Abstract
A new proportional integral derivative (PID) control method is proposed for the 3D laser scanning system converted from 2D Lidar with a pitching motion device. It combines the advantages of a fuzzy algorithm, a radial basis function (RBF) neural network and a predictive algorithm to control the pitching motion of 2D Lidar quickly and accurately. The proposed method adopts the RBF neural network and feedback compensation to eliminate the unknown nonlinear part in the Lidar pitching motion, adaptively adjusting the PID parameter by a fuzzy algorithm. Then, the predictive control algorithm is adopted to optimize the overall controller output in real time. Finally, the simulation results show that the step response time of the Lidar pitching motion system using the control method is reduced from 15.298 s to 1.957 s with a steady-state error of 0.07°. Meanwhile, the system still has favorable response performance for the sinusoidal and step inputs under model mismatch and large disturbance. Therefore, the control method proposed above can improve the system performance and control the pitching motion of the 2D Lidar effectively.
Highlights
With the development of laser technology, 2D Lidar has been widely used in various fields such as map navigation [1,2], simultaneous localization and mapping (SLAM) [3,4] and robots [5,6]
This paper proposes a predictive radial basis function (RBF) compensation fuzzy Proportional integral derivative (PID) controller (PRFPID) to control the pitching motion of 2D Lidar quickly and accurately
Controller is proposed, which combines the advantages of a fuzzy predictive RBF compensation fuzzy PID controller is proposed, which combines the advantages of a algorithm, an RBFan neural and predictive control. control
Summary
With the development of laser technology, 2D Lidar has been widely used in various fields such as map navigation [1,2], simultaneous localization and mapping (SLAM) [3,4] and robots [5,6]. Lidar pitching motion is a complex system with unknown nonlinearity For this kind of model, how to adjust PID parameters properly is not an easy task. Savran proposed a fuzzy adaptive adjustment method for PID parameters in a nonlinear process [32]. In [35], Jose R. used a feedforward neural network to fit the unknown nonlinear autoregressive moving average (NARMA) model; combined with predictive control theory, the control output signal is obtained by using the gradient descent method to minimize the error. This paper proposes a predictive RBF compensation fuzzy PID controller (PRFPID) to control the pitching motion of 2D Lidar quickly and accurately.
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