The document discusses modeling and control of a pneumatic actuator for use in a highly dynamic legged robot. It proposes using a combination of model-based trajectory optimization and system identification techniques to account for the actuator dynamics and nonlinear constraints. While detailed mathematical models of pneumatic actuators have been proposed, they have many parameters that require identification and lack identifiability. The document describes collecting input/output data from the physical pneumatic system and using system identification toolbox to estimate models and analyze the system response.

1. Submitted by:
ABHISHEK VERMA
B.Tech /Mechanical
6th Sem/3rd Year
Submitted to:-
Mr. Somesh Sir.
Assistant Professor
Mechanical Department

2. To realize motion planning for a robot system employing
actuator dynamics and nonlinear constraints, we propose a
combination of model-based trajectory optimization and
system identification techniques. Complex actuator
dynamics are usually neglected because servo motors can
act as nearly ideal torque sources in a rated operation. On
he other hand, the dynamics of a fluid actuator used in the
highly-dynamic legged robot cannot be ignored.
Furthermore, it is essential to consider the surrounding
phenomena associated with a real system for successful
performance

3. Detailed mathematical models of the pneumatic
actuator have been proposed in previous studies.
These analytical models use a combination of fluid
dynamics equations and the states of valves and
cylinder chambers. However, these models have a large
number of related parameters that require appropriate
identification but lack identifiability. Furthermore, a
commercial pressure control valve is equipped with a
built-in controller with a number of parameters which
are impractical to model.

4. pneumatic actuator

6. Hardware implementation
Software implementation

7. . HARDWARE IMPLEMENTATION
The pneumatic actuator that is used in the research composed of double-acting
actuator, 5/3 proportional directional control valve and mass payload as illustrated.
Pneumatic actuator is driven by Jun Air compressor which offers vibration level,
minimum noise, longer life time and higher pressure. National Instrument Data
Acquisition System, DAQ (Model 182556F-01REV2) is connected to the
sensors and actuators to the computer with the aim to build and exact control
system and set parameters for control algorithms to establish a custom user
interface.

8. The user is allowed to use frequency domain or time domain input
output data to build discrete time and continuous time transfer
functions, process models and state space models. Identification
toolbox provides maximum likelihood, prediction-error minimization
(PEM), subspace system identification, and other identification
approaches. User can implement the identified model to predict or
forecast the system response as well as for the simulation in Simulink.

9. MODEL ESTIMATION Data is imported to the System
Identification Toolbox for various data processing, model
estimation, and model analysis tasks. Input signal is
applied to the system in order to observe the differences
between the estimated results and the actual outcomes to
the system. Figure 3 illustrates the input and output signals
with the duration of 100 seconds for model identification.

11. [1]Ali, Samsul Bahari B Mohd Noor, S.M Bashi, M.H Marhaban, A
revieof Pneumatic Actuators (Modeling and Control), Australian
Journal of Basic and Applied Sciences, 3(2): 440-454,
[2] Amin Mohammadbagheri, Narges Zaeri and Mahdi Yaghoobi,
Comparison Performance Between PID and LQR Controllers for 4-leg
Voltage-Source Inverters, International Conference Circuit, System and
Simulation, 2011
[3] Igor L Krivts, German V Krejnin, Pneumatic Actuating Systems for
Automatic Equipment Structure and Design, Taylor & Francis Group,
2006.
[4] Jian-Bo He, Qing.Wang and Tong Lee, PI/ PID Controller Tuning
via LQR Approach, IEEE Conference on Decision & Control, 1998.
[5] Joachim Schroder, Kazuhiko Kawamura, Riidiger Dillman,
Dynamic Pneumatic Actuator Model for a Model-Based Torque
Controller, IEEE International Symposium on Computational
Intelligence in Robotics and Automation, 2003.