Model-free adaptive control scheme for EGR/VNT control of a diesel engine using the simultaneous perturbation stochastic approximation

2016 ◽  
Vol 39 (1) ◽  
pp. 114-128 ◽  
Author(s):  
Shinichi Ishizuka ◽  
Itsuro Kajiwara ◽  
Junichi Sato ◽  
Yoshifumi Hanamura ◽  
Satoshi Hanawa
Keyword(s):  
Adaptive Control ◽  
Diesel Engine ◽  
Stochastic Approximation ◽  
Simultaneous Perturbation Stochastic Approximation ◽  
Model Free ◽  
Control Scheme ◽  
Adaptive Control Scheme

scholarly journals Model-Free Adaptive Sliding Mode Robust Control with Neural Network Estimator for the Multi-Degree-of-Freedom Robotic Exoskeleton

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Xiangjian Chen ◽  
Di Li ◽  
Pingxin Wang ◽  
Xibei Yang ◽  
Hongmei Li
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Robust Control ◽  
Sliding Mode ◽  
Robotic Exoskeleton ◽  
Adaptive Sliding Mode ◽  
Model Free ◽  
Control Scheme ◽  
System Uncertainties ◽  
Adaptive Control Scheme

A new model-free adaptive robust control method has been proposed for the robotic exoskeleton, and the proposed control scheme depends only on the input and output data, which is different from model-based control algorithms that require exact dynamic model knowledge of the robotic exoskeleton. The dependence of the control algorithm on the prior knowledge of the robotic exoskeleton dynamics model is reduced, and the influence of the system uncertainties are compensated by using the model-free adaptive sliding mode controller based on data-driven methodology and neural network estimator, which improves the robustness of the system. Finally, real-time experimental results show that the control scheme proposed in this paper achieves better control performances with good robustness with respect to system uncertainties and external wind disturbances compared with the model-free adaptive control scheme and model-free sliding mode adaptive control scheme.

Adaptive Control Scheme for Coupled Tank Process

2018 ◽  
pp. 89-95
Author(s):  
Vinodhini M.
Keyword(s):  
Adaptive Control ◽  
Nonlinear Behaviour ◽  
Simulation Studies ◽  
Input Output ◽  
Control Scheme ◽  
Direct Model ◽  
Multivariable Process ◽  
Model Reference Adaptive ◽  
Siso System ◽  
Adaptive Control Scheme

The objective of this paper is to develop a Direct Model Reference Adaptive Control (DMRAC) algorithm for a MIMO process by extending the MIT rule adopted for a SISO system. The controller thus developed is implemented on Laboratory interacting coupled tank process through simulation. This can be regarded as the relevant process control in petrol and chemical industries. These industries involve controlling the liquid level and the flow rate in the presence of nonlinearity and disturbance which justifies the use of adaptive techniques such as DMRAC control scheme. For this purpose, mathematical models are obtained for each of the input-output combinations using white box approach and the respective controllers are developed. A detailed analysis on the performance of the chosen process with these controllers is carried out. Simulation studies reveal the effectiveness of proposed controller for multivariable process that exhibits nonlinear behaviour.

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