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A flight control design is presented that combines model inversion control with an online adaptive neural network (NN). The NN cancels the error due to approximate inversion. Both linear and nonlinear NNs are described. Lyapunov stability analysis leads to the online NN update laws that guarantee boundedness. The controller takes advantage of any a...
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... generation aircraft may differ radically from their prede- cessors, presenting control designers with interesting challenges and opportunities. Examples include: low-observable and super- maneuverable tailless fighter aircraft like the X-36 in Fig. 1 and [1], aircraft capable of flight in multiple configurations like the tilt-rotor described in Section II-B, and remotely piloted and au- tonomous vehicles unconstrained by human ...
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... The unaugmented XV-15 displays conventional first-order roll rate characteristics where aileron deflections produce a proportional roll rate at low fre- quencies, and acceleration at high frequencies. The dominant time constant is approximately 1 s. The critical bandwidth is de- termined by the 45 phase margin [43]. The effective phase time Fig. 12. Yaw channel response, control surface deflections and TC performance in roll rate doublet of Fig. 11. Fig. 13. HH versus TC in helicopter configuration, 30 Kts at 3000 ft. Given a doublet roll rate command, the performance in roll channel shows no differences. The maximum bank angle is approximately 25 . In HH mode, the heading ...
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... deflections produce a proportional roll rate at low fre- quencies, and acceleration at high frequencies. The dominant time constant is approximately 1 s. The critical bandwidth is de- termined by the 45 phase margin [43]. The effective phase time Fig. 12. Yaw channel response, control surface deflections and TC performance in roll rate doublet of Fig. 11. Fig. 13. HH versus TC in helicopter configuration, 30 Kts at 3000 ft. Given a doublet roll rate command, the performance in roll channel shows no differences. The maximum bank angle is approximately 25 . In HH mode, the heading remains within 0.25 of its trim value, and the aircraft reaches a lateral velocity of 20 Kts. delay is ...
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... produce a proportional roll rate at low fre- quencies, and acceleration at high frequencies. The dominant time constant is approximately 1 s. The critical bandwidth is de- termined by the 45 phase margin [43]. The effective phase time Fig. 12. Yaw channel response, control surface deflections and TC performance in roll rate doublet of Fig. 11. Fig. 13. HH versus TC in helicopter configuration, 30 Kts at 3000 ft. Given a doublet roll rate command, the performance in roll channel shows no differences. The maximum bank angle is approximately 25 . In HH mode, the heading remains within 0.25 of its trim value, and the aircraft reaches a lateral velocity of 20 Kts. delay is negligible ...
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... referred to as Small Amplitude Roll Attitude Changes. It is a requirement for a phase bandwidth of 2 rad/s. If the roll-yaw coupling is small then the time constant of the roll response is approxi- mately the inverse of the phase bandwidth, . So a command filter design with a time constant 0.5 s should result in Level 1 handling qualities. Fig. 14 shows the results of the frequency sweep. Moderate amplitude changes (attitude quickness) requirements and Level 1 requirements for so-called large amplitude roll attitude changes for IMC opera- tions were also achieved ...
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Citations
... 4. The proposed FAC for TUAV attitude control can be applied in both the transition and reversion phases of the transition mode, even though these two phases have unique demands for controller design [11,12]. Relatively, [1] only focused on the conversion phase, while Papachristos et al. [14] researched the reconversion phase, and [40] only addressed full envelope flight control without providing a detailed illustration of the transition mode. The remainder of this study is structured as follows: Section 2 expresses a 6-DOF nonlinear dynamic model of the TUAV. ...
An adaptive recursive sliding mode control (ARSMC) scheme based on a fixed-time composite observer (FTO) is introduced in this paper, to address challenges in tiltrotor unmanned aerial vehicle (TUAV) attitude control. As an electric vertical take-off and landing (eVTOL) aircraft, the unique structure of TUAV leads to significant aerodynamic disturbances, particularly during transition modes, causing flight instability. To enhance control robustness, a fast-converging FTO is designed to estimate the system states, model uncertainties and external disturbances, achieving fixed-time convergence. The FTO comprises a fixed-time state observer (FTSO) based on homogeneous function and a fixed-time disturbance observer (FTDO). To accomplish rapid and accuracy attitude tracking during TUAV flight, a recursive sliding mode control (RSMC) is employed. This method utilizes reconstructed information from the FTO, incorporates an adaptive law to update control gains, and ensures finite-time convergence. And the recursive structure reduces the convergence time while reducing chattering phenomena. Ultimately, simulation results demonstrate the effectiveness of the proposed FTO-ARSMC (FAC) strategy in rapidly and robustly controlling the attitude during the transition mode of TUAV.
