An intelligent active vibration control system for combined single-link mechatronic robot flexible manipulator

The abstract is a digest of the entire thesis and should be given the same consideration In recent years, mechatronics and robotics have emerged as interdisciplinary fields that integrate mechanical, electrical, and software engineering principles. These disciplines are vital for designing and developing complex systems such as flexible mechatronics and robotics manipulators, characterized by high precision, accuracy, and automation. In efforts to enhance the performance and versatility of Robotics Flexible Manipulators (RFMs), various methods have been explored to address the challenges they pose compared to Robotics Rigid Manipulators (RRMs). RFMs, constructed from lightweight, elastic materials, can bend and flex, making them suitable for applications in space exploration, medical robotics, and aerospace, where weight reduction and adaptability are essential. This flexibility allows for efficient operation in confined, dynamic environments but introduces issues like vibrations that complicate control and precision. Originally designed for space, RFMs have expanded into fields such as medical and military. A significant ongoing challenge is vibration control, where passive methods struggle to maintain accuracy. This research, develops an Intelligent Active Vibration Control (IAVC) strategy for the Mechatronics Test Rig System (MTRS) using the NI-my-RIO controller board, introducing a New Gyroscope Combined Actuator Model (NGCAM) and a Modified Differential Evolution Optimization Algorithm (MDEOA) to optimize PID controller parameters and control mechanical vibrations in RFMs. Active control strategies utilize intelligent systems and real-time data, essential for managing vibrations and enhancing the precision of flexible manipulators. As energy-efficient robotics advance, systems like IAVC play a critical role in improving RFM performance in precision-demanding industries. This study, initially, includes a Combined Plant (CP) consisting of a combined dual Flexible Manipulator (FM) model driven by a Direct Current Servo Motor (DCSM), secondly, a Combined Actuator (CA) or NGCAM integrating a Gyroscope (G) as the main actuator and a Direct Current Motor (DCM) as an auxiliary actuator. Intelligent methods enhance the coupled single-link structures, although controlling vibration under disturbances remains challenging. Here, this research study, tunes a PID controller using MDEOA for a Combined Single-Link Robotics Flexible Manipulator (CSLRFM). The MTRS simulates real dynamics, and the gyroscope’s rotational and kinetic energy in CA optimizes the system's nonlinear behavior under multiple degrees of freedom using IAVC techniques. Controllers are developed using Differential Evolution Optimization (DEO) and Active Vibration Control (AVC) to optimize PID parameters for vibration suppression, validated through analytical and experimental assessments. The MDEOA method improves IAVC by optimizing PID gains, enhancing vibration control. Active Force Control (AFC), is integrated with AVC, Fuzzy Logic (FL), and MDEOA in result of (AVC-FL-PID) and (AVC-DEO-PID) as two defined controllers, which are evaluated for stability and precision. MATLAB Simulink simulations and LabVIEW experimental validation demonstrate that the (AVC-DEO-PID) controller outperforms PID, Fuzzy, (Fuzzy-PID), (AVC-PID), and (AVC-FL-PID) controllers, achieving up to 93% improvement in rise and settling times, eliminating overshoot, and reducing steady-state error by 90-100%. Additionally, (AVC-DEO-PID) controller achieves 49-78% faster peak times with high consistency in simulations and experiments, demonstrating robustness in real applications. The (AVC-DEO-PID) controller excels in rigid motion tracking and endpoint vibration suppression, reducing settling time by 92% over traditional PID and showing significant gains in rise time and steady-state error. Here, this Modified Differential Evolution Optimization Algorithm (MDEOA) with Active Vibration Control (AVC) as (AVC-DEO) highlights its efficacy in optimizing RFMs performance.

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