Research On Adaptive Control Method Of Sanitation Vehicle Trajectory Tracking

In recent years,economic development has driven the deepening of domestic urbanization in China.Road construction is also developing.The workload of road cleaning and maintenance caused by this has also been increasing.The technological level of environmental protection equipment in our country is relatively backward.Problems such as the aging population and the harsh cleaning environment have also brought difficulties to the environmental sanitation work.With the development of artificial intelligence,the automatic operation of sanitation equipment is an important development direction in the future.This paper takes the unmanned sanitation vehicle as the research object to study the front wheel steering angle control and driving motor control problems during the trajectory tracking of the sanitation vehicle.High-precision trajectory tracking control is particularly important for the safe driving of unmanned sanitation vehicles.The sanitation vehicle system is a complex non-linear system.Under different road and climatic conditions,the friction coefficient and other parameters between the tire and the ground change in real time.At the same time,the vehicle load is constantly changing with the weight of the garbage cleaned by the sanitation vehicle.It is difficult to establish an accurate vehicle and environmental model.Therefore,it has certain limitations to implement intelligent control of vehicles through modeling in practical applications.In addition,unknown disturbances caused by pavement factors such as potholes and slope,as well as environmental factors such as bad weather,have an impact on the steering angle control of the front wheels and the control of the driving motor.This paper is based on the model-free adaptive control algorithm and iterative learning control algorithm.Considering the working conditions of shopping malls and parks,the unmanned sanitation vehicle mainly periodically repeats cleaning and watering on fixed road sections.This utilizes the large amount of running data generated during the trajectory tracking process to improve the self-adaptability and control accuracy of control system.The specific contents are as follows:1.This article is aimed at the steering angle control method of unmanned sanitation vehicle track tracking.The changeable model parameters of the sanitation vehicle trajectory tracking control system are considered which is very difficult to establish an accurate model for controller design.The parameters of the algorithm also need to be re-tuned when the vehicle type is different.It is low flexibility and poor portability.So an improved model-free adaptive control method is proposed.The error time-varying proportional control item is added to the steering input.Then the trajectory tracking control scheme is designed.The convergence analysis is also carried out.This paper compares and simulates the MFAC scheme and PID control scheme through two tracking trajectories of straight line and curve.The simulation results verify the effectiveness of the improved model-free adaptive control scheme applied to the trajectory tracking system of the unmanned sanitation vehicle.It has good adaptability and tracking speed.2.This article focuses on the periodic repetitive work characteristics of sanitation vehicles in fixed sections of roads under conditions such as shopping malls and parks.The trajectory tracking control of the sanitation vehicle is researched based on the iterative learning control method.First,the intelligent PD type iterative learning control method is added.The error information of the previous operation and the control input signal to correct the control input signal of the current operation are utilized.It also adds an iterative difference estimation algorithm to estimate unknown disturbances caused by road bumps and continuous changes in the weight of body waste.Furthermore,a new parameter iteration update rate and optimal learning control rate are designed.The trajectory tracking control problem of the unmanned sanitation vehicle is studied based on the optimal iterative learning control method.For the disturbance problem,an iterative extended state observer is added to compensate for the unknown disturbance.Finally,the effectiveness of the two algorithms is verified by simulation.3.The trajectory tracking control system of the unmanned sanitation vehicle presents a complex nonlinear relationship with time.The vehicle system itself is also a complex nonlinear system that is difficult to model and the model is time-varying.These are full of challenges to the control accuracy and robustness of trajectory tracking.At the same time,there are problems in self-learning and low control accuracy in the trajectory tracking control process.The trajectory tracking control system of the sanitation vehicle is transformed into a full-format dynamic linearized data model in the iterative domain with time-varying parameters and nonlinear uncertain terms.In addition,the time difference estimation algorithm is added to design the trajectory tracking model-free adaptive iterative learning control scheme based on the optimal performance index.And simulation verification is carried out.The results show that the improved algorithm has high control accuracy,fast action response and good adaptability.4.In response to disturbances such as potholes,slopes and increasing weight of body waste when cleaning roads by sanitation vehicles,the speed control problem of the drive motor is affected.A cascade model-free adaptive control algorithm is designed to control the drive motor speed control system of the sanitation vehicle.The unknown disturbance is estimated by designing a sliding mode observer.And the outer speed loop model-free adaptive controller with disturbance is designed.At the same time,the internal current loop quadrature axis model-free adaptive controller is designed.It forms a cascade model-free control structure with the outer speed loop.The simulation verifies that the proposed control method can make the driving motor of the unmanned sanitation vehicle have better speed tracking accuracy and strong anti-interference ability under the influence of unknown disturbance.It improves the speed regulation performance of the drive motor.