| As a typical multi-functional construction machinery,the hydraulic excavator plays an irreplaceable role in the construction field.The automation of the hydraulic excavator can significantly improve work efficiency and accuracy,which has very important social and economic values.In this thesis,an autonomous hydraulic excavator with electronically controlled pilot proportional pressure reducing valves(EPPRVs)and 3-position-6-way-open-center proportional directional valves is taken as the research object.With considering the characteristics of the autonomous hydraulic excavator and utilizing nonlinear control technology,the high precision control method for the electro-hydraulic system of the autonomous hydraulic excavator are studied by means of theoretical analysis,simulation research and practical experiments.This study aims to lay a valuable theoretical and experimental foundation for the automation of the hydraulic excavator under various typical working conditions,and also provides a reference for the control of the electro-hydraulic system of other construction machinery.This thesis studies from four aspects:EPPRV nonlinear compensation,single actuator motion control,bucket trajectory motion control and swing torque control.Firstly,an adaptive robust control(ARC)method is proposed to improve the control accuracy of the PPRV with nonlinear dead zone and hysteresis.Secondly,for the motion control of the single actuator with large inertia,variable load and uncertain reaction force,an adaptive neural terminal sliding mode control(ANTC)method is proposed,which ensures that the control error converges in finite time.Furthermore,the control method for the single actuator is extended to the multi-input-multi-output system to realize the control of the bucket trajectory.And for the working tasks that require higher control precision and need multiple actuators to work cooperatively,the synchronization control theory is introduced to improve the synchronization control performance among all the actuators to further improve the control accuracy of the bucket trajectory.Lastly,to solve the problem that the control gain is zero in swing torque control,a novel pump-pressure-compensator is designed to guarantee that the system is always controllable,and an adaptive neural controller is put forward to further improve the torque control performance.The specific research contents of this thesis are as follows:In Chapter 1,the importance of developing the control method for the electro-hydraulic system of an autonomous hydraulic excavator is discussed.The characteristics of the electro-hydraulic system of the autonomous hydraulic excavator are analyzed,and the main factors that affecting the controller design are pointed out.Besides,the research status of electro-hydraulic system actuator motion control,hydraulic excavator bucket trajectory control and force/torque control are summarized,and their deficiencies are analyzed.Finally,the research contents of this thesis are put forward.In Chapter 2,the autonomous excavator system is designed.By replacing the traditional manually controlled pilot proportional pressure reducing valves with EPPRVs,the hydraulic excavator can be controlled by electrical signals,thus laying the foundation for the autonomous operation under various typical working conditions.Then the nonlinear control method for the EPPRV is studied to suppress the influence of the nonlinear dead zone and hysteresis on its control accuracy.Firstly,the mathematical model of the EPPRV is established and the nonlinear dead zone and hysteresis are analyzed.In order to facilitate the controller design process,the original system model is represented as a cascade model composed of the dead zone,hysteresis and a linear plant,and the validity of the model is verified by parameter identification experiments.In order to deal with the parameter uncertainties and uncertain nonlinearities in the model,an ARC control method based on a reduced-order-system model is proposed,which can theoretically guarantee the bounded control error and its effectiveness is verified by comparative simulations and experiments.Based on the ARC control method,a double closed loop feedback control strategy is proposed for the actuator motion control and the swing torque control in the subsequent chapters.Comparative experimental results show that with the proposed strategy,the transient response speed and the steady-state control accuracy are significantly improved.In Chapter 3,with considering the characteristics of the autonomous hydraulic excavator,the high precision motion control method for the single actuator of the autonomous hydraulic excavator is studied.An ANTC control method that does not dependent on the parameters of the system model is proposed.First,the original state-feedback system is represented as an output-feedback system in the form of integral chain by coordinate transformation,which makes it possible to design a controller without the complex backstepping algorithm.To estimate the immeasurable state variables,the high-order sliding mode observer is introduced,whose observation error can converge in finite time.Based on the universal approximation ability of the neural network and the terminal sliding mode control theory,an ANTC control method is proposed.With on-line updated weights,the echo state neural network is used to approximate the unknown system function.In addition,a robust control law is introduced to deal with the unknown lumped nonlinearity,which is composed of the observation error and the neural network approximation error,and a parameter adaption law is developed to on-line estimate the upper bound of the unknown lumped nonlinearity to suppress its impact on system performance.The proposed method can theoretically guarantee the convergence of the control error in finite time,and its effectiveness is verified by comparative simulations and experiments.In Chapter 4,the control method for the bucket trajectory of the hydraulic excavator is studied.First,the kinematics model of the hydraulic excavator is presented,and then the bucket trajectory planning is carried out.Considering the system dynamics model,the control method for the single actuator proposed in Chapter 3 is extended to the multi-input-multi-output system,and a bucket trajectory controller is designed,which can guarantee that the control error of each actuator converges in finite time.To verify the effectiveness of the proposed method,comparative experiments are carried out for the typical "dig-180° rotation-unloading" working condition.For working tasks that require higher control accuracy,a novel bucket trajectory synchronization controller is designed.It can not only guarantee the control performance of each actuator,but also ensure that the synchronization errors among the actuators are convergent in finite time,and then the control accuracy of the bucket trajectory can be further improved.Finally,the effectiveness of the synchronization controller is verified by a space-straight-line tracking control experiment.In Chapter 5,the swing torque control of the autonomous hydraulic excavator is studied for the side wall pressing and excavation work.First,the characteristics of the swing motor hydraulic system are analyzed under the working condition of sidewall pressing and excavation.And it is pointed out that the existence of the 3-position-6-way-open-center proportional directional valve results in that the outlet pressure of the pump and the flow rates to the actuator are controlled coupled,which makes that the swing torque control gain is zero and the system uncontrollable under certain conditions.To solve this problem,a novel pump pressure compensator is designed to make sure that the output torque of the swing motor is always controllable.Furthermore,an adaptive neural controller is synthesized to improve the torque control performance without knowing the parameters of the system model.The pump-pressure-compensation based adaptive neural controller proposed in this chapter can theoretically guarantee the bounded torque control error.Finally,the effectiveness of the proposed method is verified by comparative experiments.In Chapter 6,the main research work and the innovations of this thesis are summarized,and the relevant future research work is discussed. |
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