... Simplifying the model can make the control design easier, but the model might be inaccurate. Designing control laws based on a nonlinear model is also a challenge, due to complex changes in dynamic characteristics and other problems (Rysdyk R and Calise, 2005). Although the transition process lasts only for a couple of seconds, it is still the most complicated and significant part (Li Z and Xia, 2018). ...
This paper investigates the state-tracking control problem in conversion mode of a tilt-rotor aircraft with a switching modeling method and a smooth interpolation technique. Based on the nonlinear model of the conversion mode, a switched linear model is developed by using the Jacobian linearization method and designing the switching signal based on the mast angle. Furthermore, an ℋ∞ state-tracking control scheme is designed to deal with the conversion mode control issue. Moreover, instead of limiting the amplitude of control inputs, a smooth interpolation method is developed to create bumpless performance. Finally, the XV-15 tilt-rotor aircraft is chosen as a prototype to illustrate the effectiveness of this developed control method.
... Acceptable power distribution and a feasible range of speed are necessary to ensure that the lift of the UAV satisfies the flight needs and that the flying attitude continues to change during the process. Consequently, the transition flight mode is somewhat complicated for VTOL UAVs [23][24][25]. The transition flight corridor of the UAV must be determined to allow the aircraft to fly securely and ensure the safety of the transition flight. ...
The purpose of this paper is to establish a transition strategy for tilt-rotor vertical takeoff and landing (VTOL) unmanned aerial vehicles (UAVs) based on an optimal design method. Firstly, The flyable transition corridor was calculated based on both the UAV’s dynamic equations and its aerodynamic and dynamic characteristics. The dynamic equations of the UAV were organized into state equation forms. The initial and final value constraints of the control and state variables in the transition process were recorded, as were the constraints of the transition process. The transition strategy design problem was transformed into an optimal control problem with constraints, while the Gauss pseudospectral method (GPM) was employed to transform and solve the problem. In addition, performance indicators were designed based on the transition quality requirements for transition time, attitude stability, and control continuity. Sensitivity was analyzed according to different index terms with different dimensions and effects. Finally, the rationality of the transition strategy designed in this paper was verified according to different simulation scenarios.
... 1. First, different from the presented robust, observer-based, H ∞ or adaptive NN approaches in [1,7,26,34,[37][38][39], this paper proposes a novel robust adaptive strategy, which can be exploited to deal with the equivalent disturbances with norm bounds depending not only on system states but also on control inputs. Importantly, the common algebraic loop problems, in existing robust algorithms to handle uncertainties related to inputs, can be avoided. ...
... and (38), and the desired pitch angle is determined by (11), then for any bounded initial states p(t 0 ) and v(t 0 ), the trajectory tracking error will converge to zero, that is, lim t→∞ ∥p(t) − p r (t)∥ = 0, and the involved states are bounded. Remark 5. From the above design and analysis procedure, it can be seen that two linear models with equivalent disturbances are derived to describe the translational dynamics of the FW UAV. ...
This paper aims to tackle the three-dimensional trajectory tracking problems of small fixed-wing unmanned aerial vehicles subject to nonlinearities, uncertainties and wind disturbances via adaptive techniques. The control objective is to efficiently control the thrust and the deflections of control surfaces to ensure the unmanned aerial vehicle arrives at a specified location within a given time frame. However, achieving this goal for small fixed-wing unmanned aerial vehicles can be challenging because the precise dynamic model and several parameters are not accessible, making most existing control strategies unworkable. Motivated by these facts, based on feedback linearization techniques, we derive linear models with equivalent disturbances to describe the translational dynamics without requiring precise aerodynamic force model information. To deal with the dilemmas where the norm bounds of equivalent disturbances depend on control inputs, system states, and unknown disturbances, a novel robust adaptive control strategy is designed for position control. Based on the assumption of two-time separation, the control scheme incorporates two parts, namely, a position controller containing the horizontal-plane and altitude parts and a robust filter-based attitude regulator. Also, to prevent chattering issues, we design a practical and robust adaptive position controller under which the tracking error is ultimately bounded The overall closed-loop stability is theoretically investigated based on the Lyapunov arguments. Hardware-in-loop simulation experiments are performed to testify our developed control scheme.
