scholarly journals PASSIVITY BASED FRACTIONAL ORDER ADAPTIVE CONTROL OF DEPTH OF ANESTHESIA

2019 ◽  
Vol 49 (3) ◽  
pp. 155-161
Author(s):  
M. BOROOJERDI ALAVI ◽  
M. TABATABAEI
Keyword(s):  
Fractional Order ◽  
Adaptive Controller ◽  
Pole Placement ◽  
Model Parameters ◽  
Depth Of Anesthesia ◽  
Model Uncertainties ◽  
Adaptation Mechanism ◽  
Polynomial Coefficients ◽  
Simulation Results ◽  
Model Reference Adaptive

In this paper, a passivity based model reference adaptive controller with fractional-order adaptation mechanism is utilized for control of depth of anesthesia. The propofol infusion rate is adjusted to reach an appropriate Bispectral Index (BIS). The Pharmacokinetic-Pharmacodynamic (PK-PD) model is employed to model the distribution of propofol in patient body. Since, the PK-PD model parameters depend on physical specifications of patient, employing an adaptive controller to control this system is inevi-table. The utilized controller is a pole placement con-troller in which its polynomial coefficients are ad-justed according to a fractional-order adaptation mechanism. Simulation results on several patients demonstrate the efficiency of the proposed method in the presence of disturbance, noise, and model uncertainties.

Synchronization of Chaotic Fractional-Order Systems via Fractional-Order Adaptive Controller

2011 ◽  
Vol 109 ◽  
pp. 333-339 ◽  
Author(s):  
Ali Fayazi
Keyword(s):  
Fractional Order ◽  
Pid Controller ◽  
Adaptive Controller ◽  
Control Signal ◽  
Fractional Order Systems ◽  
Sliding Surface ◽  
Adaptation Mechanism ◽  
Simulation Results ◽  
And Performance ◽  
Fractional Order Controller

In this paper, an adaptive fractional-order controller has been designed for synchronization of chaotic fractional-order systems. This controller is a fractional PID controller, which the coefficients will be tuned according to a proper adaptation mechanism. PID coefficients are updated using the gradient method when a proper sliding surface is chosen. To illustrate the effectiveness and performance of the controller, the proposed controller implements on a pair of topologically inequivalent chaotic fractional-order systems. The Genesio-Tessi and Coullet systems. Performance of fractional-order adaptive PID controller (PαIλDμ) on the basis of speed of synchronization, error of synchronization, and level of control signal, is compared with the conventional ones (adaptive PID controller) and sliding mod controller (SMC). The simulation results reducing the level of control signal indicate the significance of the proposed controller.

Adaptive Consensus Tracking for Fractional-Order Linear Time Invariant Swarm Systems

2014 ◽  
Vol 9 (3) ◽  
Author(s):  
Mojtaba Naderi Soorki ◽  
Mohammad Saleh Tavazoei
Keyword(s):  
Fractional Order ◽  
Linear Time ◽  
Adaptive Controller ◽  
Order Linear ◽  
Linear Time Invariant ◽  
Consensus Tracking ◽  
Simulation Results ◽  
Interactive Dynamics ◽  
Time Invariant ◽  
Adaptive Stabilizer

This paper presents an adaptive controller to achieve consensus tracking for the fractional-order linear time invariant swarm systems in which the matrices describing the agent dynamics and the interactive dynamics between agents are unknown. This controller consists of two parts: an adaptive stabilizer and an adaptive tracker. The adaptive stabilizer guarantees the asymptotic swarm stability of the considered swarm system. Also, the adaptive tracker enforces the system agents to track a desired trajectory while achieving consensus. Numerical simulation results are presented to show the effectiveness of the proposed controller.

