Honglun Wang

Active disturbance rejection based trajectory linearization control for hypersonic reentry vehicle with bounded uncertainties.

This paper investigates a novel compound control scheme combined with the advantages of trajectory linearization control (TLC) and alternative active disturbance rejection control (ADRC) for hypersonic reentry vehicle (HRV) attitude tracking system with bounded uncertainties. Firstly, in order to overcome actuator saturation problem, nonlinear tracking differentiator (TD) is applied in the attitude loop to achieve fewer control consumption. Then, linear extended state observers (LESO) are constructed to estimate the uncertainties acting on the LTV system in the attitude and angular rate loop. In addition, feedback linearization (FL) based controllers are designed using estimates of uncertainties generated by LESO in each loop, which enable the tracking error for closed-loop system in the presence of large uncertainties to converge to the residual set of the origin asymptotically. Finally, the compound controllers are derived by integrating with the nominal controller for open-loop nonlinear system and FL based controller. Also, comparisons and simulation results are presented to illustrate the effectiveness of the control strategy.

A novel robust hybrid gravitational search algorithm for reusable launch vehicle approach and landing trajectory optimization

The approach and landing (A&L) trajectory optimization is a critical problem for secure flight of reusable launch vehicle (RLV). In this paper, the A&L is divided into two sub-phases, glide phase and flare phase respectively. The flare phase is designed firstly based on the desired touchdown (TD) states. Then, the glide phase is optimized using a proposed novel robust hybrid algorithm that combines advantages of the gravitational search algorithm (GSA) and gauss pseudospectral method (GPM). In the proposed hybrid algorithm, an improved GSA (IGSA) is presented to enhance the convergence speed and the global search ability, by adopting the elite memory reservation strategy and an adaptive gravitational constant adaption with individual optimum fitness feedback. At the beginning stage of search process, an initialization generator is constructed to find an optimum solution with IGSA, due to its strong global search ability and robustness to the initial values. When the change in fitness value satisfies the predefined value, the IGSA is replaced by the GPM to accelerate the search process and to get an accurate optimum solution. Finally, the Monte Carlo simulation results are analyzed in detail, which demonstrate the proposed method is practicable. The comparison with GSA and GPM shows that the hybrid algorithm has better performance in terms of convergence speed, robustness and accuracy for solving the RLV A&L trajectory optimization problem. The reusable launch vehicle (RLV) approach and landing (A&L) trajectory is formulated into two sub-phases: glide and flare.A novel robust hybrid optimization algorithm combined advantages of gravitational search algorithm (GSA) and gauss pseudospectral method (GPM) is proposed.The GSA is improved by adding the elite memory reservation strategy and adaptive gravitational constant adaption.The hybrid algorithm is firstly applied to the trajectory optimization of RLV A&L, comparative study is presented to further demonstrate its superiority.

Dynamic Adaptive Ant Lion Optimizer applied to route planning for unmanned aerial vehicle

This paper proposes a novel Dynamic Adaptive Ant Lion Optimizer (DAALO) for route planning of unmanned aerial vehicle. Ant Lion Optimizer (ALO) is a new intelligent algorithm motivated by the phenomenon that antlions hunt ants in nature, showing the great potential to solve the optimization problems of engineering. In the proposed DAALO, the random walk of ants is replaced by Levy flight to make ALO escape from local optima more easily. Besides, by introducing the improvement rate of population as the feedback, the size of trap is adjusted dynamically based on the 1/5 Principle to improve the performance of ALO including convergence accuracy, convergence speed and stability. Compared to some other bio-inspired methods, the proposed algorithm are utilized to find the optimal route in two different environments such as mountain model and city model. The comparison results demonstrate the effectiveness, robustness and feasibility of DAALO.

A hybrid backtracking search optimization algorithm for nonlinear optimal control problems with complex dynamic constraints

Nonlinear optimal control (NOC) problem with complex dynamic constraints (CDC) is difficult to compute even with direct method. In this paper, a hybrid two-stage approach integrating an improved backtracking search optimization algorithm (IBSA) with the hp-adaptive Gauss pseudo-spectral methods (hpGPM) is proposed. Firstly, BSA is improved to enhance its convergent speed and the global search ability, by adopting the harmony search strategy and an adaptive amplitude control factor with individual optimum fitness feedback. Then, at the beginning stage of the hybrid search process, an initialization generator is constructed using IBSA to find a near optimum solution. When the change in fitness function approaches to a predefined value which is small enough, the search process is replaced by hpGPM to accelerate the search process and find an accurate solution. By this way, the hybrid algorithm is able to find a global optimum more quickly and accurately. Two NOC problems with CDC are examined using the proposed algorithm, and the corresponding Monte Carlo simulations are conducted. The comparison results show the hybrid algorithm achieves better performance in convergent speed, accuracy and robustness. The improved BSA (IBSA) is proposed to enhance the convergent speed and the global search ability, by adopting the harmony search strategy and an adaptive amplitude control factor with individual optimum fitness feedback.The hp-adaptive gauss pseudospectral method (hpGPM) is adopted to overcome the drawbacks of the fixed discrete points generated during the conventional controls parameterization.A novel hybrid two-stage optimization algorithm integrating the IBSA with the hpGPM is proposed to find a global optimum more quickly and accurately.

