method based on the improved genetic algorithm can achieve better vibration isolation performance in comparison with the passive isolation system.

An efficient intelligent decision method for bionic motion unmanned system

2021 ◽  
pp. 095965182110655
Author(s):  
Haipeng Chen ◽  
Wenxing Fu ◽  
Yuze Feng ◽  
Jia Long ◽  
Kang Chen
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Path Planning ◽  
Motion Control ◽  
Control Method ◽  
Improved Genetic Algorithm ◽  
Decision Method ◽  
Multiple Traveling Salesman Problem ◽  
Intelligent Decision ◽  
Unmanned System

In this article, we propose an efficient intelligent decision method for a bionic motion unmanned system to simulate the formation change during the hunting process of the wolves. Path planning is a burning research focus for the unmanned system to realize the formation change, and some traditional techniques are designed to solve it. The intelligent decision based on evolutionary algorithms is one of the famous path planning approaches. However, time consumption remains to be a problem in the intelligent decisions of the unmanned system. To solve the time-consuming problem, we simplify the multi-objective optimization as the single-objective optimization, which was regarded as a multiple traveling salesman problem in the traditional methods. Besides, we present the improved genetic algorithm instead of evolutionary algorithms to solve the intelligent decision problem. As the unmanned system’s intelligent decision is solved, the bionic motion control, especially collision avoidance when the system moves, should be guaranteed. Accordingly, we project a novel unmanned system bionic motion control of complex nonlinear dynamics. The control method can effectively avoid collision in the process of system motion. Simulation results show that the proposed simplification, improved genetic algorithm, and bionic motion control method are stable and effective.

scholarly journals Adaptive Air-Fuel Ratio Regulation for Port-Injected Spark-Ignited Engines Based on a Generalized Predictive Control Method

Energies ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 173 ◽  
Author(s):  
Lei Meng ◽  
Xiaofeng Wang ◽  
Chunnian Zeng ◽  
Jie Luo
Keyword(s):  
Predictive Control ◽  
Noise Suppression ◽  
Control Method ◽  
Simulation Analysis ◽  
Catalytic Converter ◽  
Exhaust Emission ◽  
Generalized Predictive Control ◽  
Si Engine ◽  
Fuel Ratio ◽  
Wall Wetting

The accurate air-fuel ratio (AFR) control is crucial for the exhaust emission reduction based on the three-way catalytic converter in the spark ignition (SI) engine. The difficulties in transient cylinder air mass flow measurement, the existing fuel mass wall-wetting phenomenon, and the unfixed AFR path dynamic variations make the design of the AFR controller a challenging task. In this paper, an adaptive AFR regulation controller is designed using the feedforward and feedback control scheme based on the dynamical modelling of the AFR path. The generalized predictive control method is proposed to solve the problems of inherent nonlinearities, time delays, parameter variations, and uncertainties in the AFR closed loop. The simulation analysis is investigated for the effectiveness of noise suppression, online prediction, and self-correction on the SI engine system. Moreover, the experimental verification shows an acceptable performance of the designed controller and the potential usage of the generalized predictive control in AFR regulation application.

scholarly journals An improved Genetic Algorithm For Fuzzy Production PlanningProblems with Application

2015 ◽  
Vol 1 (3) ◽  
pp. 390
Author(s):  
Jalal Abdulkareem Sultan ◽  
Omar Ramzi Jasim ◽  
Sarmad Abdulkhaleq Salih
Keyword(s):  
Genetic Algorithm ◽  
Customer Service ◽  
Fundamental Problem ◽  
Demand Forecasting ◽  
Fuzzy Model ◽  
Main Idea ◽  
Production Schedule ◽  
Improved Genetic Algorithm ◽  
Customer Service Level ◽  
Master Production Schedule

