scholarly journals Feedback Linearization Control of the Inertia Wheel Pendulum

2014 ◽  
Vol 14 (3) ◽  
pp. 96-109 ◽  
Author(s):  
Faculty of Automatics, Technical Un Enev
Keyword(s):  
Feedback Linearization ◽  
Wheel Speed ◽  
Control Law ◽  
Test Bed ◽  
Control Laws ◽  
Full State ◽  
Feedback Linearization Control ◽  
System Output ◽  
Inertia Wheel Pendulum ◽  
Definition Of

Abstract In this paper, two feedback linearizing control laws for the stabilization of the Inertia Wheel Pendulum are derived: a full-state linearizing controller, generalizing the existing results in literature, with friction ignored in the description and an inputoutput linearizing control law, based on a physically motivated definition of the system output. Experiments are carried out on a laboratory test bed with significant friction in order to test and verify the suggested performance and the results are presented and discussed. The main point to be made as a consequence of the experimental evaluation is the fact that actually the asymptotic stabilization was not achieved, but rather a limit cycling behavior was observed for the full-state linearizing controller. The input-output linearizing controller was able to drive the pendulum to the origin, with the wheel speed settling at a finite value

OUTPUT FEEDBACK CONTROL OF UNIFIED CHAOTIC SYSTEMS BASED ON FEEDBACK PASSIVITY

2010 ◽  
Vol 20 (05) ◽  
pp. 1519-1525 ◽  
Author(s):  
TEERAWAT SANGPET ◽  
SUWAT KUNTANAPREEDA
Keyword(s):  
Chaos Control ◽  
Chaotic Systems ◽  
Output Feedback Control ◽  
Control Law ◽  
Control Laws ◽  
Unified Chaotic Systems ◽  
System Output ◽  
Simulation Results ◽  
Passivity Based Control ◽  
System States

Recently, the concept of feedback passivity-based control has drawn attention to chaos control. In all existing papers, the implementations of passivity-based control laws require the system states for feedback. In this paper, a passivity-based control law which only requires the knowledge of the system output is proposed. Simulation results are provided to show the effectiveness of the proposed solution.

scholarly journals Combination of Two Nonlinear Techniques Applied to a 3-DOF Helicopter

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
P. Ahmadi ◽  
M. Golestani ◽  
S. Nasrollahi ◽  
A. R. Vali
Keyword(s):  
Convergence Rate ◽  
Feedback Linearization ◽  
Sliding Mode ◽  
Control Law ◽  
Sliding Mode Controller ◽  
Control Methods ◽  
Control Effort ◽  
Control Techniques ◽  
Control Scheme ◽  
Feedback Linearization Control

A combination of two nonlinear control techniques, fractional order sliding mode and feedback linearization control methods, is applied to 3-DOF helicopter model. Increasing of the convergence rate is obtained by using proposed controller without increasing control effort. Because the proposed control law is robust against disturbance, so we only use the upper bound information of disturbance and estimation or measurement of the disturbance is not required. The performance of the proposed control scheme is compared with integer order sliding mode controller and results are justified by the simulation.

Design of Optimal Control for a Stratospheric Airship Based on Generalized Coordinate Frame

2012 ◽  
Vol 466-467 ◽  
pp. 587-591
Author(s):  
Ming Zhu ◽  
Yong Mei Wu ◽  
Ze Wei Zheng
Keyword(s):  
Optimal Control ◽  
Feedback Linearization ◽  
Coordinate Frame ◽  
Control Law ◽  
Nonlinear Dynamic Model ◽  
Stratospheric Airship ◽  
Feedback Linearization Control ◽  
And Performance ◽  
Six Degree Of Freedom ◽  
Generalized Coordinate

An optimal control is presented in this paper. First, nonlinear dynamic model of a six degree of freedom stratospheric airship, traditional and full-actuated, is built based on generalized coordinate frame. Second, optimal control law is determined by Hamilton function and performance index function. This optimal control can be regarded as extension of feedback linearization control law.

scholarly journals A Multi-Index Feedback Linearization Control for a Buck-Boost Converter

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1496
Author(s):  
Xiaocong Li ◽  
Xin Chen
Keyword(s):  
Control Strategy ◽  
Feedback Linearization ◽  
Structural Characteristics ◽  
Dynamic Performance ◽  
Boost Converter ◽  
Control Law ◽  
Output Function ◽  
Multi Index ◽  
Feedback Linearization Control ◽  
Linear Subsystem

Due to the nonlinear and nonminimum phase characteristics of the buck-boost converter, the design of its controller has always been a challenging problem. In this paper, a multi-index feedback linearization control strategy is proposed to design the controller of the buck-boost converter. Firstly, by constructing an appropriate output function, the original nonlinear system is mapped into a combination of a linear subsystem and a nonlinear subsystem. Then, according to the structural characteristics of these two subsystems, the linear optimal control theory is adopted for the control design of the linear subsystem to make it have a good output performance, while for the nonlinear subsystem, the coefficient of the output function is adjusted to ensure its stability. Finally, based on the Hartman–Grobman theorem, the internal mechanism and coefficient adjustment basis of the proposed method are revealed; that is, by adjusting the coefficient of the output function and the feedback coefficient of the linear control law, the poles of the system are configured to achieve the purpose of adjusting the static and dynamic performance of the system. The simulation results show the feasibility and superiority of using the multi-index feedback linearization control strategy to design the nonlinear control law of the buck-boost converter.

