A DYNAMICAL STATE FEEDBACK CHAOTIFICATION METHOD WITH APPLICATION ON LIQUID MIXING

2013 ◽  
Vol 22 (07) ◽  
pp. 1350059 ◽  
Author(s):  
SAVAŞ ŞAHİN ◽  
CÜNEYT GÜZELİŞ
Keyword(s):  
State Feedback ◽  
Reference Model ◽  
Arbitrary Dimension ◽  
Dc Motor ◽  
Input State ◽  
Motor Speed ◽  
Dynamical State ◽  
Special Cases ◽  
Loop Dynamics ◽  
Neutralization Time

This paper introduces chaotic reference model-based dynamical state feedback chaotification method which can be applied to any input-state linearizable (nonlinear) system including linear controllable ones as special cases. In the developed method, any chaotic system of arbitrary dimension can be used as the reference model with no need to transform it into a special form, so providing the advantage of exploiting the vast amount of information on chaotic systems and their implementations available in the literature. To demonstrate the potential effective applications of the method, a permanent magnet dc motor is chaotified by the proposed dynamical state feedback as matching the closed loop dynamics to the well-known Chua's chaotic circuit. Then, an impeller mounted on the chaotified dc motor is used for mixing a corn syrup added acid–base mixture. It is observed in a nonintrusive way that mixing actuated by the chaotified dc motor is more efficient than constant and also periodical motor speed cases for the consideration of neutralization time and power consumption together.

scholarly journals Data-Driven Model-Free Tracking Reinforcement Learning Control with VRFT-based Adaptive Actor-Critic

2019 ◽  
Vol 9 (9) ◽  
pp. 1807 ◽  
Author(s):  
Radac ◽  
Precup
Keyword(s):  
Process Model ◽  
State Feedback ◽  
Reference Model ◽  
Open Loop ◽  
Design Guidelines ◽  
Feedback Controller ◽  
Input State ◽  
State Feedback Controller ◽  
Learning Framework ◽  
Model Free

This paper proposes a neural network (NN)-based control scheme in an Adaptive Actor-Critic (AAC) learning framework designed for output reference model tracking, as a representative deep-learning application. The control learning scheme is model-free with respect to the process model. AAC designs usually require an initial controller to start the learning process; however, systematic guidelines for choosing the initial controller are not offered in the literature, especially in a model-free manner. Virtual Reference Feedback Tuning (VRFT) is proposed for obtaining an initially stabilizing NN nonlinear state-feedback controller, designed from input-state-output data collected from the process in open-loop setting. The solution offers systematic design guidelines for initial controller design. The resulting suboptimal state-feedback controller is next improved under the AAC learning framework by online adaptation of a critic NN and a controller NN. The mixed VRFT-AAC approach is validated on a multi-input multi-output nonlinear constrained coupled vertical two-tank system. Discussions on the control system behavior are offered together with comparisons with similar approaches.

Straight-Move Robot Control System with LabView-Based Proportional Integral Derivative (PID) Control

2019 ◽  
Vol 3 (2) ◽  
pp. 13-24
Author(s):  
Andrean George W
Keyword(s):  
Pid Control ◽  
Rotational Speed ◽  
Pid Controller ◽  
Dc Motor ◽  
Motor Speed ◽  
Labview Software ◽  
Dc Motors ◽  
Dc Motor Control ◽  
Process System ◽  
Interface Devices

Abstract - Control and monitoring of the rotational speed of a wheel (DC motor) in a process system is very important role in the implementation of the industry. PWM control and monitoring for wheel rotational speed on a pair of DC motors uses computer interface devices where in the industry this is needed to facilitate operators in controlling and monitoring motor speed. In order to obtain the best controller, tuning the Integral Derifative (PID) controller parameter is done. In this tuning we can know the value of proportional gain (Kp), integral time (Ti) and derivative time (Td). The PID controller will give action to the DC motor control based on the error obtained, the desired DC motor rotation value is called the set point. LabVIEW software is used as a PE monitor, motor speed control. Keyword : LabView, Motor DC, Arduino, LabView, PID.

Design and application of an optimally tuned PID controller for DC motor speed regulation via a novel hybrid Lévy flight distribution and Nelder–Mead algorithm

2021 ◽  
pp. 014233122110196
Author(s):  
Davut Izci
Keyword(s):  
Pid Controller ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Absolute Error ◽  
Dc Motor ◽  
Cuckoo Search Algorithm ◽  
Lévy Flight ◽  
Motor Speed ◽  
Levy Flight ◽  
Speed Regulation

This paper deals with the design of an optimally performed proportional–integral–derivative (PID) controller utilized for speed control of a direct current (DC) motor. To do so, a novel hybrid algorithm was proposed which employs a recent metaheuristic approach, named Lévy flight distribution (LFD) algorithm, and a simplex search method known as Nelder–Mead (NM) algorithm. The proposed algorithm (LFDNM) combines both LFD and NM algorithms in such a way that the good explorative behaviour of LFD and excellent local search capability of NM help to form a novel hybridized version that is well balanced in terms of exploration and exploitation. The promise of the proposed structure was observed through employment of a DC motor with PID controller. Optimum values for PID gains were obtained with the aid of an integral of time multiplied absolute error objective function. To verify the effectiveness of the proposed algorithm, comparative simulations were carried out using cuckoo search algorithm, genetic algorithm and original LFD algorithm. The system behaviour was assessed through analysing the results for statistical and non-parametric tests, transient and frequency responses, robustness, load disturbance, energy and maximum control signals. The respective evaluations showed better performance of the proposed approach. In addition, the better performance of the proposed approach was also demonstrated through experimental verification. Further evaluation to demonstrate better capability was performed by comparing the LFDNM-based PID controller with other state-of-the-art algorithms-based PID controllers with the same system parameters, which have also confirmed the superiority of the proposed approach.

Adaptive state-feedback stabilization of stochastic nonholonomic systems with an unknown time-varying delay and perturbations

2020 ◽  
pp. 014233122097417
Author(s):  
Qinghui Du
Keyword(s):  
State Feedback ◽  
Nonholonomic Systems ◽  
Feedback Stabilization ◽  
Feedback Controller ◽  
Input State ◽  
Time Varying ◽  
Initial State ◽  
State Feedback Controller ◽  
Time Varying Delay ◽  
Varying Delay

The problem of adaptive state-feedback stabilization of stochastic nonholonomic systems with an unknown time-varying delay and perturbations is studied in this paper. Without imposing any assumptions on the time-varying delay, an adaptive state-feedback controller is skillfully designed by using the input-state scaling technique and an adaptive backstepping control approach. Then, by adopting the switching strategy to eliminate the phenomenon of uncontrollability, the proposed adaptive state-feedback controller can guarantee that the closed-loop system has an almost surely unique solution for any initial state, and the equilibrium of interest is globally asymptotically stable in probability. Finally, the simulation example shows the effectiveness of the proposed scheme.

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