Dynamic Inversion Control Research Articles

Vision-Based Nonlinear Incremental Control for a Morphing Wing With Mechanical Imperfections

Morphing structures have acquired much attention in the aerospace community because they enable an aircraft to actively adapt its shape during flight, leading to fewer emissions and fuel consumption. Researchers have designed, manufactured, and tested a morphing wing named SmartX-Alpha, which can actively alleviate loads while achieving the optimal lift distribution. However, the widely existing mechanical imperfections can degrade the performance of the morphing wing and even lead to instabilities. To tackle these issues, this article proposes a vision-based adaptive control approach to actively compensate for mechanical imperfections. In this approach, an incremental model is constructed online to identify the system dynamics using servo commands and vision measurements, and then, nonlinear dynamic inversion control is applied based on the identified model. This data-driven control approach with visual feedback has been validated by real-world experiments on the SmartX-Alpha. The results demonstrate that the vision-based system combined with the proposed control methodology can actively compensate for mechanical imperfections with minimal adjustments to the actual system design. Compared to a controller that only uses a feedforward input-output mapping, this proposed approach improves the system performance and decreases the tracking errors by more than 62% despite disturbances. The results collectively demonstrate the effectiveness of the proposed control system, which sets a foundation for realizing morphing in next-generation aircraft.

Fuzzy Neural Network Dynamic Inverse Control Strategy for Quadrotor UAV Based on Atmospheric Turbulence

Quadrotor UAV is vulnerable to external interference, which affects search and rescue. In this paper, a fuzzy neural network dynamic inverse controller (FNN-DIC) is designed to eliminate the instability of the attitude angle caused by atmospheric turbulence. Considering the complexity of atmospheric turbulence, the component model of atmospheric turbulence is obtained firstly based on the Dryden model, using Gaussian white noise as a random input signal and a designed shaping filter. Combined with the Newton-Euler equation, a nonlinear dynamic model for the quadrotor UAV with atmospheric disturbance is established. While the traditional nonlinear dynamic inverse cancels the nonlinearity of the controlled object, it relies on precise mathematical models. The fuzzy neural network can adaptively compensate for the inaccurate part of the model and the inverse error of the model caused by the external disturbance, and the stability of the control system is strictly proved by using the Lyapunov function. The experiments are carried out on the simulation platform, and the results show that the FNN method can ensure that the quadrotor UAV can still fly smoothly against strong disturbances, and that robustness of the system is significantly improved.

Hybrid Adaptive Dynamic Inverse Compensation for Hypersonic Vehicles with Inertia Uncertainty and Disturbance

This paper studies an intelligent hybrid compensation scheme for the uncertain parameter and disturbance of hypersonic flight vehicles (HFV). For the longitudinal model of HFV with modeling errors, a nominal nonlinear dynamic inverse (NDI) controller ensures that the system output can accurately track the reference command. In the presence of rotational inertia uncertainty, a multi-learning law adaptive NDI controller is proposed to directly compensate for its impact on tracking performance, making the system robust to the uncertainty and reducing high maneuvering attitude angles and velocities vibration. Then, an improved adaptive NDI controller with a sliding mode disturbance observer is designed to actively compensate for the elastic mode disturbance, and continuously ensure the system’s anti-disturbance flight quality. Ultimately, this active–passive hybrid control scheme compensates for both high maneuvering inertia uncertainty and global disturbance. The Lyapunov functions prove the system’s stability, and the semi-physical simulation platform verifies the effectiveness of the method.

A semi-active control method for magnetorheological dampers to suppress the milling vibration of thin-walled workpieces

Thin-walled workpieces are widely used in the aerospace field, and milling is the main processing method of such components. However, thin-walled workpieces are prone to vibrate during milling because of their weak stiffness, and the vibrations significantly affect the processing quality. Therefore, it is important to develop a novel milling vibration suppression method for thin-walled parts. The present work focused on the milling vibration control of a thin-walled workpiece using a semi-active magnetorheological (MR) damper. The forward dynamic model and inverse control model of the MR damper were first established, and its actuating position on the workpiece was then determined by modal analysis. The dynamic model of the MR damper at this position was built based on the proposed dynamic parameter acquisition method and used in the following semi-active control algorithm design. The effectiveness of this algorithm was verified by MATLAB/Simulink simulations. Finally, six groups of milling experiments were carried out using the non-damper, the rigid rod, the damper applied with 1-, 2-, and 3-A currents, and the semi-active control damper to validate the practical effect of the proposed algorithm. It was found that the designed semi-active control MR damper outperformed the constant-current control dampers or the rigid rod. In comparison to the non-damper experiment, the root mean square (RMS) values of milling forces in x- and y-directions decreased by 45.12% and 48.58%, respectively, and surface roughness decreased by 40.22%.

