Search results

This paper aims to extend the previous contributions about data-driven control in aircraft control system from academy and practice, respectively, combining iteration and learning strategy. More specifically, after returning output signal to input part, and getting one error signal, three kinds of data are measured to design the unknown controller without any information about the unknown plant. Using the main essence of data-driven control, iterative learning idea is introduced together to yield iterative learning data-driven control strategy. To get the optimal data-driven controller, other factors are considered, for example, adaptation, optimization and learning. After reviewing the aircraft control system in detail, the numerical simulation results have demonstrated the efficiency of the proposed iterative learning data-driven control strategy.

First, considering one closed loop system corresponding to the aircraft control system, data-driven control strategy is used to design the unknown controller without any message about the unknown plant. Second, iterative learning idea is combined with data-driven control to yield iterative learning data-driven control strategy. The optimal data-driven controller is designed by virtue of power spectrum and mathematical optimization. Furthermore, adaptation is tried to combine them together. Third, to achieve the combination with theory and practice, our proposed iterative learning data-driven control is applied into aircraft control system, so that the considered aircraft can fly more promptly.

A novel iterative learning data-driven strategy is proposed to efficiently achieve the combination with theory and practice. First, iterative learning and data-driven control are combined with each other, being dependent of adaptation and optimization. Second, iterative learning data-driven control is proposed to design the flight controller for the aircraft system. Generally, data-driven control is more wide in our living life, so it is important to introduce other fields to improve the performance of data-driven control.

To the best of the authors’ knowledge, this new paper extends the previous contributions about data-driven control by virtue of iterative learning strategy. Specifically, iteration means that the optimal data-driven controller is solved as one recursive form, being related with one gradient descent direction. This novel iterative learning data-driven control has more advanced properties, coming from data driven and adaptive iteration. Furthermore, it is a new subject on applying data-driven control into the aircraft control system.

The purpose of this paper is to identify linear model parameters of launch vehicles based on the actual flight test data. To compare the estimated parameters with the ones obtained by two other approaches: identification based on the recorded data from six‐degree‐freedom simulation of motion and linearization of the equations of motion via small‐disturbance theory as an analytical method.

As the vehicle contains all the key issues in system identification such as time‐varying, unstable, nonlinear, and closed‐loop dynamics, Kalman filter method under the autoregressive with exogenous input model structure is used as a powerful method to estimate the dynamic parameters.

Simulation results demonstrate that the linear model parameters used in the vehicle design and analysis should be validated by flight test data to accurate the vehicle dynamic model as more as possible.

One of the most important usages of a linear model of aerospace vehicles is to design their controller. Another application of the algorithm presented in this paper is to estimate online dynamic parameters of the vehicle when they are required for the operation of the control system.

Being strongly affected by vehicle dynamic characteristics, linear model parameters of launch vehicles play important part in their design and analysis.

The purpose of the paper is to examine a fixed wing unmanned aerial vehicle (UAV) as it undergoes five flight scenarios under straight and level, level climb, level turn, climbing turn and level steady heading sideslip conditions in a desired and controlled manner using constrained multi input multi output (MIMO) model predictive controllers (MPCs).

An MPC strategy based on the lateral and longitudinal linear models is proposed for the flight control design. Simulations are carried out for the nonlinear closed-loop aircraft Simulink model available from the University of Minnesota UAV research group with the implemented MIMO MPCs designed in this paper.

The results of the simulations show that the MIMO MPCs can achieve satisfactory performance and flying qualities under three different test conditions in terms of existing unmeasured outputs and unmeasured output disturbances.

The MPCs designed in this paper can be implemented to UAVs. Therefore, the implementation is considered as an advanced research.

The proposed MPC design in this paper provides more flexibility in terms of tracking complex trajectories comparing with the classical controllers in the literature. Besides they provide to change more than one reference of the states at any time.

To guarantee the asymptotic stability of discrete-time nonlinear systems, this paper aims to propose an evolved bat algorithm fuzzy neural network (NN) controller algorithm.

