Adaptive Pole Placement Control Algorithm for Nonlinear Systems

1997 ◽  
Vol 05 (02) ◽  
pp. 139-146 ◽  
Author(s):  
Jin Wang ◽  
Wenzhong Gao
Keyword(s):  
Neural Network ◽  
Control Algorithm ◽  
Learning Algorithm ◽  
Recursive Least Squares ◽  
Pole Placement ◽  
Rls Algorithm ◽  
Pole Placement Controller ◽  
Nonlinear Plants ◽  
Adaptive Control Algorithm ◽  
Pole Placement Control

A new adaptive control algorithm for unknown nonlinear plants is presented. The paper first describes a modified neural network(MNN) as well as the associated learning algorithm. The learning algorithm converges considerably faster because of the introduction of recursive least squares(RLS) algorithm. And then designs adaptive pole placement controller based on the modified neural network. Simulation results show that the proposed control algorithm can effectively control nonlinear plants.

Online parameter estimation and adaptive control with limited information in structures

2021 ◽  
pp. 107754632110191
Author(s):  
Fereidoun Amini ◽  
Elham Aghabarari
Keyword(s):  
Parameter Estimation ◽  
Adaptive Control ◽  
Least Squares ◽  
Control Algorithm ◽  
Least Squares Method ◽  
Recursive Least Squares ◽  
Limited Information ◽  
Online Identification ◽  
Recursive Least Squares Method ◽  
Adaptive Control Algorithm

An online parameter estimation is important along with the adaptive control, that is, a time-dependent plant. This study uses both online identification and the simple adaptive control algorithm with velocity feedback. The recursive least squares method was used to identify the stiffness and damping parameters of the structure’s stories. Identification was carried out online without initial estimation and only by measuring the structural responses. The limited information regarding sensor measurements, parameter convergence, and the effects of the covariance matrix is examined. The integration of the applied online identification, the appropriate reference model selection in simple adaptive control, and adopting the proportional integral filter was used to limit the structural control response error. Some numerical examples are simulated to verify the ability of the proposed approach. Despite the limited information, the results show that the simultaneous use of online identification with the recursive least squares method and simple adaptive control algorithm improved the overall structural performance.

Improved Pole-placement Control with Feed-forward Dead Zone Compensation for Position Tracking of Electro-Pneumatic Actuator System Improved Pole-placement Control with Feed-forward Dead Zone Compensation for Position Tracking of Electro-Pneumatic Actuato

2021 ◽  
Vol 20 (2) ◽  
pp. 25-32
Author(s):  
Noorhazirah Sunar ◽  
Mohd Fua’ad Rahmat ◽  
Ahmad ‘Athif Mohd Fauzi ◽  
Zool Hilmi Ismail ◽  
Siti Marhanis Osman ◽  
...  
Keyword(s):  
Dead Zone ◽  
Pneumatic Actuator ◽  
Pole Placement ◽  
Position Tracking ◽  
Zone Model ◽  
Feed Forward ◽  
The Dead ◽  
Chattering Effect ◽  
Pole Placement Controller ◽  
Pole Placement Control

Dead-zone in the valve degraded the performances of the Electro-Pneumatic Actuator (EPA) system.  It makes the system difficult to control, become unstable and leads to chattering effect nearest desired position.  In order to cater this issue, the EPA system transfer function and the dead-zone model is identified by MATLAB SI toolbox and the Particle Swarm Optimization (PSO) algorithm respectively.  Then a parametric control is designed based on pole-placement approach and combine with feed-forward inverse dead-zone compensation.  To reduce chattering effect, a smooth parameter is added to the controller output.  The advantages of using these techniques are the chattering effect and the dead-zone of the EPA system is reduced.  Moreover, the feed-forward system improves the transient performance.  The results are compared with the pole-placement control (1) without compensator and (2) with conventional dead-zone compensator.  Based on the experimental results, the proposed controller reduced the chattering effect due to the controller output of conventional dead-zone compensation, 90% of the pole-placement controller steady-state error and 30% and 40% of the pole-placement controller with conventional dead-zone compensation settling time and rise time.