... Remark 3. Another concern with the given updating rules (22)-(23) is the possibility of parameter drift (in the absence of a persistent excitation) in real applications due to the requirement for a discrete-time implementation of the introduced design. As mentioned previously, different modifications such as the -modification or the -modification techniques (Lungu, 2020;Rysdyk & Calise, 2005) could be incorporated into the above-mentioned updating rules, while, again, the asymptotic stability of the system is reduced to a bounded tracking error. ...
An adaptive neural guidance and control system is proposed in this paper for a generic fixed-wing aerial robot. Unlike most of the existing low-level control systems, which utilize a non-adaptive guidance loop, in this work both the guidance and control loops are trained using an efficient adaptive neural algorithm. A feedforward neural network is employed in each loop to identify uncertain dynamics, while an adaptive disturbance observer allows to compensate for both the external disturbances and estimation error of the neural network. This would lead to a resilient flight control system, and thus, the asymptotic stability of both the guidance and control loops can be theoretically ensured for a generic aerial robot subject to different types of nonparametric internal and external disturbances. Besides, to enhance the learning efficiency, a composite learning method is adopted in which the neural network and the disturbance observer are trained using a composite error function consisting of the tracking error and the estimation error of an introduced adaptive state observer. To the best of the authors' knowledge, this is the first completely adaptive integrated guidance and control system with guaranteed stability under parametric and nonparametric internal and external disturbances. The introduced control system is then applied to a simulation model of an electric aircraft that has been validated on the basis of real data and flight experiments. The obtained results indicate that the proposed approach could be considered a reliable guidance and control system for a generic fixed-wing aerial vehicle in the presence of actuator faults, unmodeled dynamics, external disturbances, and measurement noises.
... where the constant parameter σ in the previous technique is replaced by a term proportional to |e| [62,75]. The boundedness of the NN parameters using e-modification has been presented in [76]. ...
... The pseudocontrol strategy has been employed in different flight control systems [102] such as the attitude control of a tailless fighter aircraft [103,104,105,106], the trajectory tracking control of a helicopter [101], the attitude control of a tilt-rotor aircraft [75], etc. A similar direct adaptive control has been utilized in [107,38] to control the trajectory of a conventional fixed-wing aircraft under structural damages. ...
As the first review in this field, this paper presents an in-depth mathematical view of Intelligent Flight Control Systems (IFCSs), particularly those based on artificial neural networks. The rapid evolution of IFCSs in the last two decades in both the methodological and technical aspects necessitates a comprehensive view of them to better demonstrate the current stage and the crucial remaining steps towards developing a truly intelligent flight management unit. To this end, in this paper, we will provide a detailed mathematical view of Neural Network (NN)-based flight control systems and the challenging problems that still remain. The paper will cover both the model-based and model-free IFCSs. The model-based methods consist of the basic feedback error learning scheme, the pseudocontrol strategy, and the neural backstepping method. Besides, different approaches to analyze the closed-loop stability in IFCSs, their requirements, and their limitations will be discussed in detail. Various supplementary features, which can be integrated with a basic IFCS such as the fault-tolerance capability, the consideration of system constraints, and the combination of NNs with other robust and adaptive elements like disturbance observers, would be covered, as well. On the other hand, concerning model-free flight controllers, both the indirect and direct adaptive control systems including indirect adaptive control using NN-based system identification, the approximate dynamic programming using NN, and the reinforcement learning-based adaptive optimal control will be carefully addressed. Finally, by demonstrating a well-organized view of the current stage in the development of IFCSs, the challenging issues, which are critical to be addressed in the future, are thoroughly identified.
... Furthermore, the introduction of excessive neurons in these NN leads to severe co-adaptation and overfitting. On the other hand, some powerful NN such as cerebellar model articulation controller (CMAC) and ridge polynomial neural network (RPNN) usually have heavy structures, which bring too many hyperparameters to design and make these NN much unreliable in engineering [33]. Different from above NN, PSNN is a kind of highorder neural network and has received considerable attention recently [34,35] due to its ability of realizing faster nonlinear approximation by introducing both sum and multiplication neurons [36,37]. ...
... The input of neural network is designed in (39). Used as controls, classical INDI controller (INDIC) without adaptive compensation and traditional single layer perceptron controller (SHLC) in [33] are built in the simulation. Compared with these classical controllers, the improvement of proposed methods can be effectively verified. ...
