275 research outputs found
Adaptive fuzzy control for coordinated multiple robots with constraint using impedance learning
In this paper, we investigate fuzzy neural network (FNN) control using impedance learning for coordinated multiple constrained robots carrying a common object in the presence of the unknown robotic dynamics and the unknown environment with which the robot comes into contact. First, an FNN learning algorithm is developed to identify the unknown plant model. Second, impedance learning is introduced to regulate the control input in order to improve the environment-robot interaction, and the robot can track the desired trajectory generated by impedance learning. Third, in light of the condition requiring the robot to move in a finite space or to move at a limited velocity in a finite space, the algorithm based on the position constraint and the velocity constraint are proposed, respectively. To guarantee the position constraint and the velocity constraint, an integral barrier Lyapunov function is introduced to avoid the violation of the constraint. According to Lyapunov's stability theory, it can be proved that the tracking errors are uniformly bounded ultimately. At last, some simulation examples are carried out to verify the effectiveness of the designed control
AI based Robot Safe Learning and Control
Introduction This open access book mainly focuses on the safe control of robot manipulators. The control schemes are mainly developed based on dynamic neural network, which is an important theoretical branch of deep reinforcement learning. In order to enhance the safety performance of robot systems, the control strategies include adaptive tracking control for robots with model uncertainties, compliance control in uncertain environments, obstacle avoidance in dynamic workspace. The idea for this book on solving safe control of robot arms was conceived during the industrial applications and the research discussion in the laboratory. Most of the materials in this book are derived from the authors’ papers published in journals, such as IEEE Transactions on Industrial Electronics, neurocomputing, etc. This book can be used as a reference book for researcher and designer of the robotic systems and AI based controllers, and can also be used as a reference book for senior undergraduate and graduate students in colleges and universities
Disturbance observer-based neural network control of cooperative multiple manipulators with input saturation
In this paper, the complex problems of internal forces and position control are studied simultaneously and a disturbance observer-based radial basis function neural network (RBFNN) control scheme is proposed to: 1) estimate the unknown parameters accurately; 2) approximate the disturbance experienced by the system due to input saturation; and 3) simultaneously improve the robustness of the system. More specifically, the proposed scheme utilizes disturbance observers, neural network (NN) collaborative control with an adaptive law, and full state feedback. Utilizing Lyapunov stability principles, it is shown that semiglobally uniformly bounded stability is guaranteed for all controlled signals of the closed-loop system. The effectiveness of the proposed controller as predicted by the theoretical analysis is verified by comparative experimental studies
Bayesian estimation of human impedance and motion intention for human-robot collaboration
This article proposes a Bayesian method to acquire the estimation of human impedance and motion intention in a human-robot collaborative task. Combining with the prior knowledge of human stiffness, estimated stiffness obeying Gaussian distribution is obtained by Bayesian estimation, and human motion intention can be also estimated. An adaptive impedance control strategy is employed to track a target impedance model and neural networks are used to compensate for uncertainties in robotic dynamics. Comparative simulation results are carried out to verify the effectiveness of estimation method and emphasize the advantages of the proposed control strategy. The experiment, performed on Baxter robot platform, illustrates a good system performance
Control of constrained robot systems
Ph.DDOCTOR OF PHILOSOPH
Biomimetic Manipulator Control Design for Bimanual Tasks in the Natural Environment
As robots become more prolific in the human environment, it is important that safe operational
procedures are introduced at the same time; typical robot control methods are
often very stiff to maintain good positional tracking, but this makes contact (purposeful
or accidental) with the robot dangerous. In addition, if robots are to work cooperatively
with humans, natural interaction between agents will make tasks easier to perform with
less effort and learning time. Stability of the robot is particularly important in this
situation, especially as outside forces are likely to affect the manipulator when in a close
working environment; for example, a user leaning on the arm, or task-related disturbance
at the end-effector.
Recent research has discovered the mechanisms of how humans adapt the applied force
and impedance during tasks. Studies have been performed to apply this adaptation to
robots, with promising results showing an improvement in tracking and effort reduction
over other adaptive methods. The basic algorithm is straightforward to implement,
and allows the robot to be compliant most of the time and only stiff when required by
the task. This allows the robot to work in an environment close to humans, but also
suggests that it could create a natural work interaction with a human. In addition, no
force sensor is needed, which means the algorithm can be implemented on almost any
robot.
This work develops a stable control method for bimanual robot tasks, which could also
be applied to robot-human interactive tasks. A dynamic model of the Baxter robot is
created and verified, which is then used for controller simulations. The biomimetic control
algorithm forms the basis of the controller, which is developed into a hybrid control
system to improve both task-space and joint-space control when the manipulator is disturbed
in the natural environment. Fuzzy systems are implemented to remove the need
for repetitive and time consuming parameter tuning, and also allows the controller to
actively improve performance during the task. Experimental simulations are performed,
and demonstrate how the hybrid task/joint-space controller performs better than either
of the component parts under the same conditions. The fuzzy tuning method is then applied
to the hybrid controller, which is shown to slightly improve performance as well as
automating the gain tuning process. In summary, a novel biomimetic hybrid controller
is presented, with a fuzzy mechanism to avoid the gain tuning process, finalised with a
demonstration of task-suitability in a bimanual-type situation.EPSR
Performance of modified jatropha oil in combination with hexagonal boron nitride particles as a bio-based lubricant for green machining
This study evaluates the machining performance of newly developed modified jatropha oils (MJO1, MJO3 and MJO5), both with and without hexagonal boron nitride (hBN) particles (ranging between 0.05 and 0.5 wt%) during turning of AISI 1045 using minimum quantity lubrication (MQL). The experimental results indicated that, viscosity improved with the increase in MJOs molar ratio and hBN concentration. Excellent tribological behaviours is found to correlated with a better machining performance were achieved by MJO5a with 0.05 wt%. The MJO5a sample showed the lowest values of cutting force, cutting temperature and surface roughness, with a prolonged tool life and less tool wear, qualifying itself to be a potential alternative to the synthetic ester, with regard to the environmental concern
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