Acceleration Feedback Enhanced <I>H</I><SUB>∞</SUB> Disturbance Attenuation Control for a Class of Nonlinear Underactuated Vehicle Systems

Abstract

In this paper, a generalized acceleration feedback control (AFC) design method, named AFC enhanced H∞ controller, is proposed for both fullactuated and underactuated nonlinear autonomous vehicle systems. The AFC is designed as a robust enhancement to the normal control based on known dynamics. First, in order to reject the uncertainties and external disturbances, a linear prefilter is used in the new AFC design method to replace the high gain in the normal AFC. Then, backstepping algorithm is applied to the AFC design of underactuated systems. The analysis of both the disturbance attenuation in frequency domain and input-output finite gain L2 stability shows the new controller design method is applicable. In the end, simulations are conducted with respect to the tracking control of unmanned model helicopter. The results are compared with those obtained by the tracking control without AFC to verify the feasibility of the new method. Key words Disturbance attenuation, acceleration feedback control, underactuated systems, backstepping, nonlinear H∞ control Unmanned vehicles are being anticipated to handle some high-risk tasks that are dangerous or even impossible for humans. The general goal of the autonomous control for unmanned vehicles is to enable these systems to complete tasks with minimal human interventions. Precise tracking control is a key technology for unmanned systems which are widely used in the areas such as satellite clusters, air traffic control, and the U×Vs in battlefield (unmanned ground/surface/air vehicles) [1] . However, there are at least three difficulties or challenges in the tracking control of unmanned vehicles.1) The dynamics of most unmanned systems are highly nonlinear, time-varying, and coupled, which might be too complicated to be used for controller design. The controller based on a linearized or simplified model might only guarantee a local performance or result in unexpected tracking error because of the model differences.2) The working environments of unmanned vehicles are usually dynamic, complex, and unstructured, which bring unpredictable disturbances to the control system, for example, the aerodynamics of UAV and the wave/wind for USV.3) Many unmanned vehicles, such as UAV [2] , USV [3−4] , and UUV [5] are underactuated, i.e., a system possessing more degrees of freedom than independent control inputs, which might bring more difficulties for its controller design.Thus, how to overcome the above difficulties and achieve high tracking performance has been one of the main tasks of the autonomous control of unmanned vehicles.Traditional robust and adaptive control methods for uncertainty and external disturbances suffer from several problems, including conservativeness because of the inaccuracy in the preassumption of uncertainties, online divergence because of unknown external disturbances, and the complication for real-time implementation.Many researches on the trajectory tracking of underactuated vehicles have been proposed [6] , among which backstepping technique so...

Multiple robots formation control based on receding horizon optimization

“…Step III: Transmit the control inputs (19) to the corresponding robot systems and return Step I. Ű In controller III, u(T * , x, IJ) is a parameterized stable controller. And it can be constructed by the method in reference [13], called the GPMN control, as following Eq.…”

“…The planar dynamics of the helicopter, i.e., considering only the planar dynamics of a helicopter, can be denoted as follows [19] , 2 2 9.8cos sin 9. I I T T I I T T I I T T I I T T I I T T I I T T x y e = e =…”

“…where the detailed expression of f 1 , f 2 , g 1 , g 2 , g 3 , and g 4 can be found in reference [19].…”