PID Controllers Design for a Quadrotor System Using Teaching Learning Based Optimization

2021 ◽  
Vol 16 ◽  
pp. 94-109
Author(s):  
Naima Bouhabza ◽  
Kamel Kara ◽  
Mohamed Laid Hadjili
Keyword(s):  
Degrees Of Freedom ◽  
Sliding Mode ◽  
Absolute Error ◽  
Pid Controllers ◽  
Tracking Accuracy ◽  
Proportional Integral ◽  
Response Characteristics ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
Quadrotor System

Optimized Proportional Integral Derivative controllers are designed to control the translational and rotational motions of a quadrotor system with six degrees of freedom. The teaching learning based optimization algorithm is used to obtain the proportional, integral and derivative gains of six PID controllers so that the integral time absolute error criterion is minimized. The control objective, is to enforce the horizontal position, altitude and yaw angle of the quadrotor to track their desired reference trajectories while stabilizing its roll and pitch angles. The efficiency and the control performance of the proposed scheme are demonstrated through numerical simulation and compared with those of the PID controllers designed using genetic algorithm, the sliding mode control and other control techniques proposed in the literature. The simulation study shows the good performance of the proposed control scheme in terms of transient response characteristics, tracking accuracy and disturbance rejection.

Evaluation of Sliding Mode and Proportional-Integral-Derivative Controlled Structures with an Active Mass Damper

2005 ◽  
Vol 11 (3) ◽  
pp. 397-406 ◽  
Author(s):  
R. Guclu ◽  
A. Sertbas
Keyword(s):  
Degrees Of Freedom ◽  
Sliding Mode ◽  
Control Voltage ◽  
Proportional Integral Derivative ◽  
Active Mass ◽  
Proportional Integral ◽  
Mass Damper ◽  
Active Mass Damper ◽  
And Performance ◽  
Controlled Structures

In this paper, both a sliding mode controller (SMC) and proportional-integral-derivative (PID) controller are designed for a multi-degrees-of-freedom structure, which has an active mass damper (AMD) to suppress earthquakeor wind-induced vibration. Since the model might have uncertainties and/or parameter changes, a SMC has been included because of its robust character and performance. The structural system has five degrees of freedom and has been simulated against an initial displacement of the first floor. At the end of the paper, we present the time histories of the first floor, top floor, and AMD displacements, the control voltage and frequency response of the uncontrolled, PID controlled, and sliding mode controlled structures, and we discuss the results.

scholarly journals Modelling an Industrial Robot and Its Impact on Productivity

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 769
Author(s):  
Carlos Llopis-Albert ◽  
Francisco Rubio ◽  
Francisco Valero
Keyword(s):  
Degrees Of Freedom ◽  
Sliding Mode ◽  
Industrial Robot ◽  
System Stability ◽  
Optimal Time ◽  
Tracking Accuracy ◽  
Six Degrees Of Freedom ◽  
Trade Offs ◽  
Fuzzy Sliding Mode ◽  
Pareto Fronts

This research aims to design an efficient algorithm leading to an improvement of productivity by posing a multi-objective optimization, in which both the time consumed to carry out scheduled tasks and the associated costs of the autonomous industrial system are minimized. The algorithm proposed models the kinematics and dynamics of the industrial robot, provides collision-free trajectories, allows to constrain the energy consumed and meets the physical characteristics of the robot (i.e., restriction on torque, jerks and power in all driving motors). Additionally, the trajectory tracking accuracy is improved using an adaptive fuzzy sliding mode control (AFSMC), which allows compensating for parametric uncertainties, bounded external disturbances and constraint uncertainties. Therefore, the system stability and robustness are enhanced; thus, overcoming some of the limitations of the traditional proportional-integral-derivative (PID) controllers. The trade-offs among the economic issues related to the assembly line and the optimal time trajectory of the desired motion are analyzed using Pareto fronts. The technique is tested in different examples for a six-degrees-of-freedom (DOF) robot system. Results have proved how the use of this methodology enhances the performance and reliability of assembly lines.

scholarly journals Design of an Optimal Fuzzy Controller of an Under-Actuated Manipulator Based on Teaching-Learning-Based Optimization

2019 ◽  
Vol 13 (3) ◽  
pp. 166-172
Author(s):  
Mohammad Javad Mahmoodabadi ◽  
Amineh Yazdizadeh Baghini
Keyword(s):  
Control Method ◽  
Fuzzy Controller ◽  
Initial Conditions ◽  
Absolute Error ◽  
State Variables ◽  
Friction Forces ◽  
Time Integral ◽  
Tlbo Algorithm ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