In evolved fuzzy NN modeling, the NN model and linear differential inclusion representation are established for the arbitrary nonlinear dynamics. The control problems of the Fisher equation and a temperature cooling fin for high-speed aerospace vehicles will be described and demonstrated. The signal auxiliary controlled system is represented for the nonlinear parabolic partial differential equation (PDE) systems and the criterion of stability is derived via the Lyapunov function in terms of linear matrix inequalities.

This representation is constructed by sector nonlinearity, which converts the nonlinear model to a multiple rule base for the linear model and a new sufficient condition to guarantee the asymptotic stability.

This study also injects high frequency as an auxiliary and the control performance to stabilize the nonlinear high-speed aerospace vehicle system.

This paper proposes a novel method of fault detection, which is based on H_/H∞ Runge–Kutta observer and an adaptive threshold for a class of closed-loop non-linear systems. The purpose of this paper is to improve the rapidity and accuracy of fault detection.

First, the authors design the H_/H∞ Runge–Kutta fault detection observer, which is used as a residual generator to decouple the residual from the input. The H_ performance index metric in the specified frequency domain is used to describe how sensitive the residual to the fault. The H∞ norm is used to describe the residual robustness to the external disturbance of the systems. The residual generator is designed to achieve the best tradeoff between robustness against unknown disturbances but sensitivity to faults, thus realizing the accurate detection of the fault by suppressing the influence of noise and disturbance on the residual. Next, the design of the H_/H∞ fault detection observer is transformed into a convex optimization problem and solved by linear matrix inequality. Then, a new adaptive threshold is designed to improve the accuracy of fault detection.

The effectiveness and correctness of the method are tested in simulation experiments.

This paper presents a novel approach to improve the accuracy and rapidity of fault detection for closed-loop non-linear system with disturbances and noise.

The purpose of this paper is to develop some interesting results in the field of chaotic synchronization with a new finite-time controller to reduce the time of convergence.

This article proposes a finite-time controller for the synchronization of hyper(chaotic) systems in a given time. The chaotic systems are perturbed by the model uncertainties and external disturbances. The designed controller achieves finite-time synchronization convergence to the steady-state error without oscillation and elimination of the nonlinear terms from the closed-loop system. The finite-time synchronization convergence reduces the hacking duration and recovers the embedded message in chaotic signals within a given preassigned limited time. The free oscillation convergence keeps the energy consumption low and alleviates failure chances of the actuator. The proposed finite-time controller is a combination of linear and nonlinear parts. The linear part keeps the stability of the closed-loop, the nonlinear part increases the rate of convergence to the origin. A generalized form of analytical stability proof is derived for the synchronization of chaotic and hyper-chaotic systems. The simulation results provide the validation of the accomplish synchronization for the Lu chaotic and hyper-chaotic systems.

The designed controller not only reduces the time of convergence without oscillation of the trajectories which can run the system for a given time domain.

This work is originally written by the author.

The purpose of this paper is to describe the application of a systematic single‐range controller design procedure to control a cruise missile.

The controller design approach is based on one describing function model of the missile followed by linear system identification. The missile includes discontinuous nonlinear terms, and therefore, a small signal model is not obtainable by straight linearization. Once a linear approximation to the quasilinear model of the missile is obtained, a H_∞ controller is designed to achieve a robust nonlinear closed‐loop system.

It is found that performance of the closed‐loop system with the applied simple controller design approach competes with one other complicated describing function‐based controller design technique.

At present, the research is limited to design of linear H_∞ controllers.

The major outcome of this paper is the verification that the applied simple describing function‐based H_∞ controller design procedure may be used to design high‐performance controllers for cruise missiles.

This is the first paper that adopts an existing single‐range H_∞ controller design procedure to design high‐performance controllers for cruise missiles.

The paper aims to present a new concept based on a multi‐agent approach in the area of nonlinear model predictive control (MPC) for fast systems.

A contribution to decentralized implementation of MPC is made. The control of the nonlinear system subject to constraints is achieved via a set of actions taken from different agents. The actions are based on an analytical solution and a neural network is used to monitor the closed system using a supervisory loop concept.