Switched Fractional Order Model Reference Adaptive Control for an Automatic Voltage Regulator

2021 ◽  
Author(s):  
Norelys Aguila-Camacho ◽  
Jorge E. García-Bustos ◽  
Eduardo I. Castillo-López
Keyword(s):  
Fractional Order ◽  
Adaptive Controller ◽  
System Response ◽  
Voltage Regulator ◽  
Order Model ◽  
Model Reference ◽  
Fractional Order Model ◽  
Automatic Voltage Regulator ◽  
Adaptive Laws ◽  
Model Reference Adaptive

Abstract This paper presents the design and implementation of a Switched Fractional Order Model Reference Adaptive Controller (SFOMRAC) for an Automatic Voltage Regulator (AVR). The fractional orders, adaptive gains and switching times of the controller adaptive laws are tuned offline, using Particle Swarm Optimization (PSO). The functional to be optimized contains not only parameters of the AVR response but also the control energy. The obtained controllers are compared to non switched Integer Order Model Reference Adaptive Controller (IOMRAC) and non switched Fractional Order Model Reference Adaptive Controller (FOMRAC) proposed previously for this process, showing that the SFOMRAC can improve both, the system response and the control energy used.

scholarly journals Leader-Follower Multimotor Speed Coordination via Adaptive Fuzzy Multiagent Consensus Scheme

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Suhaib Masroor ◽  
Chen Peng ◽  
Eman H. Alkhammash
Keyword(s):  
Fuzzy Logic Controller ◽  
Cross Coupling ◽  
Adaptive Controller ◽  
Consensus Algorithm ◽  
Pole Placement ◽  
Hybrid Controller ◽  
Simulation Purpose ◽  
Pole Placement Controller ◽  
Model Reference Adaptive ◽  
Belt System

Coordinated speed of interconnected motors has vast application in the industry. Typically, the smooth operation of the system relies on the coordinated speed of the multiple motors such as the conveyer belt system. Thus, the problem to have coordinated speed in a network-connected motor is mostly dealt with wire-connected architectures such as cross coupling. The presented study suggests a unique design to deal with the said problem by proposing a network model consisting of a DC chopper drive, termed as an ith agent of a network, while a leader-follower multiagent consensus algorithm is used, in a supervisory role, to ensure coordinated speed. Moreover, a hybrid controller (Fuzzy MRAC-RST), composed of Fuzzy logic controller, pole placement controller (F-RST), along with model reference adaptive controller (MRAC), is used to control the ith agent. The proposed hybrid controller along with MAS consensus algorithm forms an adaptive tracking performance and ensure coordinated speed. The MATLAB platform is used for simulation purpose, and the obtained results validate the design concept.

scholarly journals Adaptive control of time-invariant systems with discrete delays subject to multiestimation

2006 ◽  
Vol 2006 ◽  
pp. 1-27 ◽  
Author(s):  
M. De la Sen ◽  
S. Alonso
Keyword(s):  
Closed Loop ◽  
Dead Zone ◽  
Adaptive Controller ◽  
Pole Placement ◽  
Adaptation Mechanism ◽  
Discrete Delays ◽  
Estimation Scheme ◽  
Bounded Disturbances ◽  
True Basic ◽  
Minimum Residence Time

This paper deals with a robustly stable adaptive pole-placement-based controller for time-delay linear systems with unknown point delays within known intervals of sufficiently small lengths under unmodeled dynamics and bounded disturbances. A multiestimation scheme is used to improve the identification error and then to deal with possible errors between the true basic delays compared to that used in the regressor of the adaptive scheme. Each estimation scheme possess a relative dead zone for each estimation scheme which freezes the adaptation for small sizes of the adaptation error compared with the estimated size of the contribution of the uncertainties to the filtered output. All the estimation schemes run in parallel but only that, which is currently in operation, parameterizes the adaptive controller to generate the plant input at each time. A supervisory scheme chooses in real time the appropriate estimator subject to a minimum residence time which is the tool to ensure closed-loop stability under switching between the estimators in the estimation scheme. The dead zone adaptation mechanism prevents the closed-loop system against potential instability caused by uncertainties.

