Autonomous Landing of a Quadrotor on a Moving Platform via Model Predictive Control

Landing on a moving platform is an essential requirement to achieve high-performance autonomous flight with various vehicles, including quadrotors. We propose an efficient and reliable autonomous landing system, based on model predictive control, which can accurately land in the presence of external disturbances. To detect and track the landing marker, a fast two-stage algorithm is introduced in the gimbaled camera, while a model predictive controller with variable sampling time is used to predict and calculate the entire landing trajectory based on the estimated platform information. As the quadrotor approaches the target platform, the sampling time is gradually shortened to feed a re-planning process that perfects the landing trajectory continuously and rapidly, improving the overall accuracy and computing efficiency. At the same time, a cascade incremental nonlinear dynamic inversion control method is adopted to track the planned trajectory and improve robustness against external disturbances. We carried out both simulations and outdoor flight experiments to demonstrate the effectiveness of the proposed landing system. The results show that the quadrotor can land rapidly and accurately even under external disturbance and that the terminal position, speed and attitude satisfy the requirements of a smooth landing mission.

Model-based robust control design and experimental validation of SCARA robot system with uncertainty

In this paper, we design a novel robust control method to reduce the trajectory tracking errors of the SCARA robot with uncertainties including parameters such as uncertainty of the mechanical system and external disturbance, which are time-varying and nonlinear. Then, we propose a deterministic form of the model-based robust control algorithm to deal with the uncertainties. The proposed control algorithm is composed of two parts according to the assumed upper limit of the system uncertainties: one is the traditional proportional-derivative control, and the other is the robust control based on the Lyapunov method, which has the characteristics of model-based and error-based. The stability of the proposed control algorithm is proved by the Lyapunov method theoretically, which shows the system can maintain uniformly bounded and uniformly ultimately bounded. The experimental platform includes the rapid controller prototyping cSPACE, which is designed to reduce programming time and to improve the efficiency of the practical operation. Moreover, we adopt different friction models to investigate the effect of friction on robot performance in robot joints. Finally, numerical simulation and experimental results indicate that the control algorithm proposed in this paper has desired control performance on the SCARA robot.

Boundary stabilization and disturbance rejection for an unstable time fractional diffusion-wave equation

In this paper, we study boundary stabilization and disturbance rejection problem for an unstable time fractional diffusion-wave equation with Caputo time fractional derivative. For the case of no boundary external disturbance, both state feedback control and output feedback control via Neumann boundary actuation are proposed by the classical backstepping method. It is proved that the state feedback makes the closed-loop system Mittag-Leffler stable and the output feedback makes the closed-loop system asymptotically stable. When there is boundary external disturbance, we propose a disturbance estimator constructed by two infinite dimensional auxiliary systems to recover the external disturbance. A novel control law is then designed to compensate for the external disturbance in real time, and rigorous mathematical proofs are presented to show that the resulting closed-loop system is Mittag-Leffler stable and the states of all subsystems involved are uniformly bounded. As a result, we completely resolve, from a theoretical perspective, two long-standing unsolved mathematical control problems raised in [Nonlinear Dynam., 38(2004), 339-354] where all results were verified by simulations only.

Composite Observer-Based Adaptive Dynamic Surface Control for Fractional-Order Nonlinear Systems with Input Saturation

This article proposes an adaptive neural output feedback control scheme in combination with state and disturbance observers for uncertain fractional-order nonlinear systems containing unknown external disturbance, input saturation and immeasurable state. The radial basis function neural network (RBFNN) approximation is used to estimate unknown nonlinear function, and a state observer as well as a fractional-order disturbance observer is developed simultaneously by using the approximation output of the RBFNN to estimate immeasurable states and unknown compounded disturbances, respectively. Then, a fractional-order auxiliary system is constructed to compensate the effects caused by the saturated input. In addition, by introducing a dynamic surface control strategy, the tedious analytic computation of time derivatives of virtual control laws in the conventional backstepping method is avoided. The proposed method guarantees that the boundness of all signals in the closed loop system and the tracking errors converge to a small neighbourhood around the origin. Finally, two examples are provided to verify the effectiveness of the proposed control method.

