A study of PID and L1 adaptive control for automatic balancing of a spacecraft three-axis simulator

Purpose–The three-axis simulator relies on the air film between the air bearing and the bearing seat to achieve weightlessness and the frictionless motion condition,which is essential for simulating the micro-disturbance torque of a satellite in outer space.However,at the beginning of the experiment,the disturbance torque caused by the misalignment between the center of gravity of the simulator and the center of rotation of the bearing is the most important factor restricting the use of the space three-axis simulator.In order to solve this problem,it is necessary to set the balance adjustment system on the simulator to compensate the disturbance torque caused by the eccentricity.The paper aims to discuss these issues.Design/methodology/approach–In this paper,a study of L1 adaptive automatic balancing control method for micro satellite with motor without other actuators is proposed.L1 adaptive control algorithm adds the low-pass filter to the control law,which in a certain sense to reduce the high-frequency signal and speed up the response time of the controlled system.At the same time,by estimating the adaptive parameter uncertainty in object,the output error of the state predictor and the controlled object can be stabilized under Lyapunov condition,and the robustness of the system is also improved.The automatic balancing method of PID is also studied in this paper.Findings–Through this automatic balancing mechanism,the gravity disturbance torque can be effectively reduced down to 10−6 Nm,and the automatic balancing time can be controlled within 7 s.Originality/value–This paper introduces an automatic balancing mechanism.The experimental results show that the mechanism can greatly improve the convergence speed while guaranteeing the control accuracy,and ensuring the feasibility of the large angle maneuver of spacecraft three-axis simulator.

Guidance and Control for UAV Aerial Refueling Docking Based on Dynamic Inversion with L_1 Adaptive Augmentation

The guidance and control for UAV aerial refueling docking based on dynamic inversion with L1 adaptive augmentation is studied.In order to improve the tracking performance of UAV aerial refueling docking,aguidance algorithm is developed to satisfy the tracking requirement of position and velocity,and it generates the UAV flight control loop commands.In flight control loop,based on the 6-DOF nonlinear model,the angular rate loop and the attitude loop are separated based on time-scale principle and the control law is designed using dynamic inversion.The throttle control is also derived from dynamic inversion method.Moreover,an L1 adaptive augmentation is developed to compensate for the undesirable effects of modeling uncertainty and disturbance.Nonlinear digital simulations are carried out.The results show that the guidance and control system has good tracking performance and robustness in achieving accurate aerial refueling docking.

Transition control of a tail-sitter unmanned aerial vehicle with L1 neural network adaptive control

The main task of this work is to design a control system for a small tail-sitter Unmanned Aerial Vehicle(UAV)during the transition process.Although reasonable control performance can be obtained through a well-tuned single PID or cascade PID control architecture under nominal conditions,large or fast time-varying disturbances and a wide range of changes in the equilibrium point bring nonlinear characteristics to the transition control during the transition process,which leads to control precision degradation.Meanwhile,the PID controller’s tuning method relies on engineering experiences to a certain extent and the controller parameters need to be retuned under different working conditions,which limits the rapid deployment and preliminary validation.Based on the above issues,a novel control architecture of L1 neural network adaptive control associated with PID control is proposed to improve the compensation ability during the transition process and guarantee the security transition.The L1 neural network adaptive control is revised to solve the multi-input and multi-output problem of the tail-sitter UAV system in this study.Finally,the transition characteristics of the time setting difference between the desired transition speed and the desired transition pitch angle are analyzed.

L_1 adaptive control with sliding-mode based adaptive law

This paper presents an adaptive control scheme with an integration of sliding mode control into the L1 adaptive control architecture, which provides good tracking performance as well as robustness against matched uncertainties. Sliding mode control is used as an adaptive law in the L1 adaptive control architecture, which is considered as a virtual control of error dynamics between estimated states and real states. Low-pass filtering mechanism in the control law design prevents a discontinuous signal in the adaptive law from appearing in actual control signal while maintaining control accuracy. By using sliding mode control as a virtual control of error dynamics and introducing the low-pass filtered control signal, the chattering effect is eliminated. The performance bounds between the close-loop adaptive system and the closed-loop reference system are characterized in this paper. Numerical simulation is provided to demonstrate the performance of the presented adaptive control scheme.

The Variable-Step L1 Scheme Preserving a Compatible Energy Law for Time-Fractional Allen-Cahn Equation

In this work,we revisit the adaptive L1 time-stepping scheme for solving the time-fractional Allen-Cahn equation in the Caputo’s form.The L1 implicit scheme is shown to preserve a variational energy dissipation law on arbitrary nonuniform time meshes by using the recent discrete analysis tools,i.e.,the discrete orthogonal convolution kernels and discrete complementary convolution kernels.Then the discrete embedding techniques and the fractional Gronwall inequality are applied to establish an L^(2)norm error estimate on nonuniform time meshes.An adaptive time-stepping strategy according to the dynamical feature of the system is presented to capture the multi-scale behaviors and to improve the computational performance.