Pseudo Control Hedging Research Articles

Enhancing Limited Authority Adaptive Control: Peudocontrol Hedging for Addressing Input Saturation

This paper introduces an innovative approach to address the challenges of input saturation and interconnected subsystem control in limited authority adaptive control systems. Limited authority adaptive control faces issues when encountering input saturation, which can severely impact the system's performance and adaptability. Additionally, when dealing with cascaded control systems, ensuring smooth adaptation for both subsystems is essential. To overcome these challenges, we propose a method known as Pseudocontrol Hedging (PCH), which allows adaptation to continue even during input saturation and ensures effective control in interconnected subsystems. The PCH technique modifies the reference model response to accommodate expected system behavior, enabling reliable adaptation regardless of system response.

Incremental Nonlinear Dynamic Inversion Attitude Control for Helicopter with Actuator Delay and Saturation

In this paper, an incremental nonlinear dynamic inversion (INDI) control scheme is proposed for the attitude tracking of a helicopter with model uncertainties, and actuator delay and saturation constraints. A finite integral compensation based on model reduction is used to compensate the actuator delay, and the proposed scheme can guarantee the semi-globally uniformly ultimately bounded tracking. The overall attitude controller is separated into a rate, an attitude, and a collective pitch controller. The rate and collective pitch controllers combine the proposed method and INDI to enhance the robustness to actuator delay and model uncertainties. Considering the dynamic of physical actuators, pseudo-control hedging (PCH) is introduced both in the rate and attitude controller to improve tracking performance. By using the proposed controller, the helicopter shows good dynamics under the multiple restrictions of the actuators.

Robust Helicopter Sliding Mode Control for Enhanced Handling and Trajectory Following

Helicopters in tactical missions require superior handling and stability in turbulence. This paper proposes a sliding mode control (SMC)-based helicopter trajectory controller for enhanced handling qualities (HQs) with robustness to endogenous (modeling) and exogenous (turbulence) uncertainties. The flight control problem is posed as one of attitude, angular rate, and translational rate command tracking with closed-loop axial responses conforming to predicted level 1 HQs. The required closed-loop behavior is then enforced by a two-loop output-tracking SMC treating interaxis coupling and turbulence as matched, bounded uncertainties. To mitigate the chattering effect of SMC, a continuous control approximation using the boundary-layer concept is applied. The stability and tracking performance of the two-loop controller are analyzed. Furthermore, actuator saturations encountered during aggressive maneuvers are mitigated by reference command adaptation based on pseudo-control hedging. The proposed controller is evaluated against selected HQ temporal response criteria, two mission task elements requiring moderate to aggressive agility, and a shipboard approach in high-intensity turbulence. Simulation results highlight the controller’s trajectory tracking accuracy, adequate axial decoupling, and insensitivity to disturbances.

Neural network based adaptive control of airplane’s lateral-directional motion during final approach phase of landing

The paper presents three new autonomous systems for the control of lateral-directional motion of airplane during landing. The geometry of airplane motion in lateral-directional plane takes into account the wind having different velocities and directions. All the three control systems contain an adaptive control subsystem based on the concepts of dynamic inversion and neural networks; the reference models of the adaptive control systems receive the signals provided by Pseudo Control Hedging blocks, as additional signals, to cancel the neural networks’ adapting difficulties in the case of nonlinear actuators with time delay and saturation zones with respect to the velocity and displacement. Two of the three automatic control systems have proportional–derivative/proportional–integral– derivative controllers for the control of the lateral deviation of airplane with respect to the runway. The new designed adaptive architectures have been software implemented and validated by complex numerical simulations; the obtained results prove the new architectures’ stability and small overshoots.

