Intelligent active force control of a helicopter seat suspension using iterative learning algorithm

2016 ◽  
Author(s):  
Samaneh Ahmadi ◽  
Mohammad Gohari ◽  
Mona Tahmasebi
Keyword(s):  
Force Control ◽  
Learning Algorithm ◽  
Iterative Learning ◽  
Active Force ◽  
Seat Suspension ◽  
Active Force Control

Hardware-in-the-Loop Simulation for Active Force Control with Iterative Learning Applied to an Active Vehicle Suspension System

2013 ◽  
Vol 465-466 ◽  
pp. 801-805
Author(s):  
Rosmazi Rosli ◽  
Musa Mailah ◽  
Gigih Priyandoko
Keyword(s):  
Force Control ◽  
Control Method ◽  
Learning Algorithm ◽  
Suspension System ◽  
Iterative Learning ◽  
Practical Implementation ◽  
Hardware In The Loop ◽  
Active Force ◽  
Physical Test ◽  
Active Force Control

The paper focuses on the practical implementation of a novel control method to an automotive suspension system using active force control (AFC) with iterative learning algorithm (ILA) and proportional-integral-derivative (PID) control strategy. The overall control system to be known as AFC-IL scheme essentially comprises three feedback control loops to cater for a number of specific tasks, namely, the innermost loop for the force tracking of the pneumatic actuator using PI controller, intermediate loops applying AFC with ILA strategy for the compensation of the disturbances and the outermost loop using PID controller for the computation of the desired force. A number of experiments were carried out on a physical test rig with hardware-in-the-loop simulation (HILS) feature that fully incorporates the theoretical elements. The performance of the proposed control method was evaluated and benchmarked to examine the effectiveness of the system in suppressing the vibration effect of the suspension system. It was found that the experimental results demonstrate the superiority of the active suspension system with proposed AFC-IL scheme compared to the PID and passive counterparts.

scholarly journals A Novel Voice-Coil Actuated Mini Crawler for In-Pipe Application Employing Active Force Control With Iterative Learning Algorithm

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 28156-28166
Author(s):  
Yaser Sabzehmeidani ◽  
Musa Mailah ◽  
Tang H. Hing ◽  
Sherif I. Abdelmaksoud
Keyword(s):  
Force Control ◽  
Learning Algorithm ◽  
Iterative Learning ◽  
Active Force ◽  
Active Force Control

Control of a Robot Arm Using Iterative Learning Algorithm with a Stopping Criterion

2012 ◽  
Author(s):  
Musa Mailah ◽  
Wun Shiung Jonathan Chong
Keyword(s):  
Force Control ◽  
Robot Control ◽  
Learning Algorithm ◽  
Iterative Learning ◽  
Operating Conditions ◽  
Robot Arm ◽  
Active Force ◽  
Stopping Criterion ◽  
Active Force Control ◽  
Control Scheme

Prestasi lasak bagi skema kawalan robot sangat perlu untuk memastikan robot dapat bekerja dengan berkesan seperti yang dikehendaki dalam persekitaran terbatas melibatkan gangguan, perubahan parameter, ketidaktentuan dan kepelbagaian keadaan operasi. Kajian yang dibuat adalah berkaitan dengan satu skema kawalan daya aktif dan algoritma pembelajaran berlelaran (AFCAIL) yang melibatkan satu ciri pembaikan dalam bentuk penggunaan kriteria memberhenti yang sesuai dimuatkan dalam strategi kawalan. Skema tersebut digunapakai terhadap sistem pengolah robotik planar berlengan–dua yang beroperasi secara mendatar. Kriteria memberhenti yang dicadangkan adalah reka bentuk untuk memberhentikan proses pembelajaran berlelaran apabila syarat atau keadaan berkaitan dengan kejituan ketika melakukan tugas serta perolehan matriks inersia anggaran pengolah yang dikehendaki dapat dipenuhi. Dengan cara demikian, robot dikatakan dapat beroperasi dengan baik sebagaimana yang diarahkan. Keberkesanan skema juga dikaji dengan mengambil kira beberapa keadaan bebanan dan operasi. Kata kunci: Robot; kawalan daya aktif; algoritma pembelajaran berlelaran; kriterion memberhenti The robust performance of a robot control scheme is vital to ensure that the robot accomplishes its tasks desirably in a constraint environment involving disturbances, parametric changes, uncertainties and varied operating conditions. The study introduces the Active Force Control and Iterative Learning Algorithm (AFCAIL) scheme with an improved feature in the form of a suitably designed stopping criterion incorporated in the control strategy. The scheme is applied to the control of a horiziontally operated robotic two–link planar manipulator. The proposed stopping criterion is specifically designed to halt the iterative learning process when the conditions related to the accuracy of the performed tasks and the acquisition of appropriate estimated inertia matrix of the robot arm are favourably met. In this way, the robot is said to perform desirably and excellently. The effectiveness of the scheme is also investigated by considering a number different loading and operating conditions. Key words: Robot; active force control; iterative learning algorithm; stopping criterion

Intelligent Adaptive Active Force Control of a Robotic Arm with Embedded Iterative Learning Algorithms

