Dynamic Inversion Research Articles

Dynamic Inversion Method of Calculating Large-Scale Urban Building Height Based on Cooperative Satellite Laser Altimetry and Multi-Source Optical Remote Sensing

Building height is a crucial indicator when studying urban environments and human activities, necessitating accurate, large-scale, and fine-resolution calculations. However, mainstream machine learning-based methods for inferring building heights face numerous challenges, including limited sample data and slow update frequencies. Alternatively, satellite laser altimetry technology offers a reliable means of calculating building heights with high precision. Here, we initially calculated building heights along satellite orbits based on building-rooftop contour vector datasets and ICESat-2 ATL03 photon data from 2019 to 2022. By integrating multi-source passive remote sensing observation data, we used the inferred building height results as reference data to train a random forest model, regressing building heights at a 10 m scale. Compared with ground-measured heights, building height samples constructed from ICESat-2 photon data outperformed methods that indirectly infer building heights using total building floor number. Moreover, the simulated building heights strongly correlated with actual observations at a single-city scale. Finally, using several years of inferred results, we analyzed building height changes in Tianjin from 2019 to 2022. Combined with the random forest model, the proposed model enables large-scale, high-precision inference of building heights with frequent updates, which has significant implications for global dynamic observation of urban three-dimensional features.

Commonalities between robust hybrid incremental nonlinear dynamic inversion and proportional-integral-derivative flight control law design

Incremental Nonlinear Dynamic Inversion (INDI) has received substantial interest in the recent years as a nonlinear flight control law design methodology that features inherent robustness against bare airframe aerodynamic variations. However, systematic studies into the robust design benefits of INDI-based control over the classical divide-and-conquer philosophy have been scarce. To bridge this gap, this paper compares the setup of hybrid INDI with a standard industry benchmark that is based on two-degree-of-freedom gain-scheduled proportional-integral-derivative control. This is done on an architectural basis and in terms of achievable robust stability and performance levels with respect to a common set of design requirements. To this end, a non-smooth, multi-objective H∞-synthesis algorithm is used that incorporates mixed parametric and dynamic uncertainties in the design objective and constraints. It is shown that close similarities exist between hybrid INDI design and gain-scheduled PID control, which leads to virtually equivalent robustness and performance outcomes in both linear time-invariant and linear time-varying contexts. It is therefore concluded that the main benefit of the hybrid INDI does not lie in improved robustness properties per se, but in the opportunity to perform modular robust design in an implicit model-following context. Specifically, this implies that the areas of flying qualities, robustness, and nonlinear implementation are directly visible and accessible in the control law structure.

Feedback Control for Directional Rendezvous Using Constant-Magnitude Low Thrust

Feedback control for close-range orbital rendezvous, which permits choosing the direction of the final approach, is beneficial for numerous space missions. However, developing such a feedback controller for satellites operating with constant-magnitude low thrust without introducing chattering is nontrivial due to the underactuated nature of the dynamics. This paper develops a feedback control law for rendezvous with a target on a nearly circular orbit, assuming that the chaser satellite utilizes constant-magnitude low thrust. Based on the Clohessy–Wiltshire (CW) model, a sliding surface is chosen on which the states approach the origin. A feedback law driving given initial states to the sliding surface is developed first. Close to the origin, a bang-bang type controller is then employed, which drives the state errors to zero in finite time while allowing to choose the final approach direction along the R-, V-, or H-bar. Solutions to the state trajectories are obtained in closed form, except for the case of the R-bar approach, which is proven to be finite-time stable. It is shown that the higher-order dynamics neglected in the CW equations can be handled through dynamic inversion combined with a hopping maneuver.

Iterative flexibility compensation based high precision trajectory tracking for industrial manipulators

High precision industrial manipulators are widely demanded in precision machining and advanced manufacturing. To realize high precision trajectory tracking in task space, feedback plus model based feedforward control strategies are widely used in modern industrial manipulators. Furthermore, the accuracy of feedforward model determines the accuracy of trajectory tacking to a great extent, especially for elastic manipulators. In traditional feedforward controllers, the flexibilities out of the rotational plane are often neglected and thus the end-effector tracking performance can be degraded a lot. Therefore, an iterative flexibility compensation based feedforward controller is proposed to improve the end-effector trajectory tracking accuracy of industrial manipulators in this paper. First, the flexible deformations in all directions are described accurately by using the extended flexible joint model (EFJM) for industrial manipulators. Second, a feedforward controller is designed by using the dynamic inversion of EFJM. In the feedforward controller, an iterative flexibility compensation scheme is designed to transform the reference Cartesian trajectory into joint space and thus the limitation of the nonminimum phase characteristics of EFJM can be avoided. Moreover, the flexible deformations are predicted and compensated based on EFJM. After that, the feedforward torque is derived in joint space by using the efficient feedforward torque computation algorithm for EFJM. Simulation and experimental results demonstrate the effectiveness of the proposed method.