An Adaptive Architecture for Control of Uncertain Dynamical Systems with Unknown Actuator Bandwidths

Closed-loop system stability of adaptive control architectures for autonomous aerospace vehicles can be seriously degraded by the presence of actuator dynamics. To this end, recent research by the authors focuses on a new reference model design approach such that not only closed-loop system stability is rigorously addressed using linear matrix inequalities, but also the proposed reference model achieves better performance characteristics as compared with a well-adopted method in the literature called pseudo-control hedging. Yet, this reference model assumes the exact knowledge of the actuator bandwidth parameter, where such an assumption may not always hold for practical aerospace applications. Motivated from this standpoint, this paper generalizes our recent results such that the exact knowledge of the actuator bandwidth parameter is no longer necessary. Specifically, we utilize an online estimate of the actuator bandwidth parameter in the proposed reference model and show the stability of the closed-loop dynamical system using tools and methods from Lyapunov theory and linear matrix inequalities. An illustrative numerical example involving the short-period dynamics of a hypersonic vehicle model is also included in this paper to elucidate the proposed adaptive architecture for control of uncertain dynamical systems with unknown actuator bandwidths.

Object-oriented modelling of wind turbines and its application for control design based on nonlinear dynamic inversion

ABSTRACTThis article presents the object-oriented modelling of wind turbine aero-elastics. The article details the undertaken modelling approach and demonstrates its validity by comparing the results to a domain-specific simulation tool. The resulting object-oriented models can be generically adapted to meet different tasks or combined with models from other domains. Also the combination object-oriented Modelica and functional mock-up interface technology enable a rapid design of nonlinear controllers. As one possible application example, the article presents the model-based design of a corresponding controller. Its goal is to extend the turbine lifetime by reducing the structural loads. To this end, a modern control scheme is proposed that takes full advantage of the underlying object-oriented modelling approach. The controller is based on nonlinear dynamic inversion control methods combined with pseudo control hedging. The controller uses wind speed measurement information to adjust to wind gust load. Simulation-based comparisons to conventional control designs show a large potential reduction of the gust load on the wind turbine using the proposed controller.

Automatic landing system using neural networks and radio-technical subsystems

The paper focuses on the design of a new automatic landing system (ALS) in longitudinal plane; the new ALS controls the aircraft trajectory and longitudinal velocity. Aircraft control is achieved by means of a proportional-integral (PI) controller and the instrumental landing system – the first phase of landing (the glide slope) and a proportional-integral-derivative (PID) controller together with a radio-altimeter – the second phase of landing (the flare); both controllers modify the reference model associated with aircraft pitch angle. The control of the pitch angle and longitudinal velocity is performed by a neural network adaptive control system, based on the dynamic inversion concept, having the following as components: a linear dynamic compensator, a linear observer, reference models, and a Pseudo control hedging (PCH) block. The theoretical results are software implemented and validated by complex numerical simulations; compared with other ALSs having the same radio-technical subsystems but with conventional or fuzzy controllers for the control of aircraft pitch angle and longitudinal velocity, the architecture designed in this paper is characterized by much smaller overshoots and stationary errors.

Landing Auto‐Pilots for Aircraft Motion in Longitudinal Plane using Adaptive Control Laws Based on Neural Networks and Dynamic Inversion

AbstractThis paper presents two new automatic landing systems (ALSs) for aircraft motion in longitudinal plane; the model of the landing geometry determines the flight trajectory and the aircraft calculated altitude; the flight trajectory during landing consists of two parts: the glide slope and the flare. Both designed ALSs have an adaptive system (ACS) for the aircraft output's control; for the first ALS, the output vector consists of the flying altitude and the longitudinal velocity, while, for the second ALS, the output variables are the pitch angle and the longitudinal velocity of aircraft. The second variant of ALS also contains an altitude controller providing the calculated pitch angle. The calculated altitude (for the first ALS), the calculated pitch angle (for the second ALS), and the desired flight velocity are provided to the ACS by means of a block consisting of two reference models. ACS is based on the dynamic inversion concept and contains an adaptive controller which includes a linear dynamic compensator, a state observer, a neural network, and a Pseudo Control Hedging block. The paper is focused both on the design of the two ALSs and on their complex software implementation and validation.

