scholarly journals Dynamic Surface Control and Its Application to Lateral Vehicle Control

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Bongsob Song ◽  
J. Karl Hedrick ◽  
Yeonsik Kang
Keyword(s):  
Nonlinear Systems ◽  
Dynamic Surface Control ◽  
Vehicle Control ◽  
Linear Matrix ◽  
Automated Driving ◽  
Dynamic Surface ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Surface Control ◽  
Highly Nonlinear

This paper extends the design and analysis methodology of dynamic surface control (DSC) in Song and Hedrick, 2011, for a more general class of nonlinear systems. When rotational mechanical systems such as lateral vehicle control and robot control are considered for applications, sinusoidal functions are easily included in the equation of motions. If such a sinusoidal function is used as a forcing term for DSC, the stability analysis faces the difficulty due to highly nonlinear functions resulting from the low-pass filter dynamics. With modification of input variables to the filter dynamics, the burden of mathematical analysis can be reduced and stability conditions in linear matrix inequality form to guarantee the quadratic stability via DSC are derived for the given class of nonlinear systems. Finally, the proposed design and analysis approach are applied to lateral vehicle control for forward automated driving and backward parallel parking at a low speed as well as an illustrative example.

Modeling and Dynamic Surface Control of Uncertain Strict-Feedback Nonlinear Systems Using Adaptive Fuzzy Wavelet Network

2018 ◽  
pp. 112-133
Author(s):  
Maryam Shahriari-Kahkeshi
Keyword(s):  
Nonlinear Systems ◽  
Dynamic Surface Control ◽  
Wavelet Network ◽  
Dynamic Surface ◽  
Adaptive Fuzzy ◽  
Surface Control ◽  
Control Scheme ◽  
Adaptive Laws ◽  
Dsc Method ◽  
Strict Feedback

This chapter proposes a new modeling and control scheme for uncertain strict-feedback nonlinear systems based on adaptive fuzzy wavelet network (FWN) and dynamic surface control (DSC) approach. It designs adaptive FWN as a nonlinear-in-parameter approximator to approximate the uncertain dynamics of the system. Then, the proposed control scheme is developed by incorporating the DSC method to the adaptive FWN-based model. Stability analysis of the proposed scheme is provided and adaptive laws are designed to learn all linear and nonlinear parameters of the network. It is proven that all the signals of the closed-loop system are uniformly ultimately bounded and the tracking error can be made arbitrary small. The proposed scheme does not require any prior knowledge about dynamics of the system and offline learning. Furthermore, it eliminates the “explosion of complexity” problems and develops accurate model of the system and simple controller. Simulation results on the numerical example and permanent magnet synchronous motor are provided to show the effectiveness of the proposed scheme.

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