Efficient Convolution-Based Representation of Smooth Reference Trajectories for Urban Air Mobility

Accurate trajectory-following control using dynamic inversion controllers requires suitable parameterizations of reference trajectories, such as energy-minimal flight paths generated with optimal control methods. These trajectory representations must enable efficient onboard storage and real-time evaluation while ensuring sufficient accuracy and smoothness to provide the required feedforward commands. We explore trajectory parameterizations based on the convolution of a piecewise low-order polynomial with an infinitely smooth kernel. These combine efficient storage with unlimited differentiability and allow for simple and efficient evaluation using numerical quadrature. To construct such parameterizations from given trajectories, we formulate quadratic programs that account for additional, mission-specific constraints. A numerical example demonstrates the parameterization of an approach and vertical landing trajectory based on optimal control results; we show that the convolution-based approach yields competitive results compared to a piecewise polynomial parameterization, with significant storage savings.

Aerodynamic Feedforward-Feedback Architecture for Tailsitter Control in Hybrid Flight Regimes

This paper presents a guidance-control design methodology for the autonomous maneuvering of tailsitter transitioning unmanned aerial systems (t-UASs) in hybrid flight regimes (i.e., the dynamics between VTOL and fixed-wing regimes). The tailsitter guidance-control architecture consists of a trajectory planner, an outer-loop position controller, an inner-loop attitude controller, and a control allocator. The trajectory planner uses a simplified tailsitter model, with aerodynamic and wake effect considerations, to generate a set of transition trajectories with associated aerodynamic force estimates based on an optimization metric specified by a human operator (minimum time transition). The outer-loop controller then uses the aerodynamic force estimate computed by the trajectory planner as a feedforward signal alongside feedback linearization of the outer-loop dynamics for 6DOF position control. The inner-loop attitude controller is a standard nonlinear dynamic inversion control law that generates the desired pitch, roll, and yaw moments, which are then converted to the appropriate rotor speeds by the control allocator. Analytical conditions for robust stability are derived for the outer-loop position controller to guarantee performance in the presence of uncertainty in the feedforward aerodynamic force compensation. Finally, both tracking performance and stability of the control architecture are evaluated on a high-fidelity flight dynamics simulation of a quadrotor biplane tailsitter for flight missions that demand high maneuverability in transition between flight modes.

Research on an Ice Tolerance Control Method for Large Aircraft Based on Adaptive Dynamic Inversion

Considering the effect of icing on aircraft control performance, this paper proposes an adaptive dynamic inverse ice tolerance control method based on piecewise constant. A control allocation algorithm is introduced to compensate for the change of control surface performance caused by icing. This method can achieve satisfactory disturbance estimation accuracy under a given sampling time, and thus ensure a closed-loop system error within an acceptable range. The proposed design method is applied to the design of a flight control law for a transport aircraft, aiming to solve the problem of ice-tolerant flight control, reduce the influence of icing conditions on controllability and safe flight of the transport aircraft, and thus improve the flight quality of the transport aircraft. The simulation results are verified under the influence of both standby ice type and failure ice type, and the interference effect on aircraft aerodynamic parameters is further added. The simulation results show that adaptive dynamic inverse control based on piecewise constant can overcome the influence caused by icing and aerodynamic parameter interference, achieve accurate tracking of command, and provide excellent fault tolerance and robustness, which ensures that the transport aircraft can achieve the desired control performance and safe flight capability.

Incremental Nonlinear Dynamics Inversion and Incremental Backstepping: Experimental Attitude Control of a Tail-Sitter UAV

Incremental control strategies such as Incremental Nonlinear Dynamics Inversion (INDI) and Incremental Backstepping (IBKS) provide undeniable advantages for controlling Uncrewed Aerial Vehicles (UAVs) due to their reduced model dependency and accurate tracking capacities, which is of particular relevance for tail-sitters as these perform complex, hard to model manoeuvres when transitioning to and from aerodynamic flight. In this research article, a quaternion-based form of IBKS is originally deduced and applied to the stabilization of a tail-sitter in vertical flight, which is then implemented in a flight controller and validated in a Hardware-in-the-Loop simulation, which is also made for the INDI controller. Experimental validation with indoor flight tests of both INDI and IBKS controllers follows, evaluating their performance in stabilizing the tail-sitter prototype in vertical flight. Lastly, the tracking results obtained from the experimental trials are analysed, allowing an objective comparison to be drawn between these controllers, evaluating their respective advantages and limitations. From the successfully conducted flight tests, it was found that both incremental solutions are suited to control a tail-sitter in vertical flight, providing accurate tracking capabilities with smooth actuation, and only requiring the actuation model. Furthermore, it was found that the IBKS is significantly more computationally demanding than the INDI, although having a global proof of stability that is of interest in aircraft control.