Guidance and control system using constrained adaptive backstepping method for space transportation

This study presents a new guidance and control system using a constrained adaptive backstepping method for a space transportation system. In this method, the effects of input saturations by actuator dynamics (e.g. magnitude, rate and bandwidth) are considered to introduce the compensators on the basis of pseudo control hedging. The stability of the proposed entire system is guaranteed by the Lyapunov’ stability theorem. To confirm the realization and robustness of the proposed system, Monte Carlo simulations (MCSs) were performed. In addition, to obtain optimized control performance, a parameter optimization algorithm combined with the MCSs was introduced. Finally, automatic landing simulations using the six degrees-of-freedom nonlinear flight simulation model of the NASA’s Space Shuttle Orbiter were performed to verify the effectiveness of the proposed technique.

Nonlinear optimized adaptive trajectory control of helicopter

This paper attempts to develop an optimized adaptive trajectory control system for helicopters based on the dynamic inversion method. This control algorithm is implemented by three time-scale separation architectures. Pseudo control hedging (PCH) is used to protect the adaptive element from actuator saturation nonlinearities and also from the inner-outer-loop interaction. In addition, to augment the attitude control system, two online adaptive architectures that employ a neural network are used. By tuning the neural network based on the system model, a better and faster learning will be achieved, but this is a frustrating and time consuming process. Due to complexity in accurate tuning of neural network, this paper introduces a non-dominated sorting genetic algorithm II (NSGA-II) for off-line optimization of the neural network. Thus, in the proposed method, the neural network can compensate model inversion error caused by the deficiency of full knowledge of helicopter dynamics more accurately. The effectiveness of proposed method is demonstrated by numerical simulations.

Automatic control of aircraft lateral-directional motion during landing using neural networks and radio-technical subsystems

The paper presents a new automatic architecture for the control of aircraft lateral-directional motion during landing; the system controls the lateral angular deviation of aircraft longitudinal axis with respect to the runway, by using a classical controller, a radio-navigation system, a system for the calculation of the distances between aircraft and the runway radio-markers, and an adaptive controller mainly used for the control of aircraft roll angle and its deviation with respect to the runway. The adaptive control system uses the dynamic inversion concept, a dynamic compensator, a neural network trained by the system׳s estimated error vector (signal provided by a linear observer), and a Pseudo Control Hedging block. The new designed adaptive architecture is software implemented and validated by complex numerical simulations; the obtained characteristics are very good and prove the new architecture׳s stability and its small overshoots.

Nonlinear model predictive dynamic positioning control of an underwater vehicle with an onboard USBL system

This paper presents a novel optimization-based approach for dynamic positioning (DP) of a fully actuated underwater vehicle equipped with an onboard ultrashort baseline transceiver to provide relative position information of two earth-fixed transponders near the vehicle. The DP system error is defined by the transponders’ positions compared to the desired values, which occur at the vehicle’s target pose (position and orientation). The proposed DP strategy is composed of two loops in a hierarchical structure. In the kinematic loop, the nonlinear model predictive control is used to generate the desired velocity by optimizing a cost function of the predictive trajectories under the constraints of velocity and transponder bearings over a limited time horizon. In the dynamic loop, the neural network model reference adaptive control with pseudo control hedging is utilized to ensure the asymptotical convergence of velocity tracking errors in the presence of uncertainties associated with unknown model parameters, currents and thruster dynamics. The effectiveness of the proposed control scheme is illustrated by comprehensive simulations.

Autonomous waypoint guidance for tilt-rotor unmanned aerial vehicle that has nacelle-fixed auxiliary wings

