Abstract
The quad tilt rotor (QTR) has complex dynamics characteristics, and the environmental factors have a great influence on it. This increases the difficulty of its control especially in transition mode. To solve this problem, the design of the controller based on active disturbance rejection control (ADRC) with a novel tuning algorithm is proposed in this paper. The aerodynamic models of propeller, wing, vertical tail and fuselage are build respectively by using the idea of component modeling. The pitch channel of the linearized flight dynamic model is provided for the control system. A simple tuning method of active disturbance rejection control for the quad tilt rotor in transition process that achieves high performance and good robustness is presented. The proposed method makes ADRC become easy to tune and more practical. The problem of parameter tuning is turned into a multi-objective optimization problem, and using radial basis function(RBF) neural network with particle swarm optimization algorithm(PSO) and bacterial foraging optimization algorithm(BFO) hybrid algorithm tuning method(NBPO) to fit the tuning rules. We introduce the control input, the overshoot and rise time of the system into the fitness function. The local and global optimal solutions are introduced into the chemotaxis and migration to improve the information exchange ability of BFO. The correctness and effectiveness of the NBPO tuning method were verified by numerical simulation results, compared with the one parameter tuning method(OPM) and PID, the results showed that the NBPO tuning method can greatly improve the control performance of the system.
Highlights
The quad tilt rotor (QTR) is a novel vehicle [1]–[7] which combines the characteristics of helicopter and fixed wing aircraft
Based on the above research background, in order to solve the problems of QTR time-varying, nonlinear control and active disturbance rejection control (ADRC) parameter tuning, the parameters tuning problem is turned into multi-objective optimization problem in this paper
There are many ways to evaluate controller performance, We usually take the error of the controller e(t) as the functional integral as the objective function, the main evaluation standards are Integral Square Error (ISE), Integral Absolute Error (IAE), Integral of Time multiplied by Square Error (ISTE) and Integral of Time multiplied by Absolute Error (ITAE)
Summary
The quad tilt rotor (QTR) is a novel vehicle [1]–[7] which combines the characteristics of helicopter and fixed wing aircraft. Due to the special configuration of quad tilt rotor, the aerodynamic characteristics and stability of quad tilt rotor will change significantly with the change of tilt angle and forward flight speed in the transition mode This brings great difficulty to the design of flight control system. Z. Wang et al.: Tuning of ADRC for QTR in Transition Process Based on NBPO Hybrid Algorithm on the model, which can handle various internal uncertainties and has strong robustness. Based on the above research background, in order to solve the problems of QTR time-varying, nonlinear control and ADRC parameter tuning, the parameters tuning problem is turned into multi-objective optimization problem in this paper. A novel neural network with particle swarm and bacterial foraging hybrid algorithm(NBPO) is proposed to tuning the parameters of ADRC. Verification of the NBPO hybrid algorithm by comparing with one parameter tuning method(OPM) and PID in numerical simulation is carried out. The code of this paper is upload on the github and can be found in https://github.com/MELODYLV/MATLAB.git
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