# Target Points Tracking Control for Autonomous Cleaning Vehicle Based on the LSTM Network
## 1 Introduction
Although the above methods can achieve good tracking results under specific conditions, some pre-defined path points used are too idealized. Since most of the path points currently used for tracking are obtained by smoothing and filtering from the original path points, which are collected by the map building. The original path usually has some noise points. It is still unknown whether the above methods can maintain a good tracking effect on the path with bounded disturbance, so the robustness of the target point tracking control algorithm remains to be proven. In addition, each algorithm has its own advantages and application occasions. If we can directly extract their good effect data without the tedious analysis process and construct a new control algorithm based on these existing data, it would be a new breakthrough in the field of the automatic control. According to the universal approximation theorem [18], artificial neural network (ANN) has the potent ability to approximate the nonlinear system and it is considered as an effective approach to deal with complex problems. Related research in neural networks has shown exemplary results in the field of tracking control of wheeled mobile robot [19] and quadrotors [20], which illustrate the power of learning features directly from the data in the area of deep-learning. A unified robust neural network based control scheme is presented for trajectory tracking of a wheeled mobile robot with unknown parameters in reference [19], the proposed controller has a good performance on the steady-state errors and control inputs in both tracking and stabilization problems. Reference [20] proposes a DNN-based reference learning method that can learn the flight data and improve tracking performance in the quadrotor system, because of its superior capability of approximating abstract. Reference [16] proposes a novel path points tracking method based on learning, which combines the Stanley method with a Neural Dynamic Programming (NDP) algorithm perfectly. Although this method demonstrates state-of-the-art performance, the slight vibration is existed during the path tracking process according to the comprehensive simulation experiment. Due to their success in many applications, this paper chooses artificial neural network in the form of a Long Short-Term Memory (LSTM) network as the basis to study the target points tracking control algorithm, for the reason that path points can be treated as time sequence data in tracking. And to the best of our knowledge, the LSTM framework has not been designed for target points tracking control algorithm to predict the vehicles' speed and yaw rate. In this paper, we consider the problem of autonomous mobile cleaning robot in target points tracking, where the running speed is relatively lower than other scenarios, but the accuracy of the tracking process is higher because it involves cleaning efficiency. Inspired by the pure pursuit algorithm, we use a key parameter of this algorithm, foresight distance, which simulates the vision of the driver in driving and has the bionic characteristics. The foresight distance can be obtained by the fuzzy control, its inputs are the current velocity and yaw rate of the vehicle, and its output is an appropriate target foresight distance. After setting a suitable foresight distance, a simple geometric method is intended for obtaining the target point which is relative to the current vehicle position. In order to arrive at this target point, two-layer LSTM network controller is proposed to adjust the autonomous vehicle's speed and yaw rate simultaneously and intelligently to reduce the lateral error of the path points tracking. The LSTM model has been trained in proper times based on abundant and good sample data, then the kinematic model of automatic vehicle are established and simulation experiments are carried on two typical paths. The simulation results are compared with the results of three effective control algorithms, and the advantages of the proposed algorithm are analyzed.