Trajectory tracking of a quadrotor using extend state observer based U-model enhanced double sliding mode control

This paper develops a novel U-model enhanced double sliding mode controller (UDSMC) for a quadrotor based on multiple-input and multiple-output extended-state-observer (MIMO-ESO). UDSMC is designed using Lyapunov synthesis and Hurwitz stability to not only cancel the complex dynamics and nonlinearity, but also stabilize the uncertainty and external disturbance of the underlying quadrotors. MIMO-ESO is designed to estimate the unmeasurable velocities which can reduce the impact of sensor measurement errors in practice. The difficulties associated with quadrotor velocity's measurement disturbances and uncertain aerodynamics are successfully addressed in this control design. Rigorous theoretical analysis has been carried out to determine whether the proposed control system can achieve stable trajectory tracking performance, and a comparative real-time experimental study has also been carried out to verify the better effectiveness of the proposed control system than the built-in PID control system.     

Adaptive dynamic surface tracking control for uncertain full-state constrained nonlinear systems with disturbance compensation

In this paper, an asymptotic adaptive dynamic surface tracking control strategy is investigated for uncertain full-state constrained nonlinear systems subject to parametric uncertainties and external disturbances. A novel disturbance estimator (DE) is firstly used to compensate for external disturbances. The parametric uncertainties are accordingly handled via a synthesized adaptive law. Then, by using the barrier Lyapunov function (BLF) and dynamic surface control (DSC), an appropriate backstepping design framework employing a novel adaptive-gain nonlinear filter is given, which avoids the “explosion of complexity” and relieves the conservatism of filter gain selection. The theoretical analysis reveals the asymptotic tracking performance is assured with the proposed controller. In the end, some simulation cases demonstrate the validity of the proposed controller.     

High-gain fractional disturbance observer control of uncertain dynamical systems

Disturbance observer-based control allows to compensate unknown inputs, however, in most cases, requiring their integer-order differentiability. In this paper, a novel disturbance observer-based state feedback controller is proposed to compensate a more general class of fractional-, but not necessarily integer-order, differentiable unknown inputs. The proposed fractional PI-like structure yields precise conditions for feedback gain tuning. Remarkably, the resulting controller rejects non-differentiable disturbances with a smooth controller, guaranteeing robustness, an outstanding features for tracking tasks, under a prescribed practical stability regimen. A comparison to a fractional sliding mode observer is conducted via simulations to highlight the reliability of the proposed scheme.     

Composite fuzzy voltage-based command-filtered learning control of electrically-driven robots with input delay using disturbance observer

This paper presents an improved composite fuzzy learning control for uncertain electrically-driven robot manipulators with input delay and the external disturbances. In the framework of the backstepping algorithm, fuzzy systems are employed to approximate the unknown terms where the accuracy of fuzzy learning is also considered by defining prediction errors. With the aid of integral technique and the dynamic surface control, a variable is engendered for the system in such a way that the input-delayed robotic system is converted to the non-delayed robotic system. Besides, the command-filtered control is used to cope with the complexity explosion of the backstepping-based design. In order to improve the robust behavior of the control system, the proposed control scheme is equipped with disturbance observers (DOBs). Different from the previous works, the information of the input-delayed, the compensated error surfaces (obtained from the command-filtered approach), the prediction errors and the disturbance estimations (derived from DOBs) are unified to construct the proposed control framework. The stability of the overall system is verified by the Lyapunov theorem. The efficiency of the proposed concept is illustrated using various simulations for an electrically-driven robot manipulator in the presence of uncertainties.     

Adaptive event-triggering distributed filter of positive Markovian jump systems based on disturbance observer

This paper presents an adaptive event-triggered filter of positive Markovian jump systems based on disturbance observer. A new adaptive event-triggering mechanism is constructed for the systems. A positive disturbance observer is designed for the systems to estimate the disturbance. A distributed output model of each subsystem of positive Markovian jump systems is introduced. Then, an adaptive event-triggering distributed filter is designed by employing stochastic copositive Lyapunov functions. All presented conditions are solvable in terms of linear programming. Under the designed disturbance observer and the distributed filter, the corresponding error system is stochastically stable. The filter design approach is also developed for discrete-time positive Markovian jump systems. The contribution of the paper lies in that: (i) A new adaptive event-triggering mechanism is established for positive systems, (ii) A positive disturbance observer is designed for the disturbance of positive Markovian jump systems, and (iii) The designed distributed filter can guarantee the stochastic stability of the error while existing filters in literature only achieve the stochastic gain stability of the error. Finally, two examples are given to illustrate the effectiveness of the proposed design.     