Particularities of Rotorcraft in Dealing with Advanced Controllers

Advanced nonlinear controllers are a desirable solution to rotorcraft flight control as they can solve the system high nonlinear dynamic behavior. However, conventional nonlinear controllers such as Nonlinear Dynamic Inversion (NDI) controller heavily rely on the availability of accurate model knowledge and this can be problematic for rotorcraft. Therefore, incremental control theory can solve the modelling errors sensitivity by relying on the information obtained from the sensors instead. The paper applied the Incremental Nonlinear Dynamic Inversion (INDI) controller to rotorcraft case. It will be demonstrated that, for rotorcraft, the incremental nonlinear controllers depend on the delays introduced in the controller by the rotor dynamics. To correct this behaviour, residualization and synchronization methods need to be applied accordingly in order to remove the effects of rotor flapping (disctilt) dynamics from the controller. These particularities of rotorcraft in dealing with advanced controllers shows that incremental nonlinear controllers can have relatively small stability robustness margin and careful controller design is needed in order to account properly for rotorcraft time delays and unmodelled dynamics.

Reinforcement-Learning-Based Visual Servoing of Underwater Vehicle Dual-Manipulator System

As a substitute for human arms, underwater vehicle dual-manipulator systems (UVDMSs) have attracted the interest of global researchers. Visual servoing is an important tool for the positioning and tracking control of UVDMSs. In this paper, a reinforcement-learning-based adaptive control strategy for the UVDMS visual servo, considering the model uncertainties, is proposed. Initially, the kinematic control is designed by developing a hybrid visual servo approach using the information from multi-cameras. The command velocity of the whole system is produced through a task priority method. Then, the reinforcement-learning-based velocity tracking control is developed with a dynamic inversion approach. The hybrid visual servoing uses sensors equipped with UVDMSs while requiring fewer image features. Model uncertainties of the coupled nonlinear system are compensated by the actor–critic neural network for better control performances. Moreover, the stability analysis using the Lyapunov theory proves that the system error is ultimately uniformly bounded (UUB). At last, the simulation shows that the proposed control strategy performs well in the task of dynamical positioning.

Application of dynamics inversion techniques to the force control of electrodynamic actuators used for active vibration absorption

Application of dynamics inversion techniques to the force control of electrodynamic actuators used for active vibration absorption

ACGND: towards lower complexity and fast solution for dynamic tensor inversion

Dynamic Tensor Inversion (DTI) is an emerging issue in recent research, prevalent in artificial intelligence development frameworks such as TensorFlow and PyTorch. Traditional numerical methods suffer significant lagging error when addressing this issue. To address this, Zeroing-type Neural Dynamics (ZND) and Gradient-type Neural Dynamics (GND) are employed to tackle the DTI. However, these two methods exhibit inherent limitations in the resolution process, i.e. high computational complexity and low solution accuracy, respectively. Motivated by this technology gap, this paper proposes an Adaptive Coefficient Gradient Neural Dynamics (ACGND) for dynamically solving the DTI with an efficient and precise manner. Through a series of simulation experiments and validations in engineering applications, the ACGND demonstrates advantages in resolving DTI. The ACGND enhances computational efficiency by circumventing matrix inversion, thereby reducing computational complexity. Moreover, its incorporation of adaptive coefficients and activation functions enables real-time adjustments of the computational solution, facilitating rapid convergence to theoretical solutions and adaptation to non-statinary scenarios. Code is available at https://github.com/Maia2333/ACGND-Code-Implementation.