Abstract In this paper, an optimal fuzzy controller based on the Teaching-Learning-Based Optimization (TLBO) algorithm has been presented for the stabilization of a two-link planar horizontal under-actuated manipulator with two revolute (2R) joints. For the considered fuzzy control method, a singleton fuzzifier, a centre average defuzzifier and a product inference engine have been used. The TLBO algorithm has been implemented for searching the optimum parameters of the fuzzy controller with consideration of time integral of the absolute error of the state variables as the objective function. The proposed control method has been utilized for the 2R under-actuated manipulator with the second passive joint wherein the model moves in the horizontal plane and friction forces have been considered. Simulation results of the offered control method have been illustrated for the stabilization of the considered robot system. Moreover, for different initial conditions, the effectiveness and the robustness of the mentioned strategy have been challenged.

scholarly journals Voltage control of solid oxide fuel cell power plant based on intelligent proportional integral-adaptive sliding mode control with anti-windup compensator

2019 ◽  
Vol 42 (1) ◽  
pp. 116-130 ◽  
Author(s):  
AM Omer Abbaker ◽  
Haoping Wang ◽  
Yang Tian
Keyword(s):  
Sliding Mode Control ◽  
Pid Controller ◽  
Sliding Mode ◽  
Absolute Error ◽  
Solid Oxide ◽  
Adaptive Sliding Mode Control ◽  
Oxide Fuel ◽  
Proportional Integral ◽  
Adaptive Sliding Mode ◽  
Mode Control

Solid oxide fuel cell (SOFC) plant is considered a most important type in the field of fuel cells, which gives control difficulties such as fuel flow constraints, load disturbance, system nonlinearities, and parameter uncertainties. However, due to these difficulties, the voltage control of SOFC plant is extremely difficult. In this paper, a new intelligent proportional integral-adaptive sliding mode control (iPI-ASMC) with anti-windup compensator is proposed to control the output voltage of SOFC plant to keep up with the rated voltage. The referred iPI-ASMC is made of three sub-components: (i) an extended state observer (ESO)-based-intelligent proportional-integral to estimate the unknown dynamic, (ii) an adaptive sliding mode control is added to compensate the estimation error of unknown dynamic, and (iii) an anti-windup compensator based on back-calculation is used to deal with saturation problem which is caused by input constraints. Moreover, the effectiveness and efficiency of the proposed iPI-ASMC strategy is demonstrated by comparing with some other approaches such as conventional proportional integral-derivative (PID) controller, intelligent proportional-integral (iPI) and fuzzy PID controller. Corresponding simulation results show that the proposed iPI-ASMC approach provides better dynamic responses and outperforms to compared methods in term of settling time, peak overshoots, integral absolute error (IAE), integral square error (ISE), and integral time absolute error (ITAE).

scholarly journals Optimized Proportional-Integral-Derivative Controller for Upper Limb Rehabilitation Robot

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 826 ◽  
Author(s):  
M. Kamran Joyo ◽  
Yarooq Raza ◽  
S. Faiz Ahmed ◽  
M. M. Billah ◽  
Kushsairy Kadir ◽  
...  
Keyword(s):  
Upper Limb ◽  
Pid Controller ◽  
Degrees Of Freedom ◽  
Optimization Technique ◽  
Pid Controllers ◽  
Proportional Integral Derivative ◽  
Effective Response ◽  
Upper Limb Rehabilitation ◽  
Proportional Integral ◽  
Limb Rehabilitation

This paper proposes a nature inspired, meta-heuristic optimization technique to tune a proportional-integral-derivative (PID) controller for a robotic arm exoskeleton RAX-1. The RAX-1 is a two-degrees-of-freedom (2-DOFs) upper limb rehabilitation robotic system comprising two joints to facilitate shoulder joint movements. The conventional tuning of PID controllers using Ziegler-Nichols produces large overshoots which is not desirable for rehabilitation applications. To address this issue, nature inspired algorithms have recently been proposed to improve the performance of PID controllers. In this study, a 2-DOF PID control system is optimized offline using particle swarm optimization (PSO) and artificial bee colony (ABC). To validate the effectiveness of the proposed ABC-PID method, several simulations were carried out comparing the ABC-PID controller with the PSO-PID and a classical PID controller tuned using the Zeigler-Nichols method. Various investigations, such as determining system performance with respect to maximum overshoot, rise and settling time and using maximum sensitivity function under disturbance, were carried out. The results of the investigations show that the ABC-PID is more robust and outperforms other tuning techniques, and demonstrate the effective response of the proposed technique for a robotic manipulator. Furthermore, the ABC-PID controller is implemented on the hardware setup of RAX-1 and the response during exercise showed minute overshoot with lower rise and settling times compared to PSO and Zeigler-Nichols-based controllers.