Model Based Control of a Smart Vibrating Cantilever Beam

2001 ◽  
Author(s):  
Robin G. Scott ◽  
Michael D. Brown ◽  
Warren J. Manning ◽  
Martin C. Levesley
Keyword(s):  
Cantilever Beam ◽  
Limit Cycles ◽  
Minimum Variance ◽  
Smart Structure ◽  
Pole Placement ◽  
Vibration Effect ◽  
Data Control ◽  
Pole Placement Controller ◽  
The Cost ◽  
Pole Placement Control

Abstract This paper compares Generalised Minimum Variance and Pole-placement techniques for the control of a lightly damped cantilever beam smart structure. Saturation of the control signal can lead to limit cycles in Pole-placement control. Saturation compensation can remove these limit cycles, allowing disturbances of the beam to be rejected, but introduces a low amplitude, higher frequency vibration effect. Control sensitivity functions used to investigate these limit cycles show that certain Pole-placement controllers are sensitive to frequencies in the 50Hz range. The sensitivity of a Generalised minimum variance (GMV) controller is shown to be less than that of the Pole-placement controller. This GMV controller is applied to the vibration control of the smart beam. The controller weightings of the cost function limit excessive control signals. Previous work allows a plant model to be generated that produces results that closely match experimental data. Control results shows that the GMV technique is highly effective in reducing both the decay time and amplitude of vibration for free and forced vibrations respectively.

Neural Network Control of a Laboratory Magnetic Levitator

2013 ◽  
pp. 361-374
Author(s):  
J. Katende ◽  
M. Mustapha
Keyword(s):  
Neural Network ◽  
Vibration Isolation ◽  
Magnetic Levitation ◽  
Reference Signal ◽  
Network Control ◽  
Pole Placement ◽  
Linear State ◽  
Artificial Neural Network Ann ◽  
Hidden Layer ◽  
Pole Placement Controller

Magnetic levitation (maglev) systems are nowadays employed in applications ranging from non-contact bearings and vibration isolation of sensitive machinery to high-speed passenger trains. In this chapter a mathematical model of a laboratory maglev system was derived using the Lagrangian approach. A linear pole-placement controller was designed on the basis of specifications on peak overshoot and settling time. A 3-layer feed-forward Artificial Neural Network (ANN) controller comprising 3-input nodes, a 5-neuron hidden layer, and 1-neuron output layer was trained using the linear state feedback controller with a random reference signal. Simulations to investigate the robustness of the ANN control scheme with respect to parameter variations, reference step input magnitude variations, and sinusoidal input tracking were carried out using SIMULINK. The obtained simulation results show that the ANN controller is robust with respect to good positioning accuracy.

The Design of Ship Course Intelligent Controller Based on Adaptive Neural Fuzzy Interference System

2011 ◽  
Vol 135-136 ◽  
pp. 1037-1043
Author(s):  
Guan Shan Hu ◽  
Hai Rong Xiao
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
High Performance ◽  
Learning Algorithm ◽  
Fuzzy Interference System ◽  
Interference System ◽  
Ship Steering ◽  
Neuro Fuzzy ◽  
Online Learning Algorithm ◽  
Adaptive Control Algorithm

Under the condition that the nonlinearity of ship steering model is considered and the assumption that the parameters of the model are uncertain, we proposed an adaptive control algorithm for ship course nonlinear system by incorporating the technique of neural network and fuzzy logic system. In the paper, we presented the structure and characteristics of Adaptive Neuro-Fuzzy Interference System (ANFIS), established the ship course controller, and realized an online learning algorithm to do online parameter estimation. We utilize fuzzy logic to solve the uncertainty problem of control system, neural network to optimize the controller parameters. To demonstrate the applicability of the proposed method, simulation results are presented at the end of this paper. The experiment shows that the ANFIS controller can achieve high performance control under parameter perturbation and other disturbances.

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