Continuous Sliding Mode Controller with Disturbance Observer for Hypersonic Vehicles

In this paper, a continuous sliding mode controller with disturbance observer is proposed for the tracking control of hypersonic vehicles to suppress the chattering. The finite time disturbance observer is involved to make that the continuous sliding mode controller has the property of disturbance rejection. Due to continuous terms replacing the discontinuous term of traditional sliding mode control, switching modes of velocity and altitude firstly arrive at small regions with respect to disturbance observation errors. Switching modes keep zero and velocity and altitude asymptotically converge to their reference commands after disturbance observation errors disappear. Simulation results have proved the proposed method can guarantee the tracking of velocity and altitude with continuous sliding mode control laws, and also has the fast convergence rate and robustness. © 2014 Chinese Association of Automation.

Flight control for a flexible air-breathing hypersonic vehicle based on quasi-continuous high-order sliding mode

The focus of this paper is on control design and simulation for the longitudinal model of a flexible air-breathing hypersonic vehicle（FAHV）.The model of interest includes flexibility effects and intricate couplings between the engine dynamics and flight dynamics.To overcome the analytical intractability of this model,a nominal control-oriented model is constructed for the purpose of feedback control design in the first place.Secondly,the multi-input multi-output（MIMO） quasi-continuous high-order sliding mode（HOSM） controller is proposed to track step changes in velocity and altitude,which is based on full state feedback.The simulation results are presented to verify the effectiveness of the proposed control strategy.

Data-driven active vibration control for helicopter with trailing-edge flaps using adaptive dynamic programming

The helicopter Trailing-Edge Flaps(TEFs)technology is one of the recent hot topics in morphing wing research.By employing controlled deflection,TEFs can effectively reduce the vibration level of helicopters.Thus,designing specific vibration reduction control methods for the helicopters equipped with trailing-edge flaps is of significant practical value.This paper studies the optimal control problem for helicopter-vibration systems with TEFs under the framework of adaptive dynamic programming combined with Reinforcement Learning(RL).Time-delay and disturbances,caused by complexity of helicopter dynamics,inevitably deteriorate the control performance of vibration reduction.To solve this problem,a zero-sum game formulation with a linear quadratic form for reducing vibration of helicopter systems is presented with a virtual predictor.In this context,an off-policy reinforcement learning algorithm is developed to determine the optimal control policy.The algorithm utilizes only vertical vibration load data to achieve a policy that reduces vibration,attains Nash equilibrium,and addresses disturbances while compensating for time-delay without knowledge of the dynamics of the helicopter system.The effectiveness of the proposed method is demonstrated in a virtual platform.

A distributed approach for lidar-based relative state estimation of multi-UAV in GPS-denied environments

In this paper,we present a distributed framework for the lidar-based relative state estimator which achieves highly accurate,real-time trajectory estimation of multiple Unmanned Aerial Vehicles(UAVs)in GPS-denied environments.The system builds atop a factor graph,and only on-board sensors and computing power are utilized.Benefiting from the keyframe strategy,each UAV performs relative state estimation individually and broadcasts very partial information without exchanging raw data.The complete system runs in real-time and is evaluated with three experiments in different environments.Experimental results show that the proposed distributed approach offers comparable performance with a centralized method in terms of accuracy and real-time performance.The flight test demonstrates that the proposed relative state estimation framework is able to be used for aggressive flights over 5 m/s.

A Parallel Mapping and Planning Strategy for Micro Aerial Vehicle

Autonomous fight is a fundamental problem for various saplications of miaro aerial vehicles(MAVs).Thanks to the dewvelopment of Gudance.Navigation and Control(GNC)techndogle,the rsearch on this problem i abo becoming mature.However,safe fight in unknown,dut tered enviouments remains an open question,epeaially with real-time reguirement on onboard computer.This paper propoes a framnework including prlelly mapping and planning and implements it on Graphics Pmocessing Unit(GPU).First,a spherical coomlinate proijection is used in the ocupency grid map to avold menory cnflicts.After that,in the planning phase,a method baed on latice state space sampling is applied to obtain mutiple rajectorfe parlelly.Then,we design a series of aoft constraints to ensure that the MAV is in a safe known space with optimal dynamics.By solving the cost for each trajectory and comparison,the optimal trajectory can be generated.The efectivenes of the propeed strategy is demonstrated through simulation testa.