Backstepping-based adaptive control of a quadrotor UAV with guaranteed tracking performance

Intelligent switching adaptive control for uncertain nonlinear dynamical systems

The force tracking error is dependent on both the position tracking error and the estimated parameter error so that, in constrained adaptive robot control, the convergence of the estimated parameter error becomes more important than in unconstrained adaptive robot control. However, in the direct adaptive controller, the parameter adaptation is driven only by the tracking error in the joint motion, while in the indirect adaptive controller, the parameter estimation is driven only by the prediction error in the joint torque. Based on this observation a new combined direct and indirect adaptive controller driven by both the tracking error and the prediction error is developed for constrained robots with inertia parameter uncertainty. Stability of the combined adaptive controllers is established in the Lyapunov sense. The combined control improves both position and force tracking performance compared with direct adaptive control. The computer simulations are presented to demonstrate the proposed schemes.

This paper presents a robust indirect adaptive stabilization controller for systems with linear time-invariant (LTI) nominal parts, uncertain dynamic errors, and bounded disturbances. The a priori knowledge required for designing the adaptive controller is that the nominal part of the system is of known order and is stabilizable.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Indirect adaptive control of multi-input-multi-output nonlinear singularly perturbed systems with model uncertainties

A novel low-complexity adaptive control method, capable of guaranteeing the transient and steady-state tracking performance in the presence of unknown nonlinearities and actuator saturation, is investigated for the longitudinal dynamics of a generic hypersonic flight vehicle. In order to attenuate the negative effects of classical predefined performance function for unknown initial tracking errors, a modified predefined performance function with time-varying design parameters is presented. Under the newly developed predefined performance function, two novel adaptive controllers with low-complexity computation are proposed for velocity and altitude subsystems of the hypersonic flight vehicle, respectively. Wherein, different from neural network-based approximation, a least square support vector machine with only two design parameters is utilized to approximate the unknown hypersonic dynamics. And the relevant ideal weights are obtained by solving a linear system without resorting to specialized optimization algorithms. Based on the approximation by least square support vector machine, only two adaptive scalars are required to be updated online in the parameter projection method. Besides, a new finite-time-convergent differentiator, with a quite simple structure, is proposed to estimate the unknown generated state variables in the newly established normal output-feedback formulation of altitude subsystem. Moreover, it is also employed to obtain accurate estimations for the derivatives of virtual controllers in a recursive design. This avoids the inherent drawback of backstepping — “explosion of terms” and makes the proposed control method achievable for the hypersonic flight vehicle. Further, the compensation design is employed when the saturations of the actuator occur. Finally, the numerical simulations validate the efficiency of the proposed finite-time-convergent differentiator and control method.

This paper presents the development of direct adaptive control laws to enable an autonomous unmanned aerial vehicle (UAV) to track reference trajectories in the presence of external disturbances. A maneuver database of reference trajectories is computed offline from a linear UAV dynamic model using an optimization routine. Each maneuver in the database is therefore represented as a state trajectory, as well as the control input sequence that generates that trajectory. The trajectory tracking control laws are designed to enable a UAV to follow reference trajectories from this maneuver database. A disturbance accommodating control law is first developed and applied for the tracking of reference trajectories while the UAV is subjected to a persistent external disturbance. This control design requires a state-space disturbance generator model and the derivation of ideal trajectories from a reference model, which requires the solution of a set of matching conditions. A direct adaptive controller is then developed to enable the UAV to track reference trajectories while subject to modeling error and external disturbances. The adaptive controller also requires a disturbance model and the existence of ideal trajectories, but it does not require the explicit solution of the matching conditions. Simulation results for the disturbance accommodating controller and the adaptive controller are presented using a linear six-degree-of-freedom dynamic model that is representative of typical UAV dynamics. The results show that, although both controllers track reference trajectories and provide disturbance rejection, the adaptive controller is better suited for handling modeling errors or uncertainties.