An Adaptive Control Framework for the Autonomous Aerobatic Maneuvers of Fixed-Wing Unmanned Aerial Vehicle

This article proposes an adaptive flight framework that integrates a discrete-time incremental nonlinear dynamic inversion controller and a neural network (NN)-based observer for maneuvering flight. The framework is built on the feedback-inversion scheme in which the adaptive neural network augments a discrete-time disturbance observer in the loop. The effects of the modeling uncertainties and the exogenous perturbations are both taken into consideration and are alleviated by the observer. By utilizing the Lyapunov synthesis method, the updating rule of the NN’s weights is introduced, which guarantees the system’s stability with enhanced tracking performance. The efficiency of the proposed scheme is presented through numerical verification of a 6-DOF fixed-wing fighter performing several aggressive flight maneuvers. Extensive simulation results illustrate that this versatile controller is more practical for aerobatic flights compared with the discontinuous sliding mode (DSM) and the nonlinear dynamic inversion (NDI) methods. Given well-generated maneuver commands, the aircraft can accurately track the aggressive reference in the presence of modeling perturbations such as changes in aerodynamic coefficient, inertial parameters, and wind gusts.

A cascaded nonlinear fault-tolerant control for fixed-wing aircraft with wing asymmetric damage

This paper investigates the flight dynamics of the aircraft with wing asymmetric damage and the fault-tolerant control problem to improve the stability and flight quality of damaged aircraft. A high-fidelity wing asymmetric damaged aircraft nonlinear model is developed, as well as the impact of wing asymmetric damage on the physical and aerodynamic properties of the aircraft is also analyzed. The trim strategies for damaged aircraft are investigated to achieve a rapid estimation of trim states after damage occurs. This paper presents a robust cascaded nonlinear fault-tolerant control framework that integrates the incremental nonlinear dynamic inversion control with improved piecewise-constant-based nonlinear L1 adaptive control for the stability control to enhance the stability and tracking performance of the damaged aircraft. Theoretical analysis proves that the presented fault-control structure is robust to disturbances and can decouple rapidity and robustness while guaranteeing steady-state and transient performance. Finally, the hardware-in-the-loop flight control experiment platform is developed to validate the cascaded nonlinear fault-tolerant controller. In the experiment, the proposed controller is verified under wing asymmetric damage and compared with existing methods. Experimental results show that the proposed fault-tolerant control is able to overcome wing asymmetric damage and significantly improve the tracking performance of the damaged aircraft even with 27.2% of the severe damage to the left-wing.

Deep adaptive control with online identification for industrial robots

Derivation of control equations from data is a critical problem in numerous scientific and engineering fields. The inverse dynamic control of robot manipulators in the field of industrial robot research is a key example. Traditionally, researchers needed to obtain the robot dynamic model through physical modeling methods before developing controllers. However, the robot dynamic model and suitable control methods are often elusive and difficult to tune, particularly when dealing with real dynamical systems. In this paper, we combine an enhanced online sparse Bayesian learning (OSBL) algorithm and a model reference adaptive control method to obtain a data-driven modeling and control strategy from data containing noise; this strategy can be applied to dynamical systems. In particular, we use a sparse Bayesian approach, relying only on some prior knowledge of its physics, to extract an accurate mechanistic model from the measured data. Unmodeled parameters are further identified from the modeling error through a deep neural network (DNN). By combining the identification model with a model reference adaptive control approach, a general deep adaptive control (DAC) method is obtained, which can tolerate unmodeled dynamics. The adaptive update law is derived from Lyapunov’s stability criterion, which guarantees the asymptotic stability of the system. Finally, the Enhanced OSBL identification method and DAC scheme are applied on a six-degree-of-freedom industrial robot, and the effectiveness of the proposed method is verified.