Adaptive disturbance attenuation for a class of uncertain nonlinear systems: A double-gain nonlinear observer method

This paper is concerned with the problem of adaptive disturbance attenuation for a class of nonlinear systems. The traditional adaptive methods are almost impossible to compensate the time-varying unknown disturbance by designing parameter adaptive laws without a priori knowledge about the bounds of external disturbances. To solve the problem, a new strategy is proposed by constructing an augmented system where the external disturbance is considered as another component of the augmented state vector. Based on this, a double-gain nonlinear observer is employed to estimate the state of the augmented nonlinear system. Further, an output feedback control strategy is designed, and it is proved that the proposed strategy ensures that all the signals are bounded and the tracking error exponentially converges to an adjustable compact set. Finally, an example is performed to demonstrate the validity of the proposed scheme.     

Development of advanced FDD and FTC techniques with application to an unmanned quadrotor helicopter testbed

As the first part, this paper presents an overview on the existing works on fault detection and diagnosis (FDD) and fault-tolerant control (FTC) for unmanned rotorcraft systems. Considered faults include actuator and sensor faults for single and multi-rotor systems. As the second part, several FDD and FTC techniques developed recently at the Networked Autonomous Vehicles Lab of Concordia University are detailed along with experimental application to a unique and newly developed quadrotor helicopter testbed.     

Composite adaptive disturbance observer based control and back-stepping method for nonlinear system with multiple mismatched disturbances

A novel control scheme combining disturbance observer technique and back-stepping method is proposed for a class of nonlinear system with multiple mismatched disturbances. The uncertain multiple mismatched disturbances contain not only single harmonic or constant disturbances but also another unexpected nonlinear signal presented as a nonlinear function. The composite adaptive disturbance observers are designed to estimate the disturbances with partial known information. By integrating disturbance observer based control with back-stepping method, a composite controller is designed. Here, the disturbance estimations are introduced into the design of virtual control laws in each step to compensate the mismatched disturbances. Rigorous stability analysis for the closed-loop system is established by direct Lyapunov function method. It is shown that the system output asymptotically converges to zero in spite of existing multiple mismatched disturbances. Finally, a simulation example is applied to demonstrate the effectiveness of the proposed method.     

利用粒子间相互作用的量子系统的纯度及相干性补偿

研究了混态粒子的纯度补偿以及消相干过程的抵消问题,提出了利用辅助粒子及其与目标粒子的相互作用进行纯度及相干性补偿的方法。给出了2种不同情况——混态目标粒子的纯度补偿和消相干过程的抵消——辅助粒子的充要条件。并通过系统数值仿真实验验证了所设计方法的有效性。     

Adaptive prescribed performance control of QUAVs with unknown time-varying payload and wind gust disturbance

In this paper, a new robust adaptive prescribed performance control (PPC, for short) scheme is proposed for quadrotor UAVs (QUAVs, for short) with unknown time-varying payloads and wind gust disturbances. Under the presented framework, the overall control system is decoupled into translational subsystem and rotational subsystem. These two subsystems are connected to each other through common attitude extraction algorithms. For translational subsystem, a novel robust adaptive PPC strategy is designed based on the sliding mode control technique to provide better trajectory tracking performance and well robustness. For rotational subsystem, a new robust adaptive controller is constructed based on backstepping technique to track the desired attitudes. Finally, the overall system is proved to be stable in the sense of uniform ultimate boundedness, and numerical simulation results are presented to validate the effectiveness of the proposed control scheme.     

漂浮基空间机器人的基于模糊神经网络的自适应补偿控制

针对自由漂浮状态的空间机器人模型不确定性及其动力传动机构的摩擦死区非线性,将一种自适应模糊小脑模型关联控制( FCMAC)补偿策略用于轨迹跟踪及补偿问题.利用模糊神经网络并引入GL矩阵及其乘法算子“.”分别对执行机构中的摩擦死区及系统模型不确定部分进行自适应补偿,其补偿误差及外界扰动通过滑模控制器来消除.基于Lyapunov理论证明了闭环系统跟踪误差的有界性.仿真表明控制器可以达到较高精度,且能满足实时性要求.     

Adaptive control of PLL using neural network

This paper proposes a method for increasing capture range of a phase-locked loop (PLL). The behavior of PLL in the initial condition is nonlinear. This nonlinearity phenomenon is caused by phase detector (PD). A method using neural network as controller in PLL is proposed for overcoming this problem. In the proposed method, the reference frequency and frequency of VCO converted to voltage by FTV. Frequency tracking is achieved by neural network. The circuit has been fabricated in a standard 0.13 μm CMOS process. Simulation results show a tracking range of 110 MHz, and a capture range of 90 MHz while operating from a 2 V supply with maximum 2.18 mW power consumption.     