... The meaning of all variables in (76) is explained in [33]. All constant parameters of neural network in proposed controller and control groups are given in Table 2. ...
The main goal of this study is to create a robust control system that could guide or replace pilots in the tracking of the commanded velocity and attitude in multimodal maneuver, while complex dynamics and uncertain aerodynamic cross-coupling among control surfaces of compound helicopter are considered. To this end, a Pi-Sigma neural network (PSNN) adaptive controller is proposed based upon the certainty-equivalence (CE) principle, where a novel Lyapunov-based weight self-tuning algorithm augmented with e-modification is designed to realize efficient uncertainty approximation and guarantee robustness of convergence process. Compared with traditional neural networks in control field, stronger generalization ability of PSNN must be balanced against weaker stability, which leads to inevitable parameters perturbation. Therefore, an incremental nonlinear dynamic inversion (INDI) framework is established to decouple original overactuated system and reject parameters perturbation in PSNN. Meanwhile, by incorporating Lagrange multiplier method into allocation, an original incremental allocation method is designed to get globally ideal control input according to time-varying working capability of each surface. In terms of Lyapunov theorem, it is demonstrated that the closed-loop augmented system driven by the proposed control scheme is semi-global uniformly ultimately bounded. Finally, by comparing with existing methods, the simulation validates the effectiveness of proposed control scheme.
... where the constant parameter σ in the previous technique is replaced by a term proportional to |e| [62,75]. The boundedness of the NN parameters using e-modification has been presented in [76]. ...
... The pseudocontrol strategy has been employed in different flight control systems [102] such as the attitude control of a tailless fighter aircraft [103,104,105,106], the trajectory tracking control of a helicopter [101], the attitude control of a tilt-rotor aircraft [75], etc. A similar direct adaptive control has been utilized in [107,38] to control the trajectory of a conventional fixed-wing aircraft under structural damages. ...
As the first review in this field, this paper presents an in-depth mathematical view of Intelligent Flight Control Systems (IFCSs), particularly those based on artificial neural networks. The rapid evolution of IFCSs in the last two decades in both the methodological and technical aspects necessitates a comprehensive view of them to better demonstrate the current stage and the crucial remaining steps towards developing a truly intelligent flight management unit. To this end, in this paper, we will provide a detailed mathematical view of Neural Network (NN)-based flight control systems and the challenging problems that still remain. The paper will cover both the model-based and model-free IFCSs. The model-based methods consist of the basic feedback error learning scheme, the pseudocontrol strategy, and the neural backstepping method. Besides, different approaches to analyze the closed-loop stability in IFCSs, their requirements, and their limitations will be discussed in detail. Various supplementary features, which can be integrated with a basic IFCS such as the fault-tolerance capability, the consideration of system constraints, and the combination of NNs with other robust and adaptive elements like disturbance observers, would be covered, as well. On the other hand, concerning model-free flight controllers, both the indirect and direct adaptive control systems including indirect adaptive control using NN-based system identification, the approximate dynamic programming using NN, and the reinforcement learning-based adaptive optimal control will be carefully addressed. Finally, by demonstrating a well-organized view of the current stage in the development of IFCSs, the challenging issues, which are critical to be addressed in the future, are thoroughly identified. As a result, this paper can be considered as a comprehensive road map for all researchers interested in the design and development of intelligent control systems, particularly in the field of aerospace applications.
... An improved back propagation (BP) neural network PID control algorithm was proposed for the tiltrotor flight control system in conversion mode [10]. By combining the model inversion control with an adaptive neural network, a flight control design was presented in [11]. In [12], the passive and active controls on aerodynamic interactions of a tiltrotor aircraft were investigated in hovering flight. ...
The tiltrotor aircraft consists of three primary flight modes, which are helicopter flight mode in low forward speed flight, airplane flight mode in high forward speed flight and conversion flight mode. This paper presents an active disturbance rejection controller for tiltrotor aircraft conversion flight. First, a tiltrotor aircraft flight dynamics model is developed and verified. Then, conversion flight control laws, designed via the active disturbance rejection control (ADRC) and sliding mode control (SMC) techniques, are proposed for the tiltrotor aircraft with model uncertainties and external disturbance, which are estimated with an extended state observer. Finally, the simulation of automatic conversion flight is carried out, which shows the effectiveness of the developed controller.
