A Quantum-behaved Pigeon-Inspired Optimization approach to Explicit Nonlinear Model Predictive Controller for quadrotor

Purpose The purpose of this paper is to simplify the Explicit Nonlinear Model Predictive Controller (ENMPC) by linearizing the trajectory with Quantum-behaved Pigeon-Inspired Optimization (QPIO). Design/methodology/approach The paper deduces the nonlinear model of the quadrotor and uses the ENMPC to track the trajectory. Since the ENMPC has high demand for the state equation, the trajectory needed to be differentiated many times. When the trajectory is complicate or discontinuous, QPIO is proposed to linearize the trajectory. Then the linearized trajectory will be used in the ENMPC. Findings Applying the QPIO algorithm allows the unequal distance sample points to be acquired to linearize the trajectory. Comparing with the equidistant linear interpolation, the linear interpolation error will be smaller. Practical implications Small-sized quadrotors were adopted in this research to simplify the model. The model is supposed to be accurate and differentiable to meet the requirements of ENMPC. Originality/value Traditionally, the quadrotor model was usually linearized in the research. In this paper, the quadrotor model was kept nonlinear and the trajectory will be linearized instead. Unequal distance sample points were utilized to linearize the trajectory. In this way, the authors can get a smaller interpolation error. This method can also be applied to discrete systems to construct the interpolation for trajectory tracking.

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Nonlinear model predictive controller robustness extension for unmanned aircraft

International Journal of Intelligent Unmanned Systems ◽

10.1108/ijius-01-2015-0002 ◽

2015 ◽

Vol 3 (2/3) ◽

pp. 93-121 ◽

Cited By ~ 2

Author(s):

Gonzalo Garcia ◽

Shahriar Keshmiri ◽

Thomas Stastny

Keyword(s):

Nonlinear Model ◽

Flight Control ◽

Unsteady Aerodynamics ◽

Control Design ◽

Unmanned Aircraft ◽

Model Predictive Controller ◽

Frequency Dependent ◽

Content Type ◽

Predictive Controller ◽

Predictive Controllers

Purpose – Nonlinear model predictive control (NMPC) is emerging as a way to control unmanned aircraft with flight control constraints and nonlinear and unsteady aerodynamics. However, these predictive controllers do not perform robustly in the presence of physics-based model mismatches and uncertainties. Unmodeled dynamics and external disturbances are unpredictable and unsteady, which can dramatically degrade predictive controllers’ performance. To address this limitation, the purpose of this paper is to propose a new systematic approach using frequency-dependent weighting matrices. Design/methodology/approach – In this framework, frequency-dependent weighting matrices jointly minimize closed-loop sensitivity functions. This work presents the first practical implementation where the frequency content information of uncertainty and disturbances is used to provide a significant degree of robustness for a time-domain nonlinear predictive controller. The merit of the proposed method is successfully verified through the design, coding, and numerical implementation of a robust nonlinear model predictive controller. Findings – The proposed controller commanded and controlled a large unmanned aerial system (UAS) with unsteady and nonlinear dynamics in the presence of environmental disturbances, measurement bias or noise, and model uncertainties; the proposed controller robustly performed disturbance rejection and accurate trajectory tracking. Stability, performance, and robustness are attained in the NMPC framework for a complex system. Research limitations/implications – The theoretical results are supported by the numerical simulations that illustrate the success of the presented technique. It is expected to offer a feasible robust nonlinear control design technique for any type of systems, as long as computational power is available, allowing a much larger operational range while keeping a helpful level of robustness. Robust control design can be more easily expanded from the usual linear framework, allowing meaningful new experimentation with better control systems. Originality/value – Such algorithms allows unstable and unsteady UASs to perform reliably in the presence of disturbances and modeling mismatches.

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