7,567 research outputs found
Identification of Nonlinear Systems From the Knowledge Around Different Operating Conditions: A Feed-Forward Multi-Layer ANN Based Approach
The paper investigates nonlinear system identification using system output
data at various linearized operating points. A feed-forward multi-layer
Artificial Neural Network (ANN) based approach is used for this purpose and
tested for two target applications i.e. nuclear reactor power level monitoring
and an AC servo position control system. Various configurations of ANN using
different activation functions, number of hidden layers and neurons in each
layer are trained and tested to find out the best configuration. The training
is carried out multiple times to check for consistency and the mean and
standard deviation of the root mean square errors (RMSE) are reported for each
configuration.Comment: "6 pages, 9 figures; The Second IEEE International Conference on
Parallel, Distributed and Grid Computing (PDGC-2012), December 2012, Solan
A survey of fuzzy control for stabilized platforms
This paper focusses on the application of fuzzy control techniques (fuzzy
type-1 and type-2) and their hybrid forms (Hybrid adaptive fuzzy controller and
fuzzy-PID controller) in the area of stabilized platforms. It represents an
attempt to cover the basic principles and concepts of fuzzy control in
stabilization and position control, with an outline of a number of recent
applications used in advanced control of stabilized platform. Overall, in this
survey we will make some comparisons with the classical control techniques such
us PID control to demonstrate the advantages and disadvantages of the
application of fuzzy control techniques
Sim2Real View Invariant Visual Servoing by Recurrent Control
Humans are remarkably proficient at controlling their limbs and tools from a
wide range of viewpoints and angles, even in the presence of optical
distortions. In robotics, this ability is referred to as visual servoing:
moving a tool or end-point to a desired location using primarily visual
feedback. In this paper, we study how viewpoint-invariant visual servoing
skills can be learned automatically in a robotic manipulation scenario. To this
end, we train a deep recurrent controller that can automatically determine
which actions move the end-point of a robotic arm to a desired object. The
problem that must be solved by this controller is fundamentally ambiguous:
under severe variation in viewpoint, it may be impossible to determine the
actions in a single feedforward operation. Instead, our visual servoing system
must use its memory of past movements to understand how the actions affect the
robot motion from the current viewpoint, correcting mistakes and gradually
moving closer to the target. This ability is in stark contrast to most visual
servoing methods, which either assume known dynamics or require a calibration
phase. We show how we can learn this recurrent controller using simulated data
and a reinforcement learning objective. We then describe how the resulting
model can be transferred to a real-world robot by disentangling perception from
control and only adapting the visual layers. The adapted model can servo to
previously unseen objects from novel viewpoints on a real-world Kuka IIWA
robotic arm. For supplementary videos, see:
https://fsadeghi.github.io/Sim2RealViewInvariantServoComment: Supplementary video:
https://fsadeghi.github.io/Sim2RealViewInvariantServ
Design of Predictive Controllers by Dynamic Programming and Neural Networks
This paper proposes a method for the design of predictive controllers for nonlinear systems. The method consists of two phases, a solution phase and a learning phase. In the solution phase, dynamic programming is applied to obtain a closed-loop control law. In the learning phase, neural networks are used to simulate the control law. This phase overcomes the curse of dimensionality problem that has often hindered the implementation of control laws generated by dynamic programming. Experimental results demonstrate the effectiveness of the metho
Model based control strategies for a class of nonlinear mechanical sub-systems
This paper presents a comparison between various control strategies for a class of mechanical actuators common in heavy-duty industry. Typical actuator components are hydraulic or pneumatic elements with static non-linearities, which are commonly referred to as Hammerstein systems. Such static non-linearities may vary in time as a function of the load and hence classical inverse-model based control strategies may deliver sub-optimal performance. This paper investigates the ability of advanced model based control strategies to satisfy a tolerance interval for position error values, overshoot and settling time specifications. Due to the presence of static non-linearity requiring changing direction of movement, control effort is also evaluated in terms of zero crossing frequency (up-down or left-right movement). Simulation and experimental data from a lab setup suggest that sliding mode control is able to improve global performance parameters
Sliding-mode neuro-controller for uncertain systems
In this paper, a method that allows for the merger of the good features of sliding-mode control and neural network (NN) design is presented. Design is performed by applying an NN to minimize the cost function that is selected to depend on the distance from the sliding-mode manifold, thus providing that the NN controller enforces sliding-mode motion in a closed-loop system. It has been proven that the selected cost function has no local minima in controller parameter space, so under certain conditions, selection of the NN weights guarantees that the global minimum is reached, and then the sliding-mode conditions are satisfied; thus, closed-loop motion is robust against parameter changes and disturbances. For controller design, the system states and the nominal value of the control input matrix are used. The design for both multiple-input-multiple-output and single-input-single-output systems is discussed. Due to the structure of the (M)ADALINE network used in control calculation, the proposed algorithm can also be interpreted as a sliding-mode-based control parameter adaptation scheme. The controller performance is verified by experimental results
Evolution of central pattern generators for the control of a five-link bipedal walking mechanism
Central pattern generators (CPGs), with a basis is neurophysiological
studies, are a type of neural network for the generation of rhythmic motion.
While CPGs are being increasingly used in robot control, most applications are
hand-tuned for a specific task and it is acknowledged in the field that generic
methods and design principles for creating individual networks for a given task
are lacking. This study presents an approach where the connectivity and
oscillatory parameters of a CPG network are determined by an evolutionary
algorithm with fitness evaluations in a realistic simulation with accurate
physics. We apply this technique to a five-link planar walking mechanism to
demonstrate its feasibility and performance. In addition, to see whether
results from simulation can be acceptably transferred to real robot hardware,
the best evolved CPG network is also tested on a real mechanism. Our results
also confirm that the biologically inspired CPG model is well suited for legged
locomotion, since a diverse manifestation of networks have been observed to
succeed in fitness simulations during evolution.Comment: 11 pages, 9 figures; substantial revision of content, organization,
and quantitative result
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