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

Abstract

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

Roll Movement Control of a Spray Boom Structure Using Active Force Control with Artificial Neural Network Strategy

“…The results of these studies revealed the AFC strategy is more effective in attenuating the amplitude of undesired vibrations of the system. Since the most crucial computational part in AFC is the approximation of the estimated mass or moment of inertia of any dynamical system, the use of artificial intelligence (AI) method such as artificial neural network (ANN) and iterative learning (IL) to determine this parameter has been initiated by Mailah (1998 (Mailah, 1998). Also, to the author knowledge, there is no available literature on the application of AFC approach to control the unwanted vibration of a spray boom directly.…”

“…Recently, the active control suspensions are still less applied while undesired boom oscillations have been characterized as a great restriction for chemical distribution uniformity (Anthonis et al, 2005). Additionally, conventional control method which is simple and relatively stable could just guarantee satisfactory performance at a very low speed operation (Mailah, 1998).…”

“…Where Q', I', and T ' are estimated disturbances torque, estimated inertia, and actuated torque, respectively (Mailah, 1998). If disturbance torque can be estimated accurately, it can be utilized to decouple the applied torque from the actual disturbances torque.…”

Reducing the Vibrations of an Unbalanced Rotary Engine by Active Force Control

“…This method turned out to be very robust and effective in controlling a robot arm. Later on other researchers successfully implemented this procedure for a robot arm by combining artificial intelligent (Mailah, 1998;Mailah & Rahim, 2000), as well as controlling pneumatic actuators (Mailah et al, 2009). The AFC method was very capable and successful for decreasing friction induced vibrations (Hashemi-Dehkordi et al, 2009a;Hashemi-Dehkordi et al, 2010;Hashemi-Dehkordi et al, 2009b;Hashemi-Dehkordi et al, 2012;Hashemi-Dehkordi et al, 2014).…”

Designing a 2-Degree of Freedom Model of an Unbalanced Engine and Reducing its Vibrations by Active Control

“…The AFC method was first proposed by Hewit and Burdess (1981), and it was noticed that the AFC method was very robust and operative in controlling a robot arm. Afterward, other researchers implemented the AFC method very successfully for a robot arm by taking into consideration artificial intelligence techniques (Mailah, 1998;Mailah & Rahim, 2000), in addition to controlling actuators of pneumatic types (Mailah et al, 2009). The AFC method was successful for reducing friction induced vibrations (Hashemi-Dehkordi et al, 2009a;HashemiDehkordi et al, 2009b;Hashemi-Dehkordi et al, 2010;Hashemi-Dehkordi et al, 2012;HashemiDehkordi et al, 2014).…”