**Wen Yu's Laboratory**

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**1. 3D crane
system**. It is a real-time MIMO nonlinear control system. It has 3 control
DC motors and 5 position measuring wheel encoders (3 positions and 2 angles).
The encoders measure movements with the high resolution equal to 4096 pulses
per rotation (ppr). The hardware power interface amplifies the control signal
which goes from the PC to the DC motors, and converts the encoders pulse signals
to digital 16-bit form to be read by the computer. The control computer communicates
with the hardware interface by a
RT-DAC4 PCI board
(incremental encoders inputs and PWM outputs). The computer is AMD 450MHz. .The
operation envirment is: Windows 98, Maltab 5.3, Simulink 4.0, Real-Time Workshop
5.0, Real-time Windows Target 2.0 and Watcom 11.0.

**2. Ball and beam system**.
It is a real-time SISO nonlinear control system. In this experiment, a ball
is free to roll along the track of a beam. Transducers on the ball and beam
continuously detect the ball position and the beam angle of the track. The control
problem involves moving the ball from one position on the track to another by
controlling the track angle. It has 1 output and 2 inputs. A/D and D/A are realized
by a
RT-DAC4 PCI board.
The computer is Pentium III 500MHz. The operation envirment is: Windows XP,
Maltab 6.5, Simulink 5.0, Real-Time Workshop 5.0, Real-Time Windows Target 2.2
and Visual C++ 6.0.

**3. Synchronization of
two ball and beam systems**. Ball and beam system is one of the most popular
and important laboratory models for teaching control system. There are two problems
for ball and beam synchronized control: 1) many laboratories use simple controllers
such as PD control, and theory analysis is based on linear models, 2) nonlinear
controllers for ball and beam system have good theory results, but they are
seldom used in real applications. We first use PD control with nonlinear exact
compensation for the cross-coupling synchronization. Then a neural network is
applied to approximate the nonlinear compensator. The synchronization control
can be in parallel and serial forms

**4. 3-D vision system**.
In general, recognition of 3D object requires two or more appropriately defined
2D images. In on-line application, these methods are often subject to one or
more limitations. There is a difficulty in finding the correspondence between
one image and the others. A trade-off between efficiency and accuracy is often
necessary, so that sufficient accuracy can be achieved while avoiding large
computational overhead. We show that having an well-known initial conditions
of a scene it is possible to determine stereo correspondence from two images
using a new geometric model, these algorithms process the images quickly and
efficiently.

**5. Magnetic Levitation
System**. Magnetic levitation has been successfully implemented for many applications.
Recently, much effort has been directed toward the area of the disturbance suppression
within the magnetic levitation systems. But the control results are not satisfied.
Because they need disturbance model, and it is unknown. The main objects of
this project are 1) modeling of magnetic levitation system, and 2) neural control
for magnetic levitation system.

**6. Active Structural
Control with Stable Fuzzy PID Techniques**. Active vibration control of building
structure can reduce the vibration by 60%-80%. It is more effective than the
widely implemented passive anti-seismic systems. Recently, some practical applications
of active vibration control of buildings are reported. Also many advanced control
and measurement techniques can be applied to active structural control, and
impove the active vibration control. It gives detail discussions on how to use
intelligent techniques to measure the displacements of the building, and to
control the active devices. We first propose use offset cancellation and high-pass
filtering techniques to solve common problems of the building displacement mesaurement
via accelerometers. Then we analyze the most popular control algorithms in industrial,
PD/PID controllers. After we combine industrial PID control with fuzzy compensation.
The stability is proven with standard weights training algorithms. These conditions
give explicit selection methods for the gains of the PD/PID control. Finally,
fuzzy logic and sliding mode control are applied to the structural vibration
control. Experimental studies on a two-story building prototype with the controllers
are addressed.

**7. Bidirectional Modeling
and Structural Control for earthquake. **It is on building structure modeling
and control under bidirectional seismic waves. It focuses on different types
of bidirectional control devices, control strategies, and bidirectional sensors
used in structural control systems. It uses various issues like system identification
techniques, the time-delay in the system, estimation of velocity and position
from acceleration signals, and optimal placement of the sensors and control
devices. The importance of control devices and its applications to minimize
bidirectional vibrations is also needed

.

**8. Modular Design and
Control of an Upper Limb Exoskeleton. **We use modular design method to construct
an upper limb exoskeleton. This new design method is more simple and easy for
exoskeletons than the other techniques, and it is facility to be extended into
more joints robots. We also propose a novel admittance control, which works
in task space. The admittance control has PID form, and does not need the inverse
kinematic and the dynamic model of the exoskeleton. The experimental results
show that both the design and the controller work well for the upper limb exoskeleton.

** 9. Impedance and Admittance
Control** Human-robotic systems include interaction between human operators
and robots. They should be designed with careful consideration for the dynamic
property and control ability of a human operator. This proposal performs manual
tracking control tests on a human-robotic system using an impedance and admittance
controlled robot, and investigates control characteristics of a human operator
according to the robot impedance properties.

**10. Set Membership Method
for Simultaneous Localization and Mapping (SLAM)** The extended Kalman filter
(EKF) SLAM requires the uncertainty to be Gaussian noise. This assumption can
be relaxed to bounded noise by the set membership SLAM. However, the published
set membership SLAMs are not suitable for large-scale and online problems. In
this project, we use ellipsoid algorithm for solving SLAM problem. The proposed
ellipsoid SLAM has advantages over EKF SLAM and the other set membership SLAMs,
in noise condition, online realization, and large-scale problem. By bounded
ellipsoid technique, we analyze the convergence and stability of the ellipsoid
SLAM. Simulation and experimental results show that the proposed ellipsoid SLAM
is effective for online and large-scale problems such as Victoria Park dataset.

**11. Tacking Control of
Human-Robotic Systems (Upper Limb Exoskeleton )**

**12. Bilateral symmetric
rehabilitation with haptic robots. **Human brain has self-organization capability
after paralysis caused by stork. This needs neuro-rehabilitation for stroke-patients.
Recently, robotic technology is successfully applied in the rehabilitation.
Most of robot rehabilitation devices are in 2-dimension, and do not have feedback
from the environment. The rehabilitation effectiveness is not so good. In this
project, we design a 3-demension rehabilitation system, which has force feedback.
The patients have one more training dimension and one more sense (haptic feeling).
The neuro recovery time is less than the other robot rehabilitation methods.

**13. Teleoperation**

*This
lab was founded by CONACyT and CINVESTAV-IPN**.
*