Trajectory control method of stratospheric airships based on model predictive control in wind field

2017 ◽  
Vol 233 (2) ◽  
pp. 418-425 ◽  
Author(s):  
Jiashi Zhang ◽  
Xixiang Yang ◽  
Xiaolong Deng ◽  
Huijing Lin
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Wind Field ◽  
Degrees Of Freedom ◽  
Control Method ◽  
Sliding Mode ◽  
Trajectory Control ◽  
Flight Mechanics ◽  
Tracking Accuracy ◽  
Stratospheric Airship

Trajectory control of the stratospheric airship is an important but challenging task due to the characteristics of large inertia, long time delay, and large disturbance with wind field. Based on six-degrees-of-freedom dynamic model and kinetic model, this paper proposes a model predictive control method for the trajectory control of stratospheric airship in wind field. The aerostatics, aerodynamics, and flight mechanics are incorporated into the model with consideration of wind interference. By linearization of the dynamics equations by small perturbations method, a trajectory controller is designed using model predictive control method. A simulation program is developed from the model and is applied to analyze the control response of the high altitude airship. Results show that the method has a fast response and high accuracy trajectory control in wind field. A simulation comparison of airship performance with model predictive control and traditional sliding mode control shows that model predictive control can give a higher tracking accuracy at initial stage.

Load Frequency Control of Interconnected Power System Using Teaching Learning Based Optimization

2015 ◽  
Vol 4 (1) ◽  
pp. 102-117 ◽  
Author(s):  
Adhit Roy ◽  
Susanta Dutta ◽  
Provas Kumar Roy
Keyword(s):  
Power System ◽  
Frequency Control ◽  
Load Frequency Control ◽  
Pid Controllers ◽  
Thermal System ◽  
Interconnected System ◽  
Interconnected Power System ◽  
Load Frequency ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

This paper presents the design and performance analysis of teaching learning based optimization (TLBO) algorithm based PID controller for load frequency control (LFC) of an interconnected power system. A two area reheat thermal system equipped with PID controllers which is widely used in literature is considered for the design and analysis purpose. The design objective is to improve the transient performance of the interconnected system. The power system dynamic performance is analyzed based on time response plots achieved with the implementation of designed optimal and sub-optimal LFC regulators in the wake of 1% load disturbance in one of the areas. The results of the TLBO optimized PID controllers on a two area reheat thermal system are compared with those of artificial bee colony (ABC) and differential evolution (DE) optimized PID controllers. The TLBO optimized controllers are found to be superior in terms of peak transient deviation, settling times, and dynamic oscillations.

scholarly journals Swarm-Based Design of Proportional Integral and Derivative Controllers Using a Compromise Cost Function: An Arduino Temperature Laboratory Case Study

Algorithms ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 315
Author(s):  
P. B. de Moura Oliveira ◽  
John D. Hedengren ◽  
E. J. Solteiro Pires
Keyword(s):  
Cost Function ◽  
Controller Design ◽  
Optimization Technique ◽  
Absolute Error ◽  
Performance Criteria ◽  
Pid Controllers ◽  
Control Effort ◽  
Proportional Integral ◽  
Trade Offs ◽  
And Control

Simple and easy to use methods are of great practical demand in the design of Proportional, Integral, and Derivative (PID) controllers. Controller design criteria are to achieve a good set-point tracking and disturbance rejection with minimal actuator variation. Achieving satisfactory trade-offs between these performance criteria is not easily accomplished with classical tuning methods. A particle swarm optimization technique is proposed to design PID controllers. The design method minimizes a compromise cost function based on both the integral absolute error and control signal total variation criteria. The proposed technique is tested on an Arduino-based Temperature Control Laboratory (TCLab) and compared with the Grey Wolf Optimization algorithm. Both TCLab simulation and physical data show that satisfactory trade-offs between the performance and control effort are enabled with the proposed technique.

Understanding Conformational Entropy in Small Molecules

2020 ◽  
Author(s):  
Lucian Chan ◽  
Garrett Morris ◽  
Geoffrey Hutchison
Keyword(s):  
Small Molecules ◽  
Degrees Of Freedom ◽  
Absolute Error ◽  
Low Energy ◽  
Standard Entropy ◽  
Free Energies ◽  
Conformational Entropy ◽  
Wide Range ◽  
High Degree ◽  
Empirical Corrections

The calculation of the entropy of flexible molecules can be challenging, since the number of possible conformers grows exponentially with molecule size and many low-energy conformers may be thermally accessible. Different methods have been proposed to approximate the contribution of conformational entropy to the molecular standard entropy, including performing thermochemistry calculations with all possible stable conformations, and developing empirical corrections from experimental data. We have performed conformer sampling on over 120,000 small molecules generating some 12 million conformers, to develop models to predict conformational entropy across a wide range of molecules. Using insight into the nature of conformational disorder, our cross-validated physically-motivated statistical model can outperform common machine learning and deep learning methods, with a mean absolute error ≈4.8 J/mol•K, or under 0.4 kcal/mol at 300 K. Beyond predicting molecular entropies and free energies, the model implies a high degree of correlation between torsions in most molecules, often as- sumed to be independent. While individual dihedral rotations may have low energetic barriers, the shape and chemical functionality of most molecules necessarily correlate their torsional degrees of freedom, and hence restrict the number of low-energy conformations immensely. Our simple models capture these correlations, and advance our understanding of small molecule conformational entropy.

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