Model Predictive Collision Avoidance Control for Object Transport of Unmanned Underwater Vehicle-Dual-Manipulator Systems

Unmanned underwater vehicle-dual-manipulator systems (UVDMSs) have attracted much research due to their humanoid operation capabilities, which have the advantage of cooperative manipulations and transporting underwater objects. Meanwhile, collision avoidance of UVDMSs is more challenging than that of unmanned underwater vehicle-dual manipulator systems (UVMSs). In this work, a model predictive control (MPC) approach is proposed for collision avoidance in objects transporting tasks of UVDMSs. The minimum distances of mutual manipulators and frame obstacles are handled as velocity constraints in the optimization of the UVDMS’s object tracking control. The command velocity generated by the model predictive kinematic controller is tracked by a dynamic inversion control scheme while model uncertainties are compensated by a neural network. Moreover, the tracking errors of the proposed dynamic controller are proved to be convergent by the Lyapunov method. At last, a three-dimensional (3D) UVDMS simulation platform is developed to verify the effectiveness of the proposed control strategy in the tasks of collision avoidance and object transport.

Robust-augmentation nonlinear dynamic inversion control of over-actuated coaxial high-speed helicopter in transition mode

As the elevator and rudder can be used actively for control, in addition to the rotors, Coaxial High-speed Helicopters (CHHs) have the problems of control redundancy and changing control authority in the transition mode. This paper presents a robust-augmentation transitioning flight control design for a CHH under the adverse conditions of parametric uncertainties and external disturbances. First, based on control characteristic analysis, an Adaptive Filtered Nonlinear Dynamic Inversion (AFNDI) controller is proposed for the angular rate to handle the effect of unknown unstructured uncertainties and external turbulence. Theoretical analysis proves that the presented angular rate controller can guarantee steady-state and transient performance. Furthermore, the attitude angle and velocity controllers are also added. Then, an Incremental-based Nonlinear Prioritizing Control Allocation (INPCA) method is designed to take into account control surface transition and changing control authority, which efficiently distributes the required moments between coaxial rotors and aero-surfaces, and avoids the control reversal problem of the yaw channel. In the proposed control architecture, the low-pass filter is introduced to alleviate the adverse influence of time delay and measurement noise. Finally, the effectiveness of the proposed controller is demonstrated through nonlinear numerical simulations, and is compared with existing methods. Simulation results show that the proposed control law can improve both capabilities of disturbance rejection and fast response, and works satisfactorily for the CHH transitioning control characteristic.

Evolving malware detection through instant dynamic graph inverse reinforcement learning

The rapid development and increasing evolution of malware necessitate novel defensive techniques with high accuracy and minimal false positives to safeguard information systems against potential threats. Unfortunately, current malware detection methods, primarily relying on deep learning to identify malicious fingerprints from extensive training sets, prove ineffective against few-shot and evolving malware variants. To address these challenges, this paper introduces MalIRL, which designs a model-free inverse reinforcement learning (IRL) mechanism to automatically capture the evolving attack intent of malware. Specifically, MalIRL explores six representative categories of malware actions and employs sliding windows to organically divide the massive malware execution event stream into multiple attack stages, achieving a reduced action space and state space. To model dynamic malicious environments, MalIRL proposes an instant dynamic heterogeneous graph representation learning technique. This technique learns state representations of different malware attack stages, enhancing detection accuracy and efficiency by incrementally capturing the newly added contextual semantics of diverse malware entities and relations. In experiments with three real-world malware datasets, MalIRL surpasses existing state-of-the-art methods, particularly in few-shot malware detection scenarios, MalIRL exhibits performance benefits of up to 18.9%.

Synthesis and Conformational Dynamics of Selenanthrene (Oxides): Establishing an Energetic Flexibility Index for Scaffolds.

The use of new dynamic scaffolds for constructing inorganic and organometallic complexes with enhanced reactivities is an important new research direction. Toward this fundamental aim, an improved synthesis of the dynamic scaffold selenanthrene, along with its monoxide, trans-dioxide and the previously unknown trioxide, is reported. A discussion of the potential reaction mechanism for selenanthrene is provided, and all products were characterized using 1H, 13C, and 77Se nuclear magnetic resonance (NMR) spectroscopy and single-crystal X-ray crystallography. The dynamic ring inversion processes (i.e., "butterfly motion") for selenanthrene and its oxides were investigated using variable-temperature 1H NMR and density functional theory calculations. The findings suggest that selenanthrene possesses a roughly equal barrier to inversion as its sulfur analogue, thianthrene. However, selenanthrene oxides evidently possess larger inversion barriers as compared to their sulfur analogues due to the enhanced electrostatic intramolecular interactions inherent between the highly polar selenium-oxygen bond and adjacent C-H moieties. Finally, we propose a quantitative "flexibility index" in deg/(kcal/mol) for various tricyclic scaffolds to provide researchers with a comparative scale of dynamic motion across many different systems.