Adaptive Learning Control for Cooperation of Two Robots Manipulating a Rigid Object with Model Uncertainties

Robotica ◽  
1996 ◽  
Vol 14 (4) ◽  
pp. 365-373 ◽  
Author(s):  
Dong Sun ◽  
F Xiaolun Shi Yunhui Liu
Keyword(s):  
Adaptive Learning ◽  
High Speed ◽  
Adaptive Controller ◽  
Iterative Learning ◽  
Operational Mode ◽  
Transient Performance ◽  
Model Uncertainties ◽  
Rigid Object ◽  
Control Scheme ◽  
Simulation Results

SUMMARYIn this paper, an adaptive learning (A-L) control scheme is proposed for cooperation of two manipulators handling a rigid object with model uncertainties. For robots performing repetitive cooperating tasks, their operations are decomposed into two modes: the single operational mode and the repetitive operational mode on which the A-L controller is based. In the single operational mode, the controller is a learning based adaptive controller in which the robotic parameters are updated by using the information of the previous operation. In the repetitive operational mode, the controller is a model-based iterative learning controller. The advantages of the A-L controller lie in the fact that it can improve the transient performance as robots repeat operations at a high speed of the learning convergence. Simulation results ascertain that the A-L algorithm is effective in controlling two cooperated robots with model uncertainties.

scholarly journals Fractional order adaptive MRAC controller for an active suspension system

2020 ◽  
Vol 5 (2) ◽  
pp. 112-117
Author(s):  
SEIF EDDINE KHELAS ◽  
SAMIR LADACI ◽  
YASSINE BENSAFIA
Keyword(s):  
Adaptive Control ◽  
Fractional Order ◽  
Order Parameter ◽  
Active Suspension ◽  
Adaptive Controller ◽  
Suspension System ◽  
Conventional Model ◽  
Control Schemes ◽  
Classical Control ◽  
Model Reference Adaptive

This paper investigates the use of fractional order operators in conventional model reference adaptive control (MRAC). A fractional adaptive controller is designed based on the use of a fractional-order parameter adjustment rule. Applied in numerical simulations for an active suspension system and compared with the conventional MRAC, it is shown that the performances of FOMRAC are superior to classical control schemes.

Model Reference Adaptive Control on a Hydraulic Servo System

2011 ◽  
Vol 268-270 ◽  
pp. 505-508
Author(s):  
Zhi Yong Qu ◽  
Zheng Mao Ye
Keyword(s):  
Servo System ◽  
Adaptive Controller ◽  
Fast Response ◽  
System Stability ◽  
Servo Systems ◽  
Hydraulic Servo ◽  
Hydraulic Servo System ◽  
Simulation Results ◽  
The Stability ◽  
Model Reference Adaptive

Hydraulic servo systems are usually used in industry. This kind of system is nonlinear in nature and generally difficult to control. The ordinary linear constant gain controller can cause overshoot or even loss of system stability. Application of adaptive controller to a nonlinear hydraulic servo system is investigated in this paper. The dynamic model of the system is given and the stability is also analyzed using Popov's criterion. The steady state error can be eliminated using adaptive controller combined with an integration term. Simulation results show the performance of adaptive controller with fast response and less overshoot

scholarly journals Fractional-Order Closed-Loop Model Reference Adaptive Control for Anesthesia

Algorithms ◽  
2018 ◽  
Vol 11 (7) ◽  
pp. 106 ◽  
Author(s):  
Gerardo Navarro-Guerrero ◽  
Yu Tang
Keyword(s):  
Adaptive Control ◽  
Fractional Order ◽  
Closed Loop ◽  
Adaptive Controller ◽  
Model Reference Adaptive Control ◽  
Loop Model ◽  
Unknown Parameters ◽  
Model Reference ◽  
State Measurement ◽  
Model Reference Adaptive

The design of a fractional-order closed-loop model reference adaptive control (FOCMRAC) for anesthesia based on a fractional-order model (FOM) is proposed in the paper. This proposed model gets around many difficulties, namely, unknown parameters, lack of state measurement, inter and intra-patient variability, and variable time-delay, encountered in controller designs based on the PK/PD model commonly used for control of anesthesia, and allows to design a simple adaptive controller based on the Lyapunov analysis. Simulations illustrate the effectiveness and robustness of the proposed control.

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