2,435 research outputs found
Multiple configuration shell-core structured robotic manipulator with interchangeable mechatronic joints : a thesis presented in partial fulfilment of the requirements for the degree of Masters of Engineering in Mechatronics at Massey University, Turitea Campus, Palmerston North, New Zealand
With the increase of robotic technology utilised throughout industry, the need for skilled
labour in this area has increased also. As a result, education dealing with robotics has
grown at both the high-school and tertiary educational level. Despite the range of
pedagogical robots currently on the market, there seems to be a low variety of these
systems specifically related to the types of robotic manipulator arms popular for industrial
applications. Furthermore, a fixed-arm system is limited to only serve as an educational
supplement for that specific configuration and therefore cannot demonstrate more than
one of the numerous industrial-type robotic arms.
The Shell-Core structured robotic manipulator concept has been proposed to improve the
quality and variety of available pedagogical robotic arm systems on the market. This is
achieved by the reconfigurable nature of the concept, which incorporates shell and core
structural units to make the construction of at least 5 mainstream industrial arms
possible. The platform will be suitable, but not limited to use within the educational
robotics industry at high-school and higher educational levels and may appeal to
hobbyists.
Later dubbed SMILE (Smart Manipulator with Interchangeable Links and Effectors), the
system utilises core units to provide either rotational or linear actuation in a single plane.
A variety of shell units are then implemented as the body of the robotic arm, serving as
appropriate offsets to achieve the required configuration. A prototype consisting of a
limited number of ‘building blocks’ was developed for proof-of-concept, found capable of
achieving several of the proposed configurations.
The outcome of this research is encouraging, with a Massey patent search confirming the
unique features of the proposed concept. The prototype system is an economic, easy to
implement, plug and play, and multiple-configuration robotic manipulator, suitable for
various applications
Active Inference for Integrated State-Estimation, Control, and Learning
This work presents an approach for control, state-estimation and learning
model (hyper)parameters for robotic manipulators. It is based on the active
inference framework, prominent in computational neuroscience as a theory of the
brain, where behaviour arises from minimizing variational free-energy. The
robotic manipulator shows adaptive and robust behaviour compared to
state-of-the-art methods. Additionally, we show the exact relationship to
classic methods such as PID control. Finally, we show that by learning a
temporal parameter and model variances, our approach can deal with unmodelled
dynamics, damps oscillations, and is robust against disturbances and poor
initial parameters. The approach is validated on the `Franka Emika Panda' 7 DoF
manipulator.Comment: 7 pages, 6 figures, accepted for presentation at the International
Conference on Robotics and Automation (ICRA) 202
Performance Comparison of Several Control Algorithms for Tracking Control of Pantograph Mechanism
A sort of parallel manipulator known as a pantograph robot mechanism was created primarily for industrial requests that required high precision and satisfied speed. While tracking a chosen trajectory profile requires a powerful controller. Because it has four active robot links and one robot passive link in place of just two links like the open chain does, it can carry more loads than the open chain robot mechanism while maintaining accuracy and stability. The calculated model for a closed chain pantograph robot mechanism presented in this paper takes into account the boundary conditions. For the purpose of simulating the dynamics of the pantograph robot mechanism, an entire MATLAB Simulink has been created. The related Simscape model had been created to verify the pantograph mathematical model that had been provided. Five alternative tracking controllers were also created and improved using the Flower Pollination (FP) algorithm. The PID controller, which is used in many engineering applications, is the first control. An enriched Fractional Order PID (FOPID) controller is the second control. The third control considers an improved Nonlinear conventional PID (NLPID) controller, and the parameters for this controller were likewise determined using (FP) optimization using the useful objective function. Model Reference Adaptive Control (MRAC) with PID Compensator is the fourth control. The Fuzzy PD+I Control is the last and final controller. A comparison of the different control methods was completed. A rectangular trajectory was chosen as the end effector of the pantograph robot\u27s position reference because it displays performance during sharp edges and provides a more accurate study. The proposed controllers were used for this task to analyse the performance. The outcomes demonstrate that the Fuzzy PD+I control outperforms the PID, FOPID, NLPID, and MRAC with PID Compensator controllers in terms of performance. In the case of the Fuzzy PD+I control, the angles end effector has a lower rise time, a satisfied settling time, and low overshoot with good precision
A New Approach of the Online Tuning Gain Scheduling Nonlinear PID Controller Using Neural Network
This chapter presents the design, development and implementation of a novel proposed online-tuning Gain Scheduling Dynamic Neural PID (DNN-PID) Controller using neural network suitable for real-time manipulator control applications. The unique feature of the novel DNN-PID controller is that it has highly simple and dynamic self-organizing structure, fast online-tuning speed, good generalization and flexibility in online-updating. The proposed adaptive algorithm focuses on fast and efficiently optimizing Gain Scheduling and PID weighting parameters of Neural MLPNN model used in DNN-PID controller. This approach is employed to implement the DNN-PID controller with a view of controlling the joint angle position of the highly nonlinear pneumatic artificial muscle (PAM) manipulator in real-time through Real-Time Windows Target run in MATLAB SIMULINK® environment. The performance of this novel proposed controller was found to be outperforming in comparison with conventional PID controller. These results can be applied to control other highly nonlinear SISO and MIMO systems. Keywords: highly nonlinear PAM manipulator, proposed online tuning Gain Scheduling Dynamic Nonlinear PID controller (DNN-PID), real-time joint angle position control, fast online tuning back propagation (BP) algorithm, pneumatic artificial muscle (PAM) actuator
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