The mathematical dynamics model of the tilt-rotor unmanned aerial vehicle (UAV) that has nacelle-fixed auxiliary wings (NFAWs) is presented based on computational fluid dynamics analysis using FLUENT and DATCOM. The advantage of the aerodynamic performance of the NFAW is compared to the performance of the original tilt-rotor UAV in a trim analysis as well as simulation. The inner loop and outer loop of the neural network controller are designed for the tilt-rotor and its NFAW variant. In order to improve the control performance of outer loop, pseudo-control hedging (PCH) is applied to the outer loop as well as the inner loop neural network control. The dynamic inversion on a linear model of the original tilt-rotor at hover conditions is used as a baseline. The sigma-pi neural network (SPNN) adaptation minimizes the error of the inversion model. This error typically occurs due to the use of an approximate tilt-rotor model for helicopter mode instead of the NFAW model throughout the flight envelope from helicopter to airplane mode. The waypoint navigation and the automatic hover guidance are applied to the most outer loop of the neural network controller for the autonomous flight, which consists of nacelle conversion and reconversion as well as automatic take-off and landing. The fast dynamic reference commands generated by the autonomous waypoint guidance are inputted to the outer loop control in order to make the PCH of the outer loop effective. Lastly, the nonlinear simulation results are compared under turbulent wind conditions, in which the NFAW is more negatively affected than the original tilt-rotor model.

주익이 손상된 전익형 무인기를 위한 신경회로망 적응제어기법에 관한 연구

무인항공기가 외형손상을 입는 경우, 비행역학 특성이 변하기 때문에 손상 이전 설계된 제어기는 더 이상 안정적인 제어성능을 보장하지 않는다. 본 논문에서는 주익의 손상이 일어난 무인항공기에 대해서도 강건한 제어성능을 보장하는 신경회로망 적응제어기법을 소개한다. 구동기의 특성에 의한 제어기의 성능저하를 방지하기 위해 Pseudo Control Hedging (PCH)를 추가적으로 사용하였다. 기체고정좌표계의 중심이 항공기의 무게중심에 위치하지 않는 비대칭 동역학을 사용하였으며, 전익형 무인기를 대상 비행체로 하였다. 날개가 손상되지 않은 모델과 손상된 모델의 풍동시험을 통해 얻은 공력데이터를 이용하여 시뮬레이션을 수행하였다. 시뮬레이션의 결과를 통해 제안된 제어기법이 주익의 손상이 발생한 항공기에 대해서도 여전히 안정적인 조종성능을 보장하는 제어기법임을 검증하였다. A damage imposed on an unmanned aerial vehicle changes the flight dynamic characteristics, and makes difficult for a conventional controller based on undamaged dynamics to stabilize the vehicle with damage. This paper presents a neural network based adaptive control method that guarantees stable control performance for an unmanned aerial vehicle even with damage on the main wing. Additionally, Pseudo Control Hedging (PCH) is combined to prevent control performance degradation by actuator characteristics. Asymmetric dynamic equations for an aircraft are chosen to describe motions of a vehicle with damage. Aerodynamic data from wind tunnel test for an undamaged model and a damaged model are used for numerical validation of the proposed control method. The numerical simulation has shown that the proposed control method has robust control performance in the presence of wing damage.

Path Generation and Control for Unmanned Multirotor Vehicles Using Nonlinear Dynamic Inversion and Pseudo Control Hedging

This paper presents a new structure for path following control that allows multirotor UAVs to fly on predefined paths with high velocity. In contrast to pure trajectory tracking, it prioritizes spatial over temporal errors in case of actuator saturation. Path generation is achieved by a unit quaternion curve and an associated parallel transport frame in an interactive process, which leads to feasible paths of class C3. The suggested control approach is composed of a nonlinear dynamic inversion trajectory controller and a path-based reference model with in-flight adjustable path velocity. By adopting the concept of pseudo control hedging, the tangential path progression is slowed down to the physically reachable velocity. Yaw rotation remains as an additional degree of freedom. Experimental flight tests are performed on a quadrotor with GPS receiver and low cost inertial sensors for state estimation. They demonstrate high velocity path following in the presence of thrust saturation, external disturbances and modeling uncertainties.