Adaptive Predictor-Based Output Feedback Control for a Class of Unknown MIMO Linear Systems: Experimental Results

2014 ◽  
Author(s):  
Chuong Hoang Nguyen ◽  
Alexander Leonessa
Keyword(s):  
Feedback Control ◽  
Linear Systems ◽  
Output Feedback ◽  
Reference System ◽  
State Feedback Control ◽  
Experimental Results ◽  
Control Input ◽  
Test Bed ◽  
Full State ◽  
System Output

Experimental results are presented to validate a recently developed adaptive output feedback controller for a general class of unknown MIMO linear systems. The control approach relies on three components, a predictor, a reference model, and a controller. Specifically, since the predictor is designed to predict the system’s output for any admissible control input, controlling the uncertain system is reduced to controlling the predictor, which is a virtual system with known dynamics and full state available. Subsequently, a full state feedback control law is designed to control the predictor output to approach the reference system, while the reference system tracks the desired trajectory while accounting for the actuator amplitude and rate saturation constraints. Ultimately, the control objective of driving the actual system output to track the desired trajectories is achieved by showing that the system output, the predictor output, and the reference system trajectories all converge to each other. Theorems and the step-by-step implementation of the control strategy are presented. Finally, the control’s efficacy is illustrated by a real time implementation of the proposed algorithm on an actual helicopter test bed.

Design and development of a backstepping controller autopilot for fixed-wing UAVs

2021 ◽  
pp. 1-27
Author(s):  
D. Sartori ◽  
F. Quagliotti ◽  
M.J. Rutherford ◽  
K.P. Valavanis
Keyword(s):  
Real Time ◽  
Control Law ◽  
Parametric Uncertainties ◽  
Hardware In The Loop ◽  
Aircraft Control ◽  
Backstepping Approach ◽  
Small Uav ◽  
Backstepping Controller ◽  
Definition Of ◽  
Performance Results

Abstract Backstepping represents a promising control law for fixed-wing Unmanned Aerial Vehicles (UAVs). Its non-linearity and its adaptation capabilities guarantee adequate control performance over the whole flight envelope, even when the aircraft model is affected by parametric uncertainties. In the literature, several works apply backstepping controllers to various aspects of fixed-wing UAV flight. Unfortunately, many of them have not been implemented in a real-time controller, and only few attempt simultaneous longitudinal and lateral–directional aircraft control. In this paper, an existing backstepping approach able to control longitudinal and lateral–directional motions is adapted for the definition of a control strategy suitable for small UAV autopilots. Rapidly changing inner-loop variables are controlled with non-adaptive backstepping, while slower outer loop navigation variables are Proportional–Integral–Derivative (PID) controlled. The controller is evaluated through numerical simulations for two very diverse fixed-wing aircraft performing complex manoeuvres. The controller behaviour with model parametric uncertainties or in presence of noise is also tested. The performance results of a real-time implementation on a microcontroller are evaluated through hardware-in-the-loop simulation.

Non-Linear Control of Tilting-Quadcopter Using Feedback Linearization Based Motion Control

2014 ◽  
Author(s):  
Alireza Nemati ◽  
Manish Kumar
Keyword(s):  
Control System ◽  
Dynamic Model ◽  
Motion Control ◽  
Feedback Linearization ◽  
Linear Control ◽  
Control Architecture ◽  
Simulation Studies ◽  
Nonlinear Dynamic Model ◽  
Feedback Linearization Control ◽  
Arbitrary Trajectory

In this paper, a nonlinear control of a tilting rotor quadcopter is presented. The overall control architecture is divided into two sub-controllers. The first controller is based on the feedback linearization control derived from the dynamic model of the tilting quadcopter. This controls the pitch, roll, and yaw motions required for movement along an arbitrary trajectory in space. The second controller is based on two PD controllers which are used to control the tilting of the quadcopter independently along the pitch and the yaw directions respectively. The overall control enables the quadcopter to combine tilting and movement along a desired trajectory simultaneously. Simulation studies are presented based on the developed nonlinear dynamic model of the tilting rotor quadcopter to demonstrate the validity and effectiveness of the overall control system for an arbitrary trajectory tracking.

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