Modeling and LPV flight control of the Canard Rotor/ Wing unmanned aerial vehicle

The concept, known as the Canard Rotor/Wing (CRW) unmanned aerial vehicle (UAV) combines the hover flight characteristic of a helicopter with high subsonic cruise of a fixed-wing aircraft. The longitudinal flight dynamic model of CRW was developed, and the trim result was derived in the full flight envelope. The linear model of CRW was derived by the Jacobian linear method, and it was nonlinearly dependent on the time-varying flight speed and altitude. The tensor-product (TP) model transformation was adopted to transform the model to a convex polytopic model form. Hence, a linear parameter-varying (LPV) synthesis method was used to design the flight control system of CRW in the rotary mode. The simulation results show that the desired performance objectives are achieved in the rotary flight stage.

Adaptive Neural Network Dynamic Inversion with Prescribed Performance for Aircraft Flight Control

An adaptive neural network dynamic inversion with prescribed performance method is proposed for aircraft flight control. The aircraft nonlinear attitude angle model is analyzed. And we propose a new attitude angle controller design method based on prescribed performance which describes the convergence rate and overshoot of the tracking error. Then the model error is compensated by the adaptive neural network. Subsequently, the system stability is analyzed in detail. Finally, the proposed method is applied to the aircraft attitude tracking control system. The nonlinear simulation demonstrates that this method can guarantee the stability and tracking performance in the transient and steady behavior.

Path planning for solar-powered UAV in urban environment

Abstract Aiming at the complexity and particularity of urban environment, a solar-powered UAV (SUAV) path planning framework is proposed in this paper. The framework can be decomposed into three aspects to resolve. First, to make SUAV avoid the building obstacles, a nature-inspired path planning method called Interfered Fluid Dynamical System (IFDS) is introduced. Aiming at the defect that the traditional IFDS is not suitable for SUAV energy optimization calculation, the dynamic constraints and model are introduced to IFDS. The modified IFDS, called Restrained IFDS (RIFDS), is proposed. Second, to resolve the path planning issue efficiently, a novel intelligent optimization algorithm called Whale Optimization Algorithm (WOA) is selected as the basic framework solver. To further overcome the drawback of local minima, adaptive chaos-Gaussian switching solving strategy and coordinated decision-making mechanism are introduced to the basic WOA. The modified algorithm, called Improved WOA (IWOA), is proposed. Third, to solve the accurate modeling problem of solar energy in urban environment, two measures are adopted: (1) A practical judgment method for sunlight occlusions is proposed; (2) Aiming at some unreasonable aspects in the solar energy production model, the received solar energy is modified and recalculated by ASHRAE Clear Sky Model and the solar irradiance calculation principle for slant surfaces in this paper. Finally, the effectiveness of the proposed framework is tested by the simulations.

3-D dynamic path planning for UAV based on interfered fluid flow

A 3-D dynamic path-planning algorithm based on interfered fluid flow (DA) is proposed for unmanned aerial vehicle (UAV) in dynamic environment. This paper first describes the mathematical modeling of convex obstacles. Then the static algorithm based on interfered fluid flow (SA) is introduced and improved, whose planning path conforms to the general characteristic of phenomenon that running water can avoid rock and arrive at destination. By updating the information of obstacles and obtaining the relative velocity, we then transform the dynamic problem to static problem. Therefore, DA is simplified by adopting SA. Finally, this dynamic algorithm proves to be efficient for real-time path planning by simulation results.

Three-dimensional path planning for unmanned aerial vehicles based on fluid flow

Using the principles of fluid mechanics for flow around objects, a three dimensional (3D) path planning method for unmanned aerial vehicles (UAVs) in complex environments is studied. As a potential field method, it theoretically guarantees to avoid local minima with smooth paths and the modeling of environment is simple. First, an analytical solution is derived to determine the steady 3D fluid flow acting on a single spherical obstacle. Subsequently, an interpolation function is introduced to multiple obstacles avoidance. Finally, the maneuverability constraints of the UAV are imposed and flight paths are obtained. Added the effect of human factors, a Generalized Fuzzy Competitive Neural Network (G-FCNN) is proposed to evaluate the flight paths. In simulation, the path is smoother and more reasonable. In terms of evaluation, G-FCNN could considerate multiple factors and the result is satisfied.

A Novel Method of Robust Trajectory Linearization Control Based on Disturbance Rejection

A novel method of robust trajectory linearization control for a class of nonlinear systems with uncertainties based on disturbance rejection is proposed. Firstly, on the basis of trajectory linearization control (TLC) method, a feedback linearization based control law is designed to transform the original tracking error dynamics to the canonical integral-chain form. To address the issue of reducing the influence made by uncertainties, with tracking error as input, linear extended state observer (LESO) is constructed to estimate the tracking error vector, as well as the uncertainties in an integrated manner. Meanwhile, the boundedness of the estimated error is investigated by theoretical analysis. In addition, decoupled controller (which has the characteristic of well-tuning and simple form) based on LESO is synthesized to realize the output tracking for closed-loop system. The closed-loop stability of the system under the proposed LESO-based control structure is established. Also, simulation results are presented to illustrate the effectiveness of the control strategy.