Sliding Mode Disturbance Observer-Based Adaptive Dynamic Inversion Fault-Tolerant Control for Fixed-Wing UAV

Unmanned aerial vehicles (UAVs) have been widely applied over the past decades, especially in the military field. Due to the unpredictability of the flight environment and failures, higher requirements are placed on the design of the control system of the fixed-wing UAV. In this study, a sliding mode disturbance observer-based (SMDO) adaptive dynamic inversion fault-tolerant controller was designed, which includes an outer-loop sliding mode observer-based disturbance suppression dynamic inversion controller and an inner-loop real-time aerodynamic identification-based adaptive fault-tolerant dynamic inversion controller. The sliding mode disturbance observer in the outer-loop controller was designed based on the second-order super-twisting algorithm to alleviate chattering. The aerodynamic identification in the inner-loop controller adopts the recursive least squares algorithm to update the aerodynamic model of the UAV online, thereby realizing the fault-tolerant control for the control surface damage. The effectiveness of the proposed SMDO enhanced adaptive fault-tolerant control method was validated by mathematical simulation.

Improved Neural Adaptive Control for Nonlinear Oscillatory Dynamic of Flapping Wings

Modeling uncertainties and oscillatory dynamics are control challenges for flapping vehicles. Flapping-wing aerial vehicles are nonlinear time-varying oscillatory systems with flexible multibody dynamics. As such, accurate modeling of these complex systems considering unsteady aerodynamics is a formidable task. Though adaptive controllers can work well with uncertain approximate models, inherent oscillation of flapping systems can degrade their control performance and create undesired control actuations. This paper addresses improvements of neural adaptive dynamic inversion controller toward efficient performance for flapping flight via effective utility of actuator capacity. In this respect, first an assumptive model of a bird-mimetic flapping-wing is considered for simulation and analysis. It is shown that the usage of oscillatory feedback data produces additional error in trajectory tracking and unnecessary oscillatory control actuations. Subsequently, the control loop is modified by adding an adaptive notch filter for real-time estimation, tracking, and removal of dominant oscillatory modes from the feedback data and consequently from the controller output. As a result, the required control effort is effectively reduced and the controller performance is significantly improved. Simulations are provided to demonstrate the efficiency of the proposed scheme in presence of noise, variation of system frequency, disturbances, as well as delay in the control loop.

Design of Thrust Vectoring Vertical/Short Takeoff and Landing Aircraft Stability Augmentation Controller Based on L1 Adaptive Control Law

Aiming at the conversion process of thrust vectoring vertical/short takeoff and landing (V/STOL) aircraft with a symmetrical structure in the transition stage of takeoff and landing, there is a problem with the coupling and redundancy of the control quantities. To solve this problem, a corresponding inner loop stabilization controller and control distribution strategy are designed. In this paper, a dynamic system model and a dynamic model are established. Based on the outer loop adopting the conventional nonlinear dynamic inverse control, an L1 adaptive controller is designed based on the model as the inner loop stabilization control to compensate the mismatch and uncertainty in the system. The key feature of the L1 adaptive control architecture is ensuring robustness in the presence of fast adaptation, so as to achieve a unified performance boundary in transient and steady-state operations, thus eliminating the need for adaptive rate gain scheduling. The control performance and robustness of the controller are verified by inner loop simulation and the shooting Monte Carlo approach. The simulation results show that the controller can still track the reference input well and has good robustness when there is a large parameter perturbation.

Robust Trajectory-Tracking for a Bi-Copter Drone Using INDI: A Gain Tuning Multi-Objective Approach

This paper presents an optimized robust trajectory control system for an autonomous tiltrotor bi-copter based on an incremental nonlinear dynamic inversion (INDI) strategy combined with a set of PID/PD controllers. The methodology includes a lower level, fast attitude control action using an incremental nonlinear dynamic inversion (INDI) strategy, which is driven by a higher level, slow trajectory control action that uses nonlinear dynamic inversion (NDI). The nonlinear dynamic model of the drone is derived, and the basis of the motion and the design of the attitude and position stabilizing controllers are discussed. To develop and test the suggested controller, a circle-shaped flight profile is simulated. The linear control providing inputs to the NDI and INDI controllers is tuned via a novel multi-objective optimization auto-tuning method using the non-dominated sorting genetic algorithm II (NSGA-II). The tracking and disturbance rejection optimization is achieved via the use of the integral of time multiplied by the absolute error (ITAE) and the integral of the square of the error (ISE) objective functions, which are optimized concurrently. The simulation results reveal that the proposed control design outperforms the traditional dynamic inversion controller design and demonstrate that the developed INDI + PID/PD controller possesses exceptional accuracy and performance, enabling the tiltrotor bi-copter to track the given trajectory. Furthermore, the paper shows that the proposed controller produces 40% lower overshoot and settling time as measured with respect to previous backstepping controllers reported in the literature. The robustness of the controller is validated through diverse tests where the aircraft is subjected to external (wind gust) disturbances.

