Research on robot motion control and trajectory tracking based on agricultural seeding

With the development of science and technology, agricultural production has been gradually industrialized, and the use of robots instead of humans for seeding is one of the agricultural industrializations. This paper studied the seeding path planning and path tracking algorithms of the seeding robot, carried out experiments, and compared the improved proportion, integral, differential (PID) algorithm with the traditional PID control algorithm. The results demonstrated that both the improved and non-improved control algorithms played a good role in tracking on the straight path, but the improved control algorithm had a better tracking effect on the turning path; the displacement deviation and angle deviation of the tracking trajectory of the improved PID algorithm were reduced faster and more stable than the traditional PID algorithm; the tracking trajectory was shorter and the operation time of the robot was less under the improved PID algorithm than the traditional one

Gain-scheduled sliding-mode-type iterative learning control design for mechanical systems

In this paper, a novel gain-scheduled sliding-mode-type (SM-type) iterative learning (IL) control approach is proposed for the high-precision trajectory tracking of mechanical systems subject to model uncertainties and disturbances. Based on the SM variable, the proposed controller is synthesized involving a feedback regulation item, a feedforward learning item, and a robust switching item. The feedback regulation item is adopted to regulate the position and velocity tracking errors, the feedforward learning item is applied to handle the model uncertainties and repetitive disturbance, and the robust switching item is introduced to compensate the nonrepetitive disturbance and linearization residual error. Moreover, the gain-scheduled mechanism is employed for both the feedback regulation item and feedforward learning item to enhance the convergence speed. Convergence analysis illustrates that the position and velocity tracking errors can eventually regulate to zero under the proposed controller. By combining the advantages of both SM control and IL control, the proposed controller has strong robustness against model uncertainties and disturbances. Lastly, simulations and comparisons are provided to evaluate the efficiency and excellent performance of the proposed control approach

Iterative Learning Control of a Nonlinear Aeroelastic System despite Gust Load

The development of a control strategy appropriate for the suppression of aeroelastic vibration of a two-dimensional nonlinear wing section based on iterative learning control (ILC) theory is described. Structural stiffness in pitch degree of freedom is represented by nonlinear polynomials. The uncontrolled aeroelastic model exhibits limit cycle oscillations beyond a critical value of the free-stream velocity. Using a single trailing-edge control surface as the control input, a ILC law under alignment condition is developed to ensure convergence of state tracking error. A novel Barrier Lyapunov Function (BLF) is incorporated in the proposed Barrier Composite Energy Function (BCEF) approach. Numerical simulation results clearly demonstrate the effectiveness of the control strategy toward suppressing aeroelastic vibration in the presence of parameter uncertainties and triangular, sinusoidal, and graded gust loads

Incorporation of the influences of kinematics parameters and joints tilting for the calibration of serial robotic manipulators

Serial robotic manipulators are calibrated to improve and restore their accuracy and repeatability. Kinematics parameters calibration of a robot reduces difference between the model of a robot in the controller and its actual mechanism to improve accuracy. Kinematics parameter’s error identification in the standard kinematics calibration has been configuration independent which does not consider the influence of kinematics parameter on robot tool pose accuracy for a given configuration. This research analyses the configuration dependent influences of kinematics parameters error on pose accuracy of a robot. Based on the effect of kinematics parameters, errors in the kinematics parameters are identified. Another issue is that current kinematics calibration models do not incorporate the joints tilting as a result of joint clearance, backlash, and flexibility, which is critical to the accuracy of serial robotic manipulators, and therefore compromises a pose accuracy. To address this issue which has not been carefully considered in the literature, this research suggested an approach to model configuration dependent joint tilting and presents a novel approach to encapsulate them in the calibration of serial robotic manipulators. The joint tilting along with the kinematics errors are identified and compensated in the kinematics model of the robot. Both conventional and proposed calibration approach are tested experimentally, and the calibration results are investigated to demonstrate the effectiveness of this research. Finally, the improvement in the trajectory tracking accuracy of the robot has been validated with the help of proposed low-cost measurement set-up.Thesis (M.Phil.) (Research by Publication) -- University of Adelaide, School of Mechanical Engineering , 201

Nonlinear control of an exoskeleton seven degrees of freedom robot to realize an active and passive rehabilitation tasks

This doctoral thesis proposes the developments of an exoskeleton robot used to rehabilitate patients with upper-limb impairment, named ETS-MARSE robot. The developments included in this work are the design, and validation of a kinematic inverse solution and nonlinear control strategy for an upper limb exoskeleton robot. These approaches are used in passive and active rehabilitation motion in presence of dynamics and kinematics uncertainties and unexpected disturbances. Considering the growing population of post-stroke victims, there is a need to improve accessibility to physiotherapy by using the modern robotic rehabilitation technology. Recently, rehabilitation robotics attracted a lot of attention from the scientific community since it is able to overcome the limitations of conventional physical therapy. The importance of the rehabilitation robot lies in its ability to provide intensive physiotherapy for a long period time. The measured data of the robot allows the physiotherapist to accurately evaluate the patient’s performance. However, these devices are still part of an emerging area and present many challenges compared to the conventional robotic manipulators, such as the high nonlinearity, dimensional (high number of DOFs) and unknown dynamics (uncertainties). These limitations are provoked due to their complex mechanical structure designed for human use, the types of assistive motion, and the sensitivity of the interaction with a large diversity of human wearers. As a result, these conditions make the robot system vulnerable to dynamic uncertainties and external disturbances such as saturation, friction forces, backlash, and payload. Likewise, the interaction between human and the exoskeleton make the system subjected to external disturbances due to different physiological conditions of the subjects like the different weight of the upper limb for each subject. During a rehabilitation movement, the nonlinear uncertain dynamic model and external forces can turn into unknown function that can affect the performance of the exoskeleton robot. The main challenges addressed in this thesis are firstly to design a human inverse kinematics solution to perform a smooth movement similar to natural human movement (human-like motion). Secondly, to develop controllers characterized by a high-level of robustness and accuracy without any sensitivity to uncertain nonlinear dynamics and unexpected disturbances. This will give the control system more flexibility to handle the uncertainties and parameters’ variation in different modes of rehabilitation motion (passive and active)