2012 ◽  
Author(s):  
Musa Mailah ◽  
Miaw Yong Ong
Keyword(s):  
Force Control ◽  
Learning Algorithms ◽  
Iterative Learning ◽  
Operating Conditions ◽  
Robotic Arm ◽  
Active Force ◽  
Inertia Matrix ◽  
Active Force Control ◽  
Controller Gain ◽  
On Line

Kawalan jitu dan lasak bagi satu sistem lengan robot atau pengolah adalah amat penting terutama sekali jika sistem mengalami pelbagai bentuk bebanan dan keadaan pengendalian. Kertas kerja ini memaparkan satu kaedah baru dan lasak untuk mengawal lengan robot menggunakan teknik pembelajaran secara berlelaran yang dimuatkan dalam strategi kawalan daya aktif. Sebanyak dua algoritma pembelajaran utama digunakan dalam kajian – yang pertama digunakan untuk menala gandaan pengawal secara automatik manakala yang satu lagi pula untuk menganggarkan matriks inersia pengolah. Kedua-dua parameter ini dihasilkan secara adaptif dan dalam talian ketika robot sedang menjalankan tugas menjejak trajektori dalam persekitaran tindakan daya gangguan. Dalam kajian ini, pengetahuan awal tentang kedua–dua nilai gandaan pengawal dan anggaran matriks inersia tidak wujud. Dengan demikian, suatu skema kawalan yang jitu dan lasak terhasil. Keberkesanan kaedah yang dicadangkan dapat ditentusahkan melalui hasil kajian yang diperoleh dan dibentangkan dalam kertas kerja ini. Kata kunci: Adaptif; kawalan daya aktif; pembelajaran berlelaran; matriks inersia; gandaan pengawal The robust and accurate control of a robotic arm or manipulator are of prime importance especially if the system is subjected to varying forms of loading and operating conditions. The paper highlights a novel and robust method to control a robotic arm using an iterative learning technique embedded in an active force control strategy. Two main iterative learning algorithms are utilized in the study – the first is used to automatically tune the controller gains while the second to estimate the inertia matrix of the manipulator. These parameters are adaptively computed on-line while the robot is executing a trajectory tracking task and subject to some forms of external disturbances. No priori knowledge of both the controller gains and the estimated inertia matrix are ever assumed in the study. In this way, an adaptive and robust control scheme is derived. The effectiveness of the method is verified and can be seen from the results of the work presented in this paper. Keywords: Adaptive; active force control; iterative learning; inertia matrix; controller gain

Semi Active Seat Suspension System using Modified Intelligent Active Force Control

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
R. Rosli ◽  
Z. Mohamed ◽  
G. Priyandoko
Keyword(s):  
Force Control ◽  
Ride Comfort ◽  
Mr Damper ◽  
Suspension System ◽  
Main Function ◽  
Control Force ◽  
Active Force ◽  
Damping Force ◽  
Seat Suspension ◽  
Active Force Control

This paper presents a modified intelligent active force control (AFC) control strategy in a semi active seat suspension system. The main actuator studied in this research is the Magneto-rheological (MR) damper. Since a semi-active device like MR damper can only dissipate energy so a modified version of AFC controller is needed. The modified AFC controller main function is to determine the appropriate control force. A Heaviside Step Function (HSF) is used to ensure the MR damper produce the desired damping force according to the control force generated by AFC controller. The phenomenological Bouc-Wen is used to study the effectiveness of the new AFC controller taking into account the dynamic response of the damper. Sinusoidal signals simulated as vibration sources are applied to the seat suspension system to investigate the improvement of ride comfort as well as to ascertain the new AFC controller robustness. Comparison of body acceleration signals from the passive suspension with AFC controller semi active seat suspension system shows up to to 45% improvement to the occupant ride comfort under different vibration intensities.

scholarly journals Knowledge-Based Trajectory Error Pattern Method Applied to an Active Force Control Scheme

1970 ◽  
Vol 3 (1) ◽  
Author(s):  
Endra Pitowarno, Musa Mailah, Hishamuddin Jamaluddin
Keyword(s):  
Force Control ◽  
Error Pattern ◽  
Robot Arm ◽  
Active Force ◽  
Trajectory Error ◽  
Track Error ◽  
Knowledge Based ◽  
Inertia Matrix ◽  
Active Force Control ◽  
Control Scheme

The active force control (AFC) method is known as a robust control scheme that dramatically enhances the performance of a robot arm particularly in compensating the disturbance effects. The main task of the AFC method is to estimate the inertia matrix in the feedback loop to provide the correct (motor) torque required to cancel out these disturbances. Several intelligent control schemes have already been introduced to enhance the estimation methods of acquiring the inertia matrix such as those using neural network, iterative learning and fuzzy logic. In this paper, we propose an alternative scheme called Knowledge-Based Trajectory Error Pattern Method (KBTEPM) to suppress the trajectory track error of the AFC scheme. The knowledge is developed from the trajectory track error characteristic based on the previous experimental results of the crude approximation method. It produces a unique, new and desirable error pattern when a trajectory command is forced. An experimental study was performed using simulation work on the AFC scheme with KBTEPM applied to a two-planar manipulator in which a set of rule-based algorithm is derived. A number of previous AFC schemes are also reviewed as benchmark. The simulation results show that the AFC-KBTEPM scheme successfully reduces the trajectory track error significantly even in the presence of the introduced disturbances.Key Words:  Active force control, estimated inertia matrix, robot arm, trajectory error pattern, knowledge-based.

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