L1 Adaptive Structure-Based Nonlinear Dynamic Inversion Control for Aircraft with Center of Gravity Variations

L1 Adaptive Structure-Based Nonlinear Dynamic Inversion Control for Aircraft with Center of Gravity Variations

A hybrid INDI control for ensuring flying qualities in failures of Xcg measurement subsystem

Modern version of fighter aircraft has a wide range of longitudinal center-of-gravity (Xcg) travel due to wide-range fuel tanks layout, various weapons, many payload stations, and so on. The wide Xcg travel degrades flying qualities of a closed-loop control system; moreover, a catastrophic accident can be caused by loss of stability due to the wide Xcg travel in severe cases. For this reason, the inner-loop control law generally employs additional feedback variables such as weight and Xcg position to guarantee the flying qualities and satisfy stability requirements. Nevertheless, this design approach can seriously deteriorate the flight safety and stability of the aircraft in the event of fuel mismanagement and Xcg measurement subsystem failures. Therefore, additional design technique should be considered to ensure the flight safety in response to the situation of the failure conditions. This paper proposes two types of a hybrid Incremental Nonlinear Dynamic Inversion control to guarantee the minimum flight safety for return to base in case of fuel mismanagement and Xcg measurement subsystem failures. The frequency-domain linear analysis and time-domain simulations were performed based on a supersonic trainer mathematical model to evaluate the performances of the proposed control methods. The evaluation results confirm that the proposed control method satisfies the level of the required flying qualities and ensure flight safety even in the event of the subsystem failures for the Xcg measurement.

Output tracking control of non‐minimum phase systems using on‐line output redefinition approach based on barrier function method

This paper considers the output tracking problem of non-minimum phase non-linear systems using the inverse dynamics control which is impossible in conventional methods due to unstable zero dynamics. The proposed method is based on the output redefinition approach defined as a parametric function. The tracking error is decomposed into two terms. The first one named redefined output tracking error is the difference between the redefined output and the reference signal, and the second one is the distance between the redefined output and the system output. For the first term, due to minimum phase property of the system with the redefined output, an inverse dynamics control is used to guarantee the convergence of the redefined output tracking error to zero. To minimize the second term, the parameters of the redefined output are set by solving an optimization problem whose constraints include minimum phase conditions. To solve the optimization problem which satisfies all constraints, the barrier function method as an interior point method is adopted. The optimization parameters are set using steepest descent algorithm. Due to on-line updating the redefined output parameters, the related zero dynamics is time varying. Therefore, sufficient conditions for exponential stability of the resulting time-varying zero dynamics are obtained.

Adaptive preassigned-time controller design for a hypersonic vehicle with improved performance and uncertainties

The tracking control problem of a hypersonic vehicle (HSV) under tracking error constraints and uncertainties is investigated. To achieve tracking error converges to its preselected constrained boundary in preassigned time, a performance function is proposed. The preassigned time can be determined by requirement of HSV in prior. Then an adaptive preassigned-time prescribed performance (PTPP) dynamic inversion controller and an adaptive PTPP backstepping controller are proposed for velocity and altitude subsystems, respectively. At the level of control design for altitude subsystem, a dynamic gain fixed-time filter (DGFTF) is proposed for solving ‘explosion of complexity’ problem. It can ensure that the filter error converges to an arbitrarily small vicinity of zero in fixed time. What is more, it is proved that the proposed PTPP controller can assure predefined performance of velocity and altitude tracking errors in a given preselected time. Finally, compared simulation is performed to validate the effectiveness of the proposed control strategy.

Attitude adaptive control of satellites using double-gimbal magnetically suspended control moment gyros

The paper deals with the control of the satellite attitude by means of double-gimbal magnetically suspended control moment gyros (DGMSCMGs). In its first part, the paper presents the dynamics of the DGMSCMGs and the dynamics of the satellite equipped with a DGMSCMG actuator that creates a total satellite applied gyroscopic torque which is generated by the angular rate of the satellite and the angular rates applied to the gyroscopic gimbals. Then, an adaptive satellite attitude control system is designed with actuator and angular rate sensor (both DGMSCMG type), based on the use of the dynamic inversion control technique and a neural network; the latter models an adaptive component of the control law to compensate the negative influence of the dynamic inversion error. The attitude controller mainly consists of a dynamic compensator, a neural network, and a linear observer (for the estimation of the neural network training vector). The novel attitude control architecture is validated in Matlab/Simulink; the analysis of both theoretical concepts and obtained results prove the cancel of the convergence errors, an excellent angular rate precision, as well as robustness to external disturbances.

