Modeling and Control of Flexible Link Manipulators

Autonomous maritime navigation and offshore operations have gained wide attention with the aim of reducing operational costs and increasing reliability and safety. Offshore operations, such as wind farm inspection, sea farm cleaning, and ship mooring, could be carried out autonomously or semi-autonomously by mounting one or more long-reach robots on the ship/vessel. In addition to offshore applications, long-reach manipulators can be used in many other engineering applications such as construction automation, aerospace industry, and space research. Some applications require the design of long and slender mechanical structures, which possess some degrees of flexibility and deflections because of the material used and the length of the links. The link elasticity causes deflection leading to problems in precise position control of the end-effector. So, it is necessary to compensate for the deflection of the long-reach arm to fully utilize the long-reach lightweight flexible manipulators. This thesis aims at presenting a unified understanding of modeling, control, and application of long-reach flexible manipulators. State-of-the-art dynamic modeling techniques and control schemes of the flexible link manipulators (FLMs) are discussed along with their merits, limitations, and challenges. The kinematics and dynamics of a planar multi-link flexible manipulator are presented. The effects of robot configuration and payload on the mode shapes and eigenfrequencies of the flexible links are discussed. A method to estimate and compensate for the static deflection of the multi-link flexible manipulators under gravity is proposed and experimentally validated. The redundant degree of freedom of the planar multi-link flexible manipulator is exploited to minimize vibrations. The application of a long-reach arm in autonomous mooring operation based on sensor fusion using camera and light detection and ranging (LiDAR) data is proposed.publishedVersio

Sensorless wave based control

Mechanical waves naturally propagate through dynamical systems that are subjected to initial excitation. These mechanical waves carry enough information about the dynamical system including its dynamics and parameters, in addition to the externally applied forces or torques due to the system's interaction with the environment. In other words, mechanical waves carry all the dynamical system's information in a coupled fashion. This thesis proposes an estimation algorithm that enables estimating flexible systems' dynamics, parameters, externally applied forces and disturbances. The proposed algorithm is implemented on a lumped system with an actuator located at one of its boundaries, that is used as a single platform for measurements where actuator's current and velocity are measured and used to estimate the reflected mechanical waves. Only these two measurements from the actuator are required to accomplish the motion and vibration control, keeping the dynamical system free from any attached sensors by considering the reflected mechanical waves as a natural feedback from the system. In this thesis the notion of position estimation is proposed including both rigid and flexible motion estimation, where the position of each lumped mass is estimated and experimentally compared with the actual measurements. This in turn implies the possibility of using these position estimates as a virtual feedback to the controllers instead of using the actual sensor's feedback. System's global behavior can be investigated by monitoring lumped system dynamics, to guarantee the accomplishment of motion control task and the minimization of system's residual vibrations. Since the dynamics of the system can be obtained, the externally applied forces or torques can be estimated. The experimental results show the validity of the proposed algorithm and the possibility of using two actuator parameters in order to estimate the uniform system parameters, rigid system's position, flexible system's lumped mass positions and external disturbances due to system's interaction with the environment