scholarly journals Model‐Free Adaptive Control Method Applied to Vibration Reduction of a Flexible Crane as MIMO System

PAMM ◽  
2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Hoang Anh Pham ◽  
Dirk Söffker
Keyword(s):  
Adaptive Control ◽  
Control Method ◽  
Mimo System ◽  
Vibration Reduction ◽  
Model Free

Anti-noise model-free adaptive control and its application in the circulating fluidized bed boiler

2020 ◽  
pp. 095965182097937
Author(s):  
Na Dong ◽  
Wenjin Lv ◽  
Shuo Zhu ◽  
Donghui Li
Keyword(s):  
Adaptive Control ◽  
Complete Convergence ◽  
Circulating Fluidized Bed ◽  
Control Method ◽  
Noise Model ◽  
Good Resistance ◽  
Industrial Systems ◽  
Noise Interference ◽  
Model Free ◽  
Tracking Differentiator

Model-free adaptive control has been developed greatly since it was proposed. Up to now, model-free adaptive control theory has become mature and tends to be an effective solution for complex unmodeled industrial systems. In practical industrial processes, most control systems are inevitably accompanied by noise that will result in indelible error and may further cause inaccurate feedback to the output. In order to solve this kind of problem with model-free technique, this article incorporates an improved tracking differentiator into model-free adaptive control. After that, the anti-noise model-free adaptive control method with complete convergence analysis is proposed. Meanwhile, numerical simulation proves that the improved control method can quickly track a given signal with good resistance to noise interference. Finally, the effectiveness and practicability of the proposed algorithm are verified by experiments through the control of drum water level of circulating fluidized.

A New Model-Free Stability-Based Cognitive Control Method

2014 ◽  
Author(s):  
Xi Nowak ◽  
Dirk Söffker
Keyword(s):  
Control Method ◽  
Mimo System ◽  
Equilibrium Points ◽  
Optimization Method ◽  
Control Input ◽  
Adaptive Stabilization ◽  
Whole Process ◽  
Model Free ◽  
Stabilization Control ◽  
And Performance

This contribution considers a new realization of the cognitive stabilizer, which is an adaptive stabilization control method based on a cognition-based framework. It is assumed, that the model of the system to be controlled is unknown. Only the knowledge about the system inputs, outputs, and equilibrium points are the preliminaries assumed within this approach. A new improved realization of the cognitive stabilizer is designed in this contribution using 1) a neural network estimating suitable inputs according to the desired outputs, 2) Lyapunov stability criterion according to a certain Lyapunov function, and 3) an optimization method to determine the desired system outputs with respect to the system energy. The proposed cognitive stabilizer is able to stabilize an unknown nonlinear MIMO system at arbitrary equilibrium point of it. Suitable control input can be designed automatically to guarantee the stability of motion of the system during the whole process although the changing of the system behavior or the environment. Numerical examples are shown to demonstrate the successful application and performance of this method.

Model-Free Adaptive Control Based on Particle Swarm Optimization Algorithm for Water Bath Drawing Process of Carbon Fiber

2013 ◽  
Vol 303-306 ◽  
pp. 1180-1184
Author(s):  
Jian Ren ◽  
Yong Sheng Ding ◽  
Kuang Rong Hao
Keyword(s):  
Particle Swarm Optimization ◽  
Adaptive Control ◽  
Carbon Fiber ◽  
Mimo System ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Water Bath ◽  
Swarm Optimization ◽  
Fiber Production ◽  
Model Free

The water bath drawing slot in carbon fiber production is a MIMO system with coupling and time delay. It is difficult for a traditional control scheme to realize stable control on the water bath drawing slot. In this paper, a model-free adaptive control based on particle swarm optimization (PSO) algorithm was designed with few parameters, convenient control, and without decoupling. The PSO algorithm was used to adjust MFAC controller parameters online at each sampling time. The proposed method was applied to the liquid-level-concentration control in the water bath drawing slot of carbon fiber production. The control method has rapid response time, good decoupling and performance by comparing with model-free adaptive control and traditional PID control.

scholarly journals Data-Driven Model-Free Adaptive Control Based on Error Minimized Regularized Online Sequential Extreme Learning Machine

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3241 ◽  
Author(s):  
Xiaofei Zhang ◽  
Hongbin Ma
Keyword(s):  
Adaptive Control ◽  
Extreme Learning Machine ◽  
Discrete Time ◽  
Control Method ◽  
Time Varying ◽  
Varying Parameters ◽  
Model Free ◽  
Time Varying Parameters ◽  
Learning Machine ◽  
Linearized Model

Model-free adaptive control (MFAC) builds a virtual equivalent dynamic linearized model by using a dynamic linearization technique. The virtual equivalent dynamic linearized model contains some time-varying parameters, time-varying parameters usually include high nonlinearity implicitly, and the performance will degrade if the nonlinearity of these time-varying parameters is high. In this paper, first, a novel learning algorithm named error minimized regularized online sequential extreme learning machine (EMREOS-ELM) is investigated. Second, EMREOS-ELM is used to estimate those time-varying parameters, a model-free adaptive control method based on EMREOS-ELM is introduced for single-input single-output unknown discrete-time nonlinear systems, and the stability of the proposed algorithm is guaranteed by theoretical analysis. Finally, the proposed algorithm is compared with five other control algorithms for an unknown discrete-time nonlinear system, and simulation results show that the proposed algorithm can improve the performance of control systems.

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