SDC-based dynamic inversion controller for a fixed-wing aircraft

This work presents dynamic model inversion controller which uses state dependent coefficient matrices, and is applied for a fixed-wing aircraft attitude and velocity control problem. Numerical validation of the proposed controller is done using a Cessna 172 aircraft simulation model for several scenarios of atmospheric disturbances.

Three-dimensional cooperative guidance law for multiple missile based on sliding mode control

Based on the missile-target three-dimensional relative motion model, a multi-missile coordinated three-dimensional guidance law with attack time constraints is designed for attacking stationary targets. The sliding mode variable structure control is adopted in the pitch channel, and the dynamic inverse control is adopted in the yaw channel. By maneuvering in the yaw channel, adjusting the missile's attack time and comparing with the results of using augmented proportional guidance, the simulation results show that the algorithm designed in this article can achieve coordinated attack and high hitting accuracy, which verifies the correctness and superiority of the algorithm.

Arttirimli doğrusal olmayan dinamik çevirmeye dayali bir quadrotor'un hata toleransli kontrolü

The multirotor unmanned aerial vehicles (UAVs) have rapidly attracted interest of the researchers since they play a unique role in a variety of areas including the military, agriculture, rescue, and mining. Actuator fault or failure is inevitable during multi-rotor’s operations, which can endanger humans on the ground in addition to costly damage to the system itself. Therefore, this paper introduces a nonlinear controller algorithm for fault-tolerant control of a quadcopter with partial loss of actuator effectiveness. The introduced controller includes a cascade structure of the fast inner-loop dynamics and slow outer-loop dynamics. In the inner-loop part of the controller, an incremental nonlinear dynamic inversion controller is applied and a modified PID control algorithm is used in the outer-loop of the controller. Simulation results for different fault scenarios demonstrate that the proposed fault-tolerant controller approach can quickly adapt itself to the abrupt change due to the motor faults and tracks the desired inputs satisfactorily.

Detection and Analysis of Bionic Motion Pose of Single Leg and Hip Joint Based on Random Process.

Based on the spectral representation method of random function and combined with memoryless nonlinear translation theory, this paper analyzes the transformation relationship between potential Gaussian random process and non-Gaussian random process, and successfully generates a stationary non-Gaussian random process that conforms to the target non-Gaussian random process. For the non-stationary non-Gaussian random process simulation, on the basis of the stationary Gaussian random process, the intensity non-stationary uniform modulation model is used to modulate it, and combined with the nonlinear translation theory, the non-stationary non-Gaussian random process conforming to the target non-Gaussian random process is obtained. Aiming at the single-leg bouncing model based on the flexible rotary hip joint, the stability of its bouncing motion under passive motion is studied, and the influence of the flexible hip rotary joint on the motion stability is analyzed by comparing the single-leg bouncing motion characteristics of the free rotary hip joint. Based on the inverse dynamic control of the air phase, the fixed point distribution of the single-leg bounce of the flexible rotary hip joint was improved, and the function of the flexible rotary hip joint in the energy conversion of the bouncing motion was studied by establishing the energy consumption evaluation function. The kinematic performance verification, dynamic performance verification, dynamic parameter identification verification, and modal experiment simulation analysis were carried out for the built experimental platform, and the comparison and analysis with its theoretical model were carried out. The results show that the theoretical motion trajectory of the test mobile platform is basically consistent with the actual motion trajectory in the X and Y directions, and there is a small error in the Z-axis direction, and the error is within an acceptable range, indicating that the experimental platform system can be used to simulate the human hip joint. There is a large error between the theoretical value of the driving torque calculated by the theoretical value of the dynamic parameters and the measured value, and the dynamic theoretical model cannot accurately predict the driving torque. The predicted value of the driving torque calculated by using the identification value of the dynamic parameters is in good agreement with the measured torque, and its confidence is increased by 10–16%, indicating that the dynamic parameter identification method in this paper has a high degree of confidence.