Aggressive flight control of quadrotors using incremental nonlinear dynamic inversion with a high-fidelity thrust unit model

The application of quadrotors has been expanded into aggressive tasks such as military confrontation and aerobatics shows, making it crucial to enhance flight maneuverability to adapt to rapid and large-scale command changes. Unlike hovering or low-speed modes, aggressive flight exacerbates quadrotor model uncertainty, making it challenging for controllers to balance response speed and robustness. This paper proposes an INDI control method that integrates a high-fidelity thrust model unit to address this practical need. First, based on the pole distribution theory and the application of INDI on quadrotors, it is concluded that the dynamic response of quadrotor flight is impacted by the accuracy of the thrust model. Second, consider the airflow velocity and angles, the high-fidelity modeling approach, and the aerodynamic forces and torque expression validated through wind tunnel experiments are provided. Then the flight controller for the quadrotor is designed with the established high-fidelity thrust unit model. Finally, aggressive trajectory-tracking flight tests are set to evaluate the improvement of our work. The results demonstrate that our proposed method indeed enhances the response speed and tracking accuracy, as well as the flight stability of the quadrotor.

Parameter Tuning Approach for Incremental Nonlinear Dynamic Inversion-Based Flight Controllers

Incremental nonlinear dynamic inversion (INDI) is a widely used approach to controlling UAVs with highly nonlinear dynamics. One key element of INDI-based controllers is the control allocation realizing pseudo controls using available actuators. However, the tracking of commanded pseudo controls is not the only objective considered during control allocation. Since the approach only works locally due to linearization and the solution is often ambiguous, additional aspects like control efforts or penalizing the deviation of certain states must be considered. Conducting the control allocation by solving a quadratic program this results in a considerable number of weighting parameters, which must be tuned during control design. Currently, this is conducted manually and is therefore time consuming. An automated approach for tuning these parameters is therefore highly beneficial. Thus, this paper presents and evaluates a model-based approach automatically tuning the control allocation parameters of a tiltrotor VTOL using an optimization algorithm. This optimization algorithm searches for optimal parameters minimizing a cost functional that reflects the design target. This cost functional is calculated based on a test mission for the VTOL which is conducted within a simulation environment. The test mission represents the common operating range of the VTOL. The simulation environment consists of an aircraft model as well as a model of the INDI-based controller which is dependent on the control allocation parameters. On this basis, model-based optimization is conducted and the optimal parameters are identified. Finally, successful real-world tests on a 4-degrees-of-freedom testbench using the identified parameters are presented. Since the control allocation parameters can significantly influence the aircraft’s stability, the 4-DOF testbench for the aircraft is required for rapid validation of the parameters at a minimum amount of risk.

Output feedback control for hypersonic flight vehicles via distributed high-gain observer

During the hypersonic flight process of rigid hypersonic flight vehicles (HFVs), the flight-path angle and angle of attack are usually small and sensitive to flight condition variation, the accurate measurements are difficult, subsequently the formulation of states observers is an urgent issue. A distributed high-gain observer is proposed to reconstruct the admissible system state dynamics based on limited measured output. First, a distributed observer is proposed for the cascade strict-feedback system to ensure the global convergence of the state estimation errors between the estimated state in each local observer and system states, for the prescribed system initial values. Second, the distributed high-gain observer-based nonlinear control is investigated on the longitudinal dynamics of HFVs with partial measurable states. The rigid body system dynamic is divided into the altitude subsystem and the velocity subsystem. In the altitude subsystem, the proposed distributed high-gain observer is formulated to exact estimate the flight-path angle and angle of attack, and then the back-stepping design is proposed for constructing the controllers. The nonlinear dynamic inversion controller is introduced to accomplish the velocity tracking. Finally, simulation examples are served to verify that the proposed output feedback control possesses the superior properties of high tracking performance, fast convergence of state estimation error and easy-implementation.

Discriminatory resonance energy transfer mediated by a chiral environment

In this study, we delve into the crucial influence of and enhancement by chiral environments on the discriminatory capabilities of resonance energy transfer. Firstly, we scrutinize the impact of a macroscopic chiral medium enveloping the interacting molecules; secondly, we probe the effect of a chiral mediating molecule in close proximity to the system. Importantly, our findings demonstrate that chiral environments not only modulate pre-existing discriminatory effects but also introduce novel mechanisms for discrimination. Central to our research is the application of an innovative model for chiral local-field corrections, which unveils a remarkable distance-dependent inversion of the discrimination dynamics. Our study extends beyond the confines of any specific molecular system, offering a comprehensive discussion of these diverse effects, thereby providing insights with broader implications. Finally, we present a comparative analysis across all studied systems, illustrating our insights by employing 3-methyl-cyclopentanone as an example molecule.