Model predictive control (MPC), with prediction and control horizons under multivariable constraints, is an advanced form of model-based control and is commonly used to implement the path following of autonomous vehicles. Conventionally, MPC requires a kinematic or dynamic model of the vehicle to optimize the controller. When a nonlinear kinematic model is utilized, the model should be linearized prior to use by the MPC. However, the computational cost of model linearization is rather high, and hence implementing the MPC in real-time is extremely difficult. Furthermore, estimating the parameters of a classic dynamic model is difficult. Accordingly, the present study first uses a data-driven system identification (system ID) approach to estimate the dynamic model of the considered vehicle (a tracked unmanned ground vehicle (UGV)) as a state-space linear dynamic model. It is shown that the identified model with two-channel inputs and three-channel outputs achieves a fitting of more than 45% between the predicted and measured position and posture of the vehicle. The S-curve and L-shape path-following performance of the tracked vehicle based on MPC with the identified state-space dynamic model is significantly improved. Furthermore, a nonlinear MPC with a long short-term memory (LSTM) model is utilized to adapt different kinds of working environments such as on sand land or in rainy day. According to different system input and output, a suitable model via the LSTM network is estimated in real time and utilized in the nonlinear MPC to enforce the tracked vehicle to follow the path accurately.
KEYWORDS: Systems modeling, Agriculture, Vehicle control, Unmanned ground vehicles, Control systems design, Control systems, Applied research, Unmanned vehicles, Process control, Photonics
Unmanned ground vehicles (UGVs) will be widely adopted in agricultural applications. To accomplish autonomous cruising in farm, path following is an essential skill. However, in the process of field cruising, some obstacles such as wild animals or motorcycles are present. In this study, tracked vehicles are utilized with deep deterministic policy gradient (DDPG) compensating for model uncertainties and achieving collision avoidance simultaneously. Among all, the most important issue is to keep the UGV following the predetermined path in specific agricultural field environment and coping with the uncertainty of the surroundings. Path following and obstacle avoidance of field tracked vehicles are conducted by using model predictive control (MPC) with a controller (agent) trained by DDPG. Therefore, we proposed control algorithm fusion with MPC and model-free DDPG.
KEYWORDS: Robots, Kinematics, Visual process modeling, RGB color model, System identification, Simulink, Visualization, Cameras, Control systems, Actuators
Model predictive control (MPC) with prediction and control horizons under multivariable constraints can prompt field tracked vehicles to follow the reference path accurately. However, a kinematic model or a classic dynamic model of a vehicle is needed in MPC, and both of them must be linearized and hence the computation cost is large. Also, the parameters of a classic dynamic model are difficult to be measured. In this paper, system identification approach for estimated the linear state-space dynamic model of a field tracked vehicle in farm has been utilized. The dynamic model has been identified with more than 50% estimated fitting. Using the dynamic model, a linear MPC can be adopted, and hence the computation can be saved more than 2/3, compared with the conventional nonlinear MPC with a kinematic model. Furthermore, the tracked vehicle adopted the linear MPC with the dynamic model can achieve superior S-curve and L-shape path following.
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