Robust piecewise adaptive control for an uncertain semilinear parabolic distributed parameter systems

In this study, we focus on designing a robust piecewise adaptive controller to globally asymptotically stabilize a semilinear parabolic distributed parameter systems (DPSs) with external disturbance, whose nonlinearities are bounded by unknown functions. Firstly, a robust piecewise adaptive control is designed against the unknown nonlinearity and the external disturbance. Then, by constructing an appropriate Lyapunov–Krasovskii functional candidate (LKFC) and using the Wiritinger’s inequality and a variant of the Agmon’s inequality, it is shown that the proposed robust piecewise adaptive controller not only ensures the globally asymptotic stability of the closed-loop system, but also guarantees a given performance. Finally, two simulation examples are given to verify the validity of the design method.

Command-Filtered Adaptive Neural Network Backstepping Quantized Control for Fractional-order Nonlinear Systems with Asymmetric Actuator Dead-zone via Disturbance Observer

An adaptive neural network (NN) backstepping quantized control based on command filter and disturbance observer is proposed for fractional-order nonlinear systems with asymmetric actuator dead-zone and unknown external disturbance in this paper. An adaptive NN mechanism is designed to estimate unknown functions, and a command filter is introduced to estimate the virtual control variable as well as its derivative, so the ``explosion of complexity" issue can be avoided existed in the classical backstepping method. To handle the unknown external disturbance, a fractional-order disturbance observer is developed. Moreover, a hysteresis-type quantizer is used to quantify the final input signal to overcome the system performance damage caused by the actuator dead-zone. The quantized input signal can ensure that all the involved signals keep bounded and the tracking error converges to an arbitrarily small region of the origin. Finally, two examples are presented to verify the effectiveness of the proposed method.

Composite nonlinear feedback–based adaptive integral sliding mode control for a servo position control system subject to external disturbance

This paper presents a composite nonlinear feedback–based adaptive integral sliding mode controller with a reaching law (CNF-AISMRL) for fast and accurate control of a servo position control system subject to external disturbance. The proposed controller exploits the advantages of composite nonlinear feedback (CNF) and sliding mode control (SMC) schemes to improve the transient performance and increase the robustness of the closed-loop system. An integral sliding mode combined with a quick reaching law is designed to eliminate the effect of disturbances, which mitigates chattering and achieves finite-time convergence of the sliding mode. An adaptation tuning approach is utilized to deal with unknown but bounded system uncertainties and disturbances. When considering the model uncertainties and disturbances, the stability of the closed-loop system is verified based on the Lyapunov theorem. Numerical simulations are investigated to the effectiveness of the proposed scheme. The transient performance of load disk position to step signal with disturbances using CNF, composite nonlinear feedback based integral sliding mode control (CNF-ISM), and the proposed CNF-AISMRL schemes is given. The simulation results indicate that, without acquiring the knowledge of bounds on system disturbances, the proposed control scheme has superior performance in the presence of time-varying disturbances.

CONDITIONS VULNERABILITY IDENTIFICATION OF NATURAL AND TECHNICAL OBJECTS BASED ON LINEAR SPLINE INTERPOLATION OF INTERFACE TRAFFIC INTENSITY

The method of application of spline interpolation in solving the problems of identification of abnormal states (A-events) in information data flows and classification of the specified events in the control of natural and technical objects (PTO) is considered. The approach is based on the representation of the intensity of interface traffic by piecewise linear splines and implemented using a modeling stand. At the first stage, descriptions are generated and formed in the form of linear splines representing the states of controlled objects, one of which is subject to external disturbance. At the second stage, the generated descriptions of splines are used to assess discrepancies between the studied distributions and the influence of a number of factors on the reliability of decisions made using probabilistic modeling methods in the Anylogic environment.