Composite anti-disturbance control for uncertain Markovian jump systems with actuator saturation based disturbance observer and adaptive neural network

This paper studies the problem of composite control for a class of uncertain Markovian jump systems (MJSs) with partial known transition rates, multiple disturbances and actuator saturation. Compared with the existing results, a novel robust composite control scheme is put forward by virtue of adaptive neural network technique. For MJSs, the partial unknown information on transition rates and the actuator saturation influence the design of disturbance observer and the robust H_∞ controller. Firstly, without taking account of external disturbances, the network reconstruction error and saturation, a novel robust adaptive control strategy is established to ensure that all the signals of the closed-loop system are asymptotically bounded in mean square. Secondly, the solvability condition for ensuring the robust H_∞ performance is given by using a modified adaptive law, where the saturation is treated as a disturbance-like signal. Finally, the simulations for a numerical example and an application example are performed to validate the effectiveness of the proposed results.     

Robust adaptive finite-time stabilization control for a class of nonlinear switched systems based on finite-time disturbance observer

The problem of adaptive global finite-time stabilization control for a class of nonlinear switched systems in the presence of external perturbations and arbitrary switchings has been addressed in this research study. The proposed scheme has been designed based on a finite-time estimation technique in which during the control procedure, unknown imposed perturbations are accurately estimated by means of the designed finite-time disturbance observer (FTDO). Due to the exact estimation of the external disturbances within a given finite time, the encountered complications and adversities from loss of information in the Lyapunov parameter estimation (LPE) methods have been solved which are caused by the persistent switchings in the system. Furthermore, a new solution for the problem of chattering phenomenon in nonlinear switched systems has been presented by utilizing the designed FTDO, which can counteract the malfunctioning responses of the system caused by external disturbances and unmodeled dynamics. In this paper, an acknowledged class of nonlinear switched systems has been taken into account which is in the general form of canonical structure. In addition, the established design strategy is formulated for the control of perturbed nonlinear switched systems with one and only input and assures that the system states through the finite-time convergence characteristic, reach the equilibrium point of origin. Finally, numerical simulations are carried out on a mass-spring-damper (MSD) dynamical system to indicate advantages and superior efficiency of the suggested method.     

商业银行信息技术风险控制

商业银行是现代市场经济中的金融主体，对宏观经济有着举足轻重的影响；而同时也是一台“风险机器”，它承担风险，转移风险，并且还将风险植入其金融产品和服务中再出售风险。因此，商业银行自身抵御风险的能力就显得非常重要。随着中国金融改革的不断深化，商业银行运行的安全性日益受到重视，风险管理成为银行管理工作的重要组成部分。主要探讨在信息化条件下，银行应如何管理信息技术所带来的风险以及中国商业银行的相应对策。     

Adaptive sliding mode trajectory tracking control of robotic airships with parametric uncertainty and wind disturbance

An adaptive sliding mode trajectory tracking controller is developed for fully-actuated robotic airships with parametric uncertainties and unknown wind disturbances. Based on the trajectory tracking model of robotic airships, an adaptive sliding mode control strategy is proposed to ensure the asymptotic convergence of trajectory tracking errors and adaptive estimations. The crucial thinking involves an adaptive scheme for the controller gains to avoid the off-line tuning. Specially, the uncertain physical parameters and unknown wind disturbances are rejected by variable structure control, and boundary layer technique is employed to avoid the undesired control chattering phenomenon. Computer experiments are performed to demonstrate the performance and advantage of the proposed control method.     

Attitude tracking control of a quadrotor UAV in the exponential coordinates

A new approach to control the attitude of a quadrotor UAV in terms of the exponential coordinates is developed in this paper. The exponential coordinate is a minimal representation of the rotation matrix, but it can avoid singularities. Since the quadrotor UAV can be considered as a rigid body aircraft, the analytic closed-form expressions of a rigid body's attitude kinematics are derived from differential of exponential on SO(3). Furthermore, based on the exponential expressions of attitude kinematics, the controller of a fully actuated rigid body is designed using trajectory linearization control method. The overall attitude controller contains two loops, which are designed according to the torque equation and the angular velocity equation respectively. In the numerical simulation, the proposed attitude controller is compared to a controller in the Euler angles, showing that singularities induced by Euler angles are avoided by using exponential coordinates. The robustness test of the attitude controller is also demonstrated in the simulation. The simulation results indicate that the proposed method can be applied to the attitude tracking control of an aerial robot especially when the robot needs to make aggressive maneuverings.