Research On Several Control Methods Of Unmanned Vehicles: From Motion Control To Multi-vehicle Cooperation

Unmanned vehicle plays an extremely important role in intelligent transportation systems and complex high-risk operating environments,which is an intelligent system developed by artificial intelligence disciplines,control science and mobile engineering.Therefore,unmanned vehicles have received widespread attention in many fields such as military,industrial,and scientific communities.The definition of unmanned vehicle is that a vehicle is able to perceive its surroundings without human intervention and autonomously control its behavior based on environmental information.The autonomous control capability of the unmanned vehicle system is very critical to the realization of the unmanned vehicle system.Meanwhile,with the increase in the application scale of unmanned vehicles and the expansion of the application scope,it is also necessary to consider the scenario of multi-vehicle collaboration.In terms of intelligent transportation systems,multi-vehicle collaboration can effectively enhance road safety and improve travel efficiency,which will bring great changes to transportation modes.In the military and civilian fields,multi-vehicle collaboration can also complete more complex tasks.In this thesis,the research object is the unmanned vehicle system and several key control problems of unmanned vehicles are studied from the speed control problems of a single vehicle to the cooperative control problems of multiple vehicles.Specifically,for the speed control problem without input constraints and the speed control problem with input constraints,adaptive control methods and model prediction control methods are proposed,respectively.For multivehicle cooperative control problems,it is mainly divided into multi-vehicle formation control problems and multi-vehicle speed consensus control problems.For these two problems,the formation control algorithm based on multi-agent and the consensus control algorithm based on Nussbaum function are proposed.The main work done and the contributions in this thesis are as follows:(1)Aiming at the speed control problem of bicycle without input constraints,the steering controller and adaptive speed controller based on the bicycle model are designed to realize the lateral and longitudinal dynamic control of the vehicle.Firstly,the car model is simplified to a bicycle model with two degrees of freedom.The dynamic model is decoupled laterally and longitudinally.The steering controller and speed controller are designed respectively so that the car can achieve the corresponding steering angle and longitudinal speed under the global coordinate system.For the speed controller,an adaptive controller is designed for the problem where the control direction of the car is known but the system parameters are unknown,which solves the problem that the parameters of the vehicle system are difficult to measure and unknown.For the steering controller,a feedback control algorithm based on the tangent function is proposed for the limited steering angle of the car,which can reduce the amount of calculation of the controller.The stability proof of the steering controller and the speed controller and the simulation verification of the control system are given respectively.Finally,the steering control system and the speed control system are simulated simultaneously and the simulation results verify the effectiveness of the proposed controller in the vehicle speed tracking control problem laterally and longitudinally.(2)A nonlinear model predictive control algorithm based on a linear regression model is proposed for the vehicle speed tracking control problem constrained by the control input.For generalization of the algorithm and mathematical expression,the longitudinal speed control system of the vehicle is first modeled as a first-order nonlinear system control problem containing linear unknown system parameters.For this problem,the linear unknown parameters in the system are solved by a linear regression model,and then the nominal system is designed according to the obtained unknown system parameters.The contracted terminal domain is designed to ensure the convergence and stability of the closed-loop control system.Finally,the stability proof of the proposed model predictive controller is given.Meanwhile,a nonlinear model control algorithm based on terminal equation constraints is also proposed and the stability proof of the controller is given to prove the effectiveness of the proposed shrinking terminal domain algorithm.The proposed model predictive control algorithm based on a linear regression model is applied to the longitudinal speed control problem of vehicles.Two nonlinear model predictive control algorithms based on terminal equation constraints and terminal shrinkage domain constraints are simulated respectively,and the simulation results prove the effectiveness of the proposed algorithm.(3)For the global speed planning problem of vehicles,the formation method of multi-agent systems is applied to the multi-lane unmanned driving platoons to give the global speed which unmanned vehicles should reach at the next moment.According to the multi-lane driving environment of unmanned vehicles,a distance-based rigid formation algorithm is designed,so that multiple unmanned vehicles can form a rigid formation in a multi-lane road environment.The formation transformation is carried out according to the change of road environment and vehicle requirements.The stability proof is given for the formation control algorithm.Three simulation scenarios including curves,straight lane changes,and platoon overtaking are designed to verify the effectiveness of the formation algorithm.In addition,for the second-order multi-unmanned vehicle formation problem,a position-based formation control algorithm is designed,so that the multi-unmanned vehicle can maintain the formation.The system design framework and stability proof are given,and the simulation results prove the effectiveness of the algorithm.(4)For the problem of speed consensus of multiple vehicles,specifically for multi-vehicle systems with unchanged unknown control direction and changed unknown control direction with unknown bounded disturbance,two types of distributed adaptive controllers based on Nussbaum functions are designed respectively so that the multi-vehicle system can achieve speed consensus.For generalization of the control algorithm and the convenience of a mathematical expression,the speed consensus problem of multiple vehicles is constructed as a consensus problem of multi-agent systems with unknown control directions,that is,the control direction and control parameters of the agent are unknown.In this problem,an adaptive control method based on Nussbaum functions is proposed,which makes the multi-agent system with unknown control directions achieve consensus.In addition,the consensus problems of the first-order multi-agent system and the nonlinear multi-agent system with time-varying unknown control direction and bounded unknown disturbance are studied.Two adaptive controllers based on Nussbaum functions are designed for two problems respectively,and the Lyapunov stability proofs are given.Simulation experiments were performed on two kinds of multi-agent control systems to verify the effectiveness of the algorithm.