The high online computational need to solve an optimal control actions in nonlinear MPC, which results in a non‐convex optimization, is compared with the new proposed concept. Simulation results show that this approach has very remarkable performances in time computing and target arrival.

In practice, each MPC problem of the individual agent in multi‐agent MPC can run in parallel at the same time, instead of in serial, one agent after another. A parallel processor can be useful for real time implementation. However, it is estimated that how much time can be gained by performing the computations in parallel instead of in serial.

The proposed concept discussed in the paper has the potential to be applied to systems with rapid dynamics.

The multi‐agent MPC compares favorably with respect to a numerical optimization routine and also offers a solution for non‐convex optimization problems in single‐input single‐output systems.

Reducing fuel consumption of unmanned aerial vehicles (UAVs) during transient operation is a cornerstone to achieve environment-friendly operations. The purpose of this paper is to develop a control scheme that improves the fuel economy of a turbojet in its full operating envelope.

A novel direct-thrust linear quadratic integral (LQI) approach, comprised by an optimal observer/controller satisfying specified performance parameters, is presented. The thrust estimator, based in a Wiener model, is validated with the experimental data of a micro-turbojet. Model uncertainty is characterized by analyzing variations between the identified model and measured data. The resulting uncertainty range is used to verify closed-loop stability with the circle criterion. The proposed controller provides stable responses with the specified performance in the whole operating range, even with after considering plant nonlinearities. Finally, the direct-thrust LQI is compared with a standard thrust controller to assess fuel economy and performance.

The direct-thrust LQI approach reduced the fuel consumption by 2.1090% in the most realistic scenario. The controllers were also evaluated using the environmental effect parameter (EEP) and transient-thrust-specific fuel consumption (T-TSFC). These novel metrics are proposed to evaluate the environmental impact during transient-thrust operations. The direct-thrust LQI approach has a more efficient fuel consumption according to these metrics. The results also show that isolating the thrust dynamics within the feedback loop has an important impact in fuel economy. Controllers were also evaluated using the EEP and T-TSFC. These novel metrics are proposed to evaluate the environmental impact during transient-thrust operations. The direct-thrust LQI approach has a more efficient fuel consumption according to these metrics. The results also show that isolating the thrust dynamics within the feedback loop has an important impact in fuel economy.

This study shows the design of an effective direct-thrust control approach that minimizes fuel consumption, ensures stable responses for the full operation range, allows isolating the thrust dynamics when designing the controller and is compatible with classical robustness and performance metrics. Finally, the study shows that a simple controller can reduce the fuel consumption of the turbojet during transient operation in scenarios that approximate realistic operating conditions.

This paper designs an optimal closed-loop supply chain network with an integrated forward and reverse logistics to examine the possibility of remanufacturing end-of-life (EoL) ships.

Explanatory variables are used to estimate the number of EoL ships available in a closed-loop supply chain network. The estimated number of EoL ships is used as an input in the model and then it is solved by a mixed-integer linear programming (MILP) model of the closed-loop supply chain network to minimise the total logistic costs. A discounted payback period formula is developed to calculate the length of time to recoup an investment based on the investment's discounted cash flows. Existing ship wrecking industry clusters in the Western region of India are used as the case study to apply the proposed model.

The MILP model has optimised the total logistics costs of the closed-loop supply network and ascertained the optimal number and location of remanufacturing for building EoL ships. The capital and variable costs required for establishing and operating remanufacturing centres are computed. To remanufacture 30 ships a year, the discounted payback period of this project is estimated to be less than two years.

Ship manufacturing businesses are yet to re-manufacture EoL ships, given high upfront capital expenditure and operational challenges. This study provides management insights into the costs and benefits of EoL ship remanufacturing; thus, informing the decision-makers to make strategic operational decisions.

The design of an optimal close loop supply chain network coupled with a Bayesian network approach and discounted payback period formula for the collection and remanufacturing of EoL ships provides a new integrated perspective to ship manufacturing.