61,930 research outputs found
Barrier-Lyapunov-Function-Based Backstepping Adaptive Hybrid Force/Position Control for Manipulator with Force and Position Constraints
In this paper, we present a backstepping adaptive hybrid force/position control based on Barrier Lyapunov Function for a robotic manipulator to prevent constraint violation of applied force and position simultaneously. First, the task space is partitioned according to the constrained and unconstrained directions, and a new representation of dynamics is introduced. Next, force/position control is applied using the strict-feedback backstepping technique, in which a time-varying Barrier Lyapunov Function is employed to ensure that the force and position do not violate their constraints. Finally, to deal with uncertainty, disturbance and non-linearity of the system, an adaptive radial basis function neural network (RBFNN) is also implemented in the control algorithm. Stability proof of the proposed control method is presented, and simulation studies on a 2-link manipulator show the effectiveness as well as the performance of the proposed controller in preventing constraint violation
Design of Optimal Hybrid Position/Force Controller for a Robot Manipulator Using Neural Networks
The application of quadratic optimization and sliding-mode approach is considered for hybrid position and force control of a robot manipulator. The dynamic model of the manipulator is transformed into a state-space model to contain two sets of state variables, where one describes the constrained motion and the other describes the unconstrained motion. The optimal feedback control law is derived solving matrix differential Riccati equation, which is obtained using Hamilton Jacobi Bellman optimization. The optimal feedback control law is shown to be globally exponentially stable using Lyapunov function approach. The dynamic model uncertainties are compensated with a feedforward neural network. The neural network requires no preliminary offline training and is trained with online weight tuning algorithms that guarantee small errors and bounded control signals. The application of the derived control law is demonstrated through simulation with a 4-DOF robot manipulator to track an elliptical planar constrained surface while applying the desired force on the surface
Robust data assimilation in river flow and stage estimation based on multiple imputation particle filter
In this paper, new method is proposed for a more robust Data Assimilation (DA) design of the
river flow and stage estimation. By using the new sets of data that are derived from the incorporated Multi
Imputation Particle Filter (MIPF) in the DA structure, the proposed method is found to have overcome the
issue of missing observation data and contributed to a better estimation process. The convergence analysis
of the MIPF is discussed and shows that the number of the particles and imputation influence the ability of
this method to perform estimation. The simulation results of the MIPF demonstrated the superiority of the
proposed approach when being compared to the Extended Kalman Filter (EKF) and Particle Filter (PF)
Hybrid LQG-Neural Controller for Inverted Pendulum System
The paper presents a hybrid system controller, incorporating a neural and an
LQG controller. The neural controller has been optimized by genetic algorithms
directly on the inverted pendulum system. The failure free optimization process
stipulated a relatively small region of the asymptotic stability of the neural
controller, which is concentrated around the regulation point. The presented
hybrid controller combines benefits of a genetically optimized neural
controller and an LQG controller in a single system controller. High quality of
the regulation process is achieved through utilization of the neural
controller, while stability of the system during transient processes and a wide
range of operation are assured through application of the LQG controller. The
hybrid controller has been validated by applying it to a simulation model of an
inherently unstable system of inverted pendulum
Combining Physical Simulators and Object-Based Networks for Control
Physics engines play an important role in robot planning and control;
however, many real-world control problems involve complex contact dynamics that
cannot be characterized analytically. Most physics engines therefore employ .
approximations that lead to a loss in precision. In this paper, we propose a
hybrid dynamics model, simulator-augmented interaction networks (SAIN),
combining a physics engine with an object-based neural network for dynamics
modeling. Compared with existing models that are purely analytical or purely
data-driven, our hybrid model captures the dynamics of interacting objects in a
more accurate and data-efficient manner.Experiments both in simulation and on a
real robot suggest that it also leads to better performance when used in
complex control tasks. Finally, we show that our model generalizes to novel
environments with varying object shapes and materials.Comment: ICRA 2019; Project page: http://sain.csail.mit.ed
A deep reinforcement learning based homeostatic system for unmanned position control
Deep Reinforcement Learning (DRL) has been proven to be capable of designing an optimal control theory by minimising the error in dynamic systems. However, in many of the real-world operations, the exact behaviour of the environment is unknown. In such environments, random changes cause the system to reach different states for the same action. Hence, application of DRL for unpredictable environments is difficult as the states of the world cannot be known for non-stationary transition and reward functions. In this paper, a mechanism to encapsulate the randomness of the environment is suggested using a novel bio-inspired homeostatic approach based on a hybrid of Receptor Density Algorithm (an artificial immune system based anomaly detection application) and a Plastic Spiking Neuronal model. DRL is then introduced to run in conjunction with the above hybrid model. The system is tested on a vehicle to autonomously re-position in an unpredictable environment. Our results show that the DRL based process control raised the accuracy of the hybrid model by 32%.N/
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