: Unmanned aerial vehicles (UAVs) are critical components of the future naval forces. UAV control and monitoring with autonomous operation will become an absolute necessity and adaptive cooperation of vehicles is the only practical alternative. The objective of this project is to develop and evaluate new methodologies for cooperative (formation) control of multiple unmanned air vehicles. The goal is to have multiple UAVs working together as a group. Instead of separately assigning distinct tasks to each vehicle, the operator would assign tasks to the UAV group, which then determines the best way to accomplish each task, freeing the operator to maintain surveillance over the entire operation. In this project we investigated Path Tracking and obstacle avoidance of UAVs using fuzzy logic method. Algorithms for close formation control of multi-UAVs are developed and simulated. We also investigated fault-tolerant control of single UAVs by neuro-adaptive method. Detailed description of this method is provided in this document. The project has supported S graduate students with 9 technical papers published.

This paper proposes an indirect adaptive fuzzy neural network (FNN) controller with state observer and supervisory controller for a class of uncertain nonlinear dynamic time delay systems. First, the approximate function of unknown time delay system is inferred by the adaptive time delay FNN system. Next, a state observer is designed to estimate the unknown system states and the indirect adaptive fuzzy controller is constructed. Finally, the closed loop controller is obtained by incorporating the supervisory controller with the indirect adaptive fuzzy controller. Therefore, if the system tends to unstable, i.e., error dynamics is larger than a prescribed constraint which is determined by designer, the supervisory controller will activate to force the state to be stable. The free parameters of the indirect adaptive FNN controller can be tuned online by observer-based output feedback control law and adaptive laws by means of Lyapunov stability criterion. The resulting simulation example shows that the performance of nonlinear time delay chaotic system is fully tracking the reference trajectory. Meanwhile, simulation results show that the adaptive control effort of the proposed control scheme is much less due to the assist of the supervisory controller.

As a complicated multi-input, multi-output, and time-varying nonlinear system, flight control system of unmanned aerial vehicle (UAV), which determines the whole system’s performance directly, is crucial for the simulation training system design. This chapter mainly focuses on parameter identification of flight control system based on the modeling of UAVs and a specific controller design for the pendulum-like oscillation in micro aerial vehicle (MAV). A predator–prey particle swarm optimization (PSO) algorithm for identifying parameters of UAV flight control system is presented, with the aim of reducing the workload of the designers during the process of designing complicated UAV control system. Besides, a software environment for UAV controller design was developed based on the UAV model and the proposed method. Then a specific kind of controller design involving the pendulum-like oscillation in the hover and stare state for a MAV in the presence of external disturbances is discussed in detail, since the pendulum-like oscillation caused by uncertainty and external disturbances badly jeopardize the performance of hover and stare, resulting in blurred images or even MAV’s overturning. A novel type of PSO-based linear-quadratic regulator (LQR) controller for stabilizing the pendulum-like oscillation is developed, which can enhance the MAV’s performance efficiently.

A quaternion-based attitude control system is developed for the X-33 in the ascent flight phase. A non-linear control law commands body-axis rotation rates that align the angular velocity vector with an Euler-axis defining the axis of rotation that takes the body-axis system into a desired-axis system. The magnitude of the commanded body rates are deter- mined by the magnitude of the rotation error. The commanded body rates form the input to a dynamic inversion-based adaptive/reconfigurable control law. The indirect adaptive control portion uses on-line system identification to estimate the current control effectiveness matrix to update a control allocation module. The control allocation runs in a null-space injection mode that excites and decorrelates the ef- fectors without degrading the vehicle response in or- der to enable on-line system identification. A direct adaptive control scheme uses the output of a neural network to compensate for dynamic inversion error. The overall system is designed to provide fault and damage tolerance for the X-33 on ascent. Prelimi- nary results are shown to demonstrate the feasibility of the approach.

