Control of temperature conditioning systems with distributed actuator dynamics

2016 ◽  
Author(s):  
Stefan Schaut ◽  
Simon Alt ◽  
Oliver Sawodny
Keyword(s):  
Actuator Dynamics ◽  
Distributed Actuator

scholarly journals Sensing, Actuation, and Control of the SmartX Prototype Morphing Wing in the Wind Tunnel

Actuators ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 107
Author(s):  
Nakash Nazeer ◽  
Xuerui Wang ◽  
Roger M. Groves
Keyword(s):  
Wind Tunnel ◽  
Optical Fibers ◽  
Fiber Optic ◽  
Experimental Testing ◽  
Single Mode ◽  
Fiber Optic Sensor ◽  
Sensor Data ◽  
Random Errors ◽  
Morphing Wing ◽  
Actuator Dynamics

This paper presents a study on trailing edge deflection estimation for the SmartX camber morphing wing demonstrator. This demonstrator integrates the technologies of smart sensing, smart actuation and smart controls using a six module distributed morphing concept. The morphing sequence is brought about by two actuators present at both ends of each of the morphing modules. The deflection estimation is carried out by interrogating optical fibers that are bonded on to the wing’s inner surface. A novel application is demonstrated using this method that utilizes the least amount of sensors for load monitoring purposes. The fiber optic sensor data is used to measure the deflections of the modules in the wind tunnel using a multi-modal fiber optic sensing approach and is compared to the deflections estimated by the actuators. Each module is probed by single-mode optical fibers that contain just four grating sensors and consider both bending and torsional deformations. The fiber optic method in this work combines the principles of hybrid interferometry and FBG spectral sensing. The analysis involves an initial calibration procedure outside the wind tunnel followed by experimental testing in the wind tunnel. This method is shown to experimentally achieve an accuracy of 2.8 mm deflection with an error of 9%. The error sources, including actuator dynamics, random errors, and nonlinear mechanical backlash, are identified and discussed.

scholarly journals Task-space regulation of rigid-link electrically-driven robots with uncertain kinematics using neural networks

2021 ◽  
Vol 54 (1-2) ◽  
pp. 102-115
Author(s):  
Wenhui Si ◽  
Lingyan Zhao ◽  
Jianping Wei ◽  
Zhiguang Guan
Keyword(s):  
Neural Networks ◽  
Asymptotic Stability ◽  
Control Method ◽  
Joint Space ◽  
Robot Kinematics ◽  
Rbf Neural Networks ◽  
Task Space ◽  
Actuator Dynamics ◽  
Rigid Link ◽  
On Line

Extensive research efforts have been made to address the motion control of rigid-link electrically-driven (RLED) robots in literature. However, most existing results were designed in joint space and need to be converted to task space as more and more control tasks are defined in their operational space. In this work, the direct task-space regulation of RLED robots with uncertain kinematics is studied by using neural networks (NN) technique. Radial basis function (RBF) neural networks are used to estimate complicated and calibration heavy robot kinematics and dynamics. The NN weights are updated on-line through two adaptation laws without the necessity of off-line training. Compared with most existing NN-based robot control results, the novelty of the proposed method lies in that asymptotic stability of the overall system can be achieved instead of just uniformly ultimately bounded (UUB) stability. Moreover, the proposed control method can tolerate not only the actuator dynamics uncertainty but also the uncertainty in robot kinematics by adopting an adaptive Jacobian matrix. The asymptotic stability of the overall system is proven rigorously through Lyapunov analysis. Numerical studies have been carried out to verify efficiency of the proposed method.

Modified sliding mode control of a seismic active mass damper system considering model uncertainties and input time delay

2016 ◽  
Vol 24 (6) ◽  
pp. 1051-1064 ◽  
Author(s):  
Mehdi Soleymani ◽  
Amir Hossein Abolmasoumi ◽  
Hasanali Bahrami ◽  
Arash Khalatbari-S ◽  
Elham Khoshbin ◽  
...  
Keyword(s):  
Structural Control ◽  
Sliding Mode ◽  
Test Structure ◽  
Dynamic State ◽  
Shake Table ◽  
Model Uncertainties ◽  
Actuator Dynamics ◽  
Active Structural Control ◽  
Mass Damper ◽  
Active Mass Damper

Model uncertainties and actuator delays are two factors that degrade the performance of active structural control systems. A new robust control system is proposed for control of an active tuned mass damper (AMD) in a high-rise building. The controller comprises a two-loop sliding model controller in conjunction with a dynamic state predictor. The sliding model controller is responsible for model uncertainties and the state predictor compensates for the time delays due to actuator dynamics and process delay. A reduced model that is validated against experimental data was constructed and equipped with an electro-mechanical AMD system mounted on the top storey. The proposed controller was implemented in the test structure and its performance under seismic disturbances was simulated using a seismic shake table. Moreover, robustness of the proposed controller was examined via variation of the test structure parameters. The shake table test results reveal the effectiveness of the proposed controller at tackling the simulated disturbances in the presence of model uncertainties and input delay.

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