Nonlinear Adaptive Control of Tiltrotor Aircraft Using Neural Networks

Rysdyk, Rolf; Calise, Anthony J.; Chen, Robert T. N.

doi:10.4271/975613

Cited by 13 publications

(6 citation statements)

References 5 publications

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“…In the same years, the application of adaptive control techniques to flight control of tiltrotor aircraft was investigated (e.g. neural network-based feedback linearization, Rysdyc and Calise [5,6]); this is an active research field [7], in view of the innumerable promising applications of vertical take-off and landingcapable high-performance unmanned vehicles. Mueller et al [8] investigated the application of a simple single-input single-output control, compared to more sophisticated linear quadratic Gaussian and GPC control strategies, to the multibody model of a semi span tiltrotor configuration.…”

Section: Introductionmentioning

confidence: 99%

Multibody simulation of a generalized predictive controller for tiltrotor active aeroelastic control

Mattaboni

Masarati

Mantegazza

2011

Proceedings of the Institution of Mechanical Engineers, Part G:

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A generalized predictive control has been developed for the control of the aeroelasticity and structural dynamics of a tiltrotor. Adaptivity is the key feature of the proposed technique. It gives the controller the capability to autonomously follow the variations of the system. A comprehensive simulation system has been adopted to design and test the adaptive regulator under realistic conditions. The aeroservoelastic tiltrotor has been modelled using a multibody approach, considering the structural dynamics, the aerodynamics, the blade pitch control system kinematics, and actuator and sensor dynamics. Numerical simulations illustrate the capability of the adaptive controller to reduce wing vibrations and loads while significantly extending the flutter-free flight envelope. This controller shows satisfactory performances within the whole flight envelope as it is able to properly work in different rotor trim conditions and varying structural properties in the presence of external disturbances and measurement noise.

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Section: Introductionmentioning

confidence: 99%

Multibody simulation of a generalized predictive controller for tiltrotor active aeroelastic control

Mattaboni

Masarati

Mantegazza

2011

Proceedings of the Institution of Mechanical Engineers, Part G:

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“…Various control techniques were introduced in order to overcome these difficulties. One of the successful approaches is adaptive non-linear control techniques using feedback linearization [5][6][7][8][9]. The main procedure of the feedback linearization is to map a non-linear plant to a linear plant through a coordinate transformation so that a linear control method can be used to control the feedback linearized plant.…”

Section: Introductionmentioning

confidence: 99%

Trajectory tracking controller design using neural networks for a tiltrotor unmanned aerial vehicle

Kim

2010

Proceedings of the Institution of Mechanical Engineers, Part G:

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This article presents a design of trajectory tracking controller for a tiltrotor unmanned aerial vehicle (UAV). The objective of this design is to control the tiltrotor aircraft with one fixed controller architecture for all flight modes, viz. helicopter mode, conversion mode, and aeroplane mode. The controller is implemented using two time-scale separations, and thus comprises an inner loop for attitude control and an outer loop for trajectory control. The dynamic model inversion (DMI) technique is applied to both the control loops such that the inner-loop and the outer-loop DMIs are driven using the angular equations of motion and the translational equations of motion, respectively. In addition, online adaptive neural networks are employed to compensate for the model inversion errors of the inner loop and the outer loop due to the lack of complete knowledge of tiltrotor aircraft dynamics. The pseudo-control hedging algorithm is adopted to reduce the instability problem caused by the time-delay of control loops, saturation, and rate limit of actuators. The trajectory tracking controller is evaluated using a sophisticated six-degree-of-freedom non-linear simulation programme with an approach and landing scenario including all flight modes of a smart UAV, a tiltrotor UAV.

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“…With a chosen equal-actuation scheme (25) or proportional actuation scheme (26) for a given application, the control task is to design a single feedback control signal v 0 (t), without knowing which of the m actuators u i (t), i = 1, 2, . .…”

Section: Has the Form (9): V(t) = K(t)x(t) + κ(T)r(t) + θ(T)mentioning

confidence: 99%

“…Fault detection and diagnosis methods [12][13][14][15][16][17][18][19][20][21] have also been proposed. Reconfigurable flight control designs using neural networks [22][23][24][25][26] have been developed for aircraft systems with failures. Results on control of systems with failures also include those using: fault tolerant control designs [27][28][29][30] ; identification of multiplicative faults based on parameter estimation techniques [31][32][33][34] ; function approximations for control and adaptive law design 35 ; residual generation techniques for fault detection and diagnosis 21,[36][37][38][39][40][41] ; and other design and analysis techniques.…”

Section: Introductionmentioning

confidence: 99%

Direct Adaptive Control of Systems with Actuator Failures: State of the Art and Continuing Challenges

Tao

Joshi

2008

AIAA Guidance, Navigation and Control Conference and Exhibit

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In this paper, the problem of controlling systems with failures and faults is introduced, and an overview of recent work on direct adaptive control for compensation of uncertain actuator failures is presented. Actuator failures may be characterized by some unknown system inputs being stuck at some unknown (fixed or varying) values at unknown time instants, that cannot be influenced by the control signals. The key task of adaptive compensation is to design the control signals in such a manner that the remaining actuators can automatically and seamlessly take over for the failed ones, and achieve desired stability and asymptotic tracking. A certain degree of redundancy is necessary to accomplish failure compensation. The objective of adaptive control design is to effectively use the available actuation redundancy to handle failures without the knowledge of the failure patterns, parameters, and time of occurrence. This is a challenging problem because failures introduce large uncertainties in the dynamic structure of the system, in addition to parametric uncertainties and unknown disturbances. The paper addresses some theoretical issues in adaptive actuator failure compensation: actuator failure modeling, redundant actuation requirements, plant-model matching, error system dynamics, adaptation laws, and stability, tracking, and performance analysis. Adaptive control designs can be shown to effectively handle uncertain actuator failures without explicit failure detection. Some open technical challenges and research problems in this important research area are discussed.

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Nonlinear Adaptive Control of Tiltrotor Aircraft Using Neural Networks

Cited by 13 publications

References 5 publications

Multibody simulation of a generalized predictive controller for tiltrotor active aeroelastic control

Multibody simulation of a generalized predictive controller for tiltrotor active aeroelastic control

Trajectory tracking controller design using neural networks for a tiltrotor unmanned aerial vehicle

Direct Adaptive Control of Systems with Actuator Failures: State of the Art and Continuing Challenges

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