Unmanned Aerial Vehicles (UAVs) have become essential tools in various industries for tasks such as inspection, maintenance, and surveillance. An Online Impedance Adaptive Controller (OIAC) is proposed for the online modulating of UAV control gains to obtain better performance and stability of tracking curved trajectories than the traditional methods, Model Reference Adaptive Controller (MRAC) and Proportional–Integral–Derivative (PID). Two UAV path planners with minimal jerk and snap were integrated into OIAC, MRAC, and PID. These six controllers were implemented and compared in a simulated UAV with perceptional noise, which follows curved pipelines and avoids obstacles. Experimental results show that the OIAC controller achieves at least an 80% improvement over the PID controller across all trajectory types in terms of the trajectory tracking error. Additionally, OIAC demonstrates an over 20% improvement in jerk trajectories and a more than 30% improvement in snap trajectories when compared to the MRAC controller. These results indicate that OIAC offers enhanced trajectory tracking accuracy and robustness against perceptual noise. Our work presents an advanced controller of a UAV and its preliminary validation in accurate and robust path tracking.

The control of unmanned aerial vehicles (UAVs) has been an active area of research over the past decade, particularly for operations in complex environments. This paper addresses the finite-time fault-tolerant control problem for UAV subjected to simultaneous actuator faults and wind disturbances. A novel adaptive finite-time disturbance observer-based fault-tolerant control (AFTDO-FTC) scheme is proposed. This scheme integrates a finite-time sliding surface, a nonlinear disturbance observer, and an adaptive controller to achieve accurate tracking of position and attitude. First, a nonlinear disturbance observer is designed to estimate the lumped uncertainty arising from combined faults and wind disturbances. Then, a finite-time sliding surface, formulated using weighted error vectors, is introduced to effectively mitigate the adverse effects of estimation errors from the disturbance observer. Furthermore, adaptive finite-time position and attitude controllers are developed based on the estimates provided by the adaptive disturbance observer. Finally, the effectiveness of the proposed method is verified through comparative simulations and Lyapunov stability analysis.

In this note we address the problem of indirect adaptive (regulation or tracking) control of nonlinear, input affine dissipative systems. It is assumed that the supply rate, the storage and the internal dissipation functions may be expressed as nonlinearly parameterized regression equations where the mappings (depending on the unknown parameters) satisfy a monotonicity condition—this encompasses a large class of physical systems, including passive systems. We propose to estimate the system parameters using the “power‐balance” equation, which is the differential version of the classical dissipation inequality, with a new estimator that ensures global, exponential, parameter convergence under the very weak assumption of interval excitation of the power‐balance equation regressor. To design the indirect adaptive controller we make the standard assumption of existence of an asymptotically stabilizing controller that depends—possibly nonlinearly—on the unknown plant parameters, and apply a certainty‐equivalent control law. The benefits of the proposed approach, with respect to other existing solutions, are illustrated with examples.

With the demand for energy efficiency in electrohydraulic servo systems (EHSS), the separate meter-in and separate meter-out (SMISMO) control system draws massive attention. In this paper, the SMISMO control system is decoupled completely into two subsystems by the proposed indirect adaptive robust dynamic surface control (IARDSC) method. Indirect adaptive robust control (IARC) is proposed to address the internal parameter uncertainties and external disturbances. Dynamic surface control (DSC) is utilized in the design procedure of IARC to deal with the inherent ‘explosion of terms’ problem. The proposed IARDSC simplifies the design procedure and decreases the computational cost of the controller. Besides, a faster parameter estimation scheme is proposed to adapt to the parameter change for a better estimation performance. Finally, experimental results show that the proposed IARDSC can achieve a good parameter estimation and trajectory tracking performance. Meanwhile, two energy saving techniques are discussed.

A quadratic programming based neural dynamic (QPND) controller design method is proposed for time-varying trajectory tracking of unmanned aerial vehicles (UAVs). Different from the traditional controller frameworks which only use pitch and roll angles to achieve the horizontal movement of the UAV, all the three attitude angles, i.e., roll, pitch and yaw angles, are applied for horizontal moving in this paper. There are at least three great superiorities of the proposed QPND controller. Firstly, three attitude angles can be coordinated control since the quadratic programming framework incorporates three attitudes. Secondly, the new framework enables the UAV to achieve the additional performance criteria or to perform subtasks. Third, the new controller can track time-varying trajectories due to neural dynamic approach. Specifically, the design process of the controller is divided into position controller design, attitude controller design and quadratic programming formulation. Finally, simulation experiments verify the feasibility and high efficiency of the proposed controller and its superiority compared with some state-of-the-art UAV controllers.