Adaptive Control of Helicopter Pitch Angle and Velocity

AbstractThis paper discusses flying objects’ adaptive control with direct application to the flight of helicopters. Two new automatic adaptive control systems are suggested: the former is used for pitch angle control, while the latter is used for control of helicopter pitch angle and velocity; this second system is an extension of the first one. The adaptive control is based on the dynamic inversion principle and the use of neural networks. The two adaptive control systems have reference models, linear dynamic compensators, linear observers, and neural networks. The adaptive components of the automatic control laws compensate for the approximation errors of the dynamic model’s nonlinear functions. The used actuators are linear or nonlinear. To eliminate the neural networks’ adapting difficulties, a pseudo-control hedging (PCH) block is inserted in the adaptive system; it limits the adaptive pseudo-control by means of a component that represents the estimation error of the actuator dynamics. Thus, the PCH ...

Open-Loop Reference System for Nonlinear Control Applied to Unmanned Helicopters

A novel way to design a nonlinear feedforward controller is presented. Contrary to input-output linearization and pseudo-control hedging techniques, the new formulation proposed is based on the states of the reference system instead of the plant’s states. By reshaping a commonly-used structure, the resulting scheme is used to produce a pure nonlinear feedforward system which is completely independent from system outputs. This permits the application of the presented control law when knowledge of the system states is not available. Due to the strict separation of plant and reference dynamics, stability of the reference system is no longer affected by the feedback of plant states. Results of flight tests of an unmanned helicopter, which are presented in this paper, demonstrate the applicability of this control concept.

Design and simulation of fault tolerant flight control based on a physical approach

This paper presents a study on fault tolerant flight control of a benchmark aircraft model, using a physical modular approach. Reconfiguring control is implemented by making use of adaptive nonlinear dynamic inversion for autopilot control. The weakness of classical nonlinear dynamic inversion, its sensitivity to modeling errors, is circumvented here by making use of a real-time identified physical model of the damaged aircraft. With help of the Boeing 747 benchmark simulation model, including the realistic component as well as the structural failure modes, it is possible to analyze the damage accommodation capabilities of the considered approach. In failure conditions, the damaged aircraft model is identified in real time by the two-step method and this model is applied subsequently to the model-based adaptive nonlinear dynamic inversion routine in a modular structure, which allows flight control reconfiguration on-line. Pseudo Control Hedging is included to guarantee that no unachievable reference commands are given to the system, which would otherwise result in control effector saturation. Various simulated reconfiguration test results are shown for damaged aircraft models. These results indicate satisfactory failure handling capabilities of this fault tolerant control setup, for component as well as structural failures.

Adaptive Control of Advanced Fighter Aircraft in Nonlinear Flight Regimes

When advanced fighter aircraft fly at high angles of attack, unsteady aerodynamic effects, wing rock, and saturation of aerodynamic effectors can lead to difficulty in control and maneuverability. A novel adaptive output feedback control design based on dynamic inversion is investigated for aircraft which are operated in highly nonlinear flight regimes, where uncertainties in the form of both unmodeled parameter variations and unmodeled dynamics are common. The stability of the design is analyzed and validated with simulations using a modified NASA F-15 simulation. The simulation includes thrust vectoring and a differential stabiliator to provide increased control authority at high angles of attack and relaxed static stability to increase pitch maneuverability. The control designs include the use of pseudocontrol hedging techniques to exclude adaptation to control saturation.

Adaptive Trajectory Control for Autonomous Helicopters

For autonomous helicopter flight, it is common to separate the flight control problem into an inner loop that controls attitude and an outer loop that controls the translational trajectory of the helicopter. In previous work, dynamic inversion and neural-network-based adaptation was used to increase performance of the attitude control system and the method of pseudocontrol hedging (PCH) was used to protect the adaptation process from actuator limits and dynamics. Adaptation to uncertainty in the attitude, as well as the translational dynamics, is introduced, thus, minimizing the effects of model error in all six degrees of freedom and leading to more accurate position tracking. The PCH method is used in a novel way that enables adaptation to occur in the outer loop without interacting with the attitude dynamics. A pole-placement approach is used that alleviates timescale separation requirements, allowing the outer-loop bandwidth to be closer to that of the inner loop, thus, increasing position tracking performance. A poor model of the attitude dynamics and a basic kinematics model is shown to be sufficient for accurate position tracking. The theory and implementation of such an approach, with a summary of flight-test results, are described.