Production Planning or Master Production Schedule (MPS) is a key interface between marketing and manufacturing, since it links customer service directly to efficient use of production resources. Mismanagement of the MPS is considered as one of fundamental problem in operation and it can potentially lead to poor customer satisfaction.  In this paper, an improved Genetic Algorithm (IGA) is used to solving fuzzy multi-objective master production schedule (FMOMPS). The main idea is to integrate GA with local search operator. The FMOMPS was applied in the Cotton and medical gauzes plant in Mosul city. The application involves determine the gross requirements by demand forecasting using artificial neural networks. The IGA proved its efficiency in solving MPS problems compared with the genetic algorithm for fuzzy and non-fuzzy model, as the results clearly showed the ability of IGA to determine intelligently how much, when, and where the additional capacities (overtimes) are required such that the inventory can be reduced without affecting customer service level.

scholarly journals Genetic Algorithm-Based Variable Value Control Method for Solving the Ground Target Attacking Weapon-Target Allocation Problem

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Chao Wang ◽  
Guangyuan Fu ◽  
Daqiao Zhang ◽  
Hongqiao Wang ◽  
Jiufen Zhao
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Control Method ◽  
Search Space ◽  
Initial Population ◽  
Improved Genetic Algorithm ◽  
Mutation Strategy ◽  
Ground Target ◽  
Value Range ◽  
Target Allocation

Key ground targets and ground target attacking weapon types are complex and diverse; thus, the weapon-target allocation (WTA) problem has long been a great challenge but has not yet been adequately addressed. A timely and reasonable WTA scheme not only helps to seize a fleeting combat opportunity but also optimizes the use of weaponry resources to achieve maximum battlefield benefits at the lowest cost. In this study, we constructed a ground target attacking WTA (GTA-WTA) model and designed a genetic algorithm-based variable value control method to address the issue that some intelligent algorithms are too slow in resolving the problem of GTA-WTA due to the large scale of the problem or are unable to obtain a feasible solution. The proposed method narrows the search space and improves the search efficiency by constraining and controlling the variable value range of the individuals in the initial population and ensures the quality of the solution by improving the mutation strategy to expand the range of variables. The simulation results show that the improved genetic algorithm (GA) can effectively solve the large-scale GTA-WTA problem with good performance.

The Study of Optimal Fuzzy PID-Smith Control Based on Genetic Algorithm

2013 ◽  
Vol 313-314 ◽  
pp. 448-452
Author(s):  
Dian Ting Liu ◽  
Hai Xia Li
Keyword(s):  
Genetic Algorithm ◽  
Simulation Model ◽  
Control Method ◽  
Order System ◽  
Membership Functions ◽  
Fuzzy Pid ◽  
Improved Genetic Algorithm ◽  
Simulation Results ◽  
The Stability ◽  
Better Than

In this paper, the improved genetic algorithm is applied to optimize the quantization factors and the scaling factors of fuzzy control, and the optimized rule table and membership functions is obtained according to certain performances. Then a kind of optimal fuzzy PID-Smith control method based on genetic algorithm is proposed and its simulation model is built in this paper, a second-order system is simulated and analyzed. The results show that requirements of deterministic performances of the new control method are better than the conventional methods through the simulation results in the stability, rapidity and robustness.

A Novel Generalized Predictive Control and its Application on Active Clearance Control of Aero-Engine

2012 ◽  
Vol 616-618 ◽  
pp. 1922-1925
Author(s):  
Kai Peng ◽  
Ding Fan ◽  
Lei Zhang ◽  
Qiu Xia Wang
Keyword(s):  
Predictive Control ◽  
Gas Turbines ◽  
Dynamic Performance ◽  
Tip Clearance ◽  
Generalized Predictive Control ◽  
Aero Engine ◽  
Blade Tip ◽  
Additional Service ◽  
And Control ◽  
Clearance Control

Turbine blade tip clearance continues to be a concern in the design and control of gas turbines. Ever increasing demands for improved efficiency and higher operating temperatures require more stringent tolerances on turbine tip clearance. An implicit active generalized predictive control with AR error modification and fuzzy adjustment on control horizon of aero-engine turbine tip clearance is presented and evaluated. The results show the resultant active tip clearance control system has good steady and dynamic performance and benefits of increased efficiency, reduced specific fuel consumption, and additional service life.

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