Fixed-time neuroadaptive practical tracking control based on extended state/disturbance observer for a QUAV with external disturbances and time-varying parameters

This paper investigates the fixed-time neural network adaptive (FNNA) tracking control of a quadrotor unmanned aerial vehicle (QUAV) to achieve flight safety and high efficiency. By combining radial basis function neural network (RBFNN) with fixed time adaptive sliding mode algorithm, a novel radial basis function neural network adaptive law is proposed. In addition, an extended state/disturbance observer (ESDO) is proposed to solve the problem of unmeasurable state and external interference, which can obtain reliable state feedback and interference input. Unlike most other ESO applications, this paper does not set the uncertainty model and external disturbances as total disturbances. Instead, the external disturbances are observed by extending the states and the observed states are fed back to the controller to cancel the disturbances. In view of the time-varying resistance coefficient and inertia torque in the QUAV model, the neural network is introduced so that the controller does not need to consider these nonlinear uncertainties. Finally, a numerical example is given to verify the effectiveness of the coupled non-simplified QUAV model.     

Adaptive robust control for planar n-link underactuated manipulator based on radial basis function neural network and online iterative correction method

This paper presents an adaptive robust control strategy based on a radial basis function neural network (RBFNN) and an online iterative correction method (OICM) for a planar n-link underactuated manipulator with a passive first joint to realize its position control objective. An uncertain model of the planar n-link underactuated manipulator is built, which contains the parameter perturbation and the external disturbance. The adaptive robust controllers based on the RBFNN are designed to realize the model reduction, which makes the system reduce to a planar virtual three-link underactuated manipulator (PVTUM) and simplifies the complexity of the system control. An online differential evolution (DE) algorithm is used to calculate the target angles of the PVTUM based on the nominal model parameters. The control of the PVTUM is divided into two stages, and the adaptive robust controllers are still employed to realize the control objective of each stage. Then, the OICM is used to correct the deviations of all link angles of the PVTUM caused by the parameter perturbation, which makes the end-point of the system gradually approach to its target position. Finally, simulation results of a planar four-link underactuated manipulator demonstrate the effectiveness of the proposed adaptive robust control strategy.     

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.     

Spacecraft output feedback attitude control based on extended state observer and adaptive dynamic programming

This paper investigates spacecraft output feedback attitude control problem based on extended state observer (ESO) and adaptive dynamic programming (ADP) approach. For the plant described by the unit quaternion, an ESO is first presented in view of the property of the attitude motion, and the norm constraint on the unit quaternion can be satisfied theoretically. The practical convergence proof of the developed ESO is illustrated by change of coordinates. Then, the controller is designed with an involvement of two parts: the basic part and the supplementary part. For the basic part, a proportional-derivative control law is designed. For the supplementary part, an ADP method called action-dependent heuristic dynamic programming (ADHDP) is adopted, which provides a supplementary control action according to the differences between the actual and the desired system signals. Simulation studies validate the effectiveness of the proposed scheme.     

Distributed consensus tracking control based on state and disturbance observations for mixed-order multi-agent mechanical systems

In this paper, we study the cooperative consensus control problem of mixed-order (also called hybrid-order) multi-agent mechanical systems (MMSs) under the condition of unmeasurable state, unknown disturbance and constrained control input. Here, the controlled mixed-order MMSs are consisted of the mechanical agents having heterogeneous nonlinear dynamics and even non-identical orders, which means that the agents can be of different types and their states to be synchronized can be not exactly the same. In order to achieve the ultimate synchronization of all mixed-order followers, we present a novel distributed adaptive tracking control protocol based on the state and disturbance observations. Wherein, a distributed state observer is used to estimate the followers’ and their neighbors’ unmeasurable states. And, a novel estimated-state-based disturbance observer (DOB) is proposed to reduce the effect of unknown lumped disturbance for the mixed-order MMSs. The proposed control protocol and observers are fully distributed and can be calculated for each follower locally. Lyapunov theory is used for proving the stability of the proposed control algorithm and the convergence of the cooperative tracking errors. A practical cooperative longitudinal landing control example of unmanned aerial vehicles (UAVs) is given to illustrate the effectiveness of the presented control protocol.     

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.     

Real time observer and control scheme for a wind turbine system based on a high order sliding modes

The introduction of advanced control algorithms may improve considerably the efficiency of wind turbine systems. This work proposes a high order sliding mode (HOSM) control scheme based on the super twisting algorithm for regulating the wind turbine speed in order to obtain the maximum power from the wind. A robust aerodynamic torque observer, also based on the super twisting algorithm, is included in the control scheme in order to avoid the use of wind speed sensors. The presented robust control scheme ensures good performance under system uncertainties avoiding the chattering problem, which may appear in traditional sliding mode control schemes. The stability analysis of the proposed HOSM observer is provided by means of the Lyapunov stability theory. Experimental results show that the proposed control scheme, based on HOSM controller and observer, provides good performance and that this scheme is robust with respect to system uncertainties and external disturbances.     

Analysis and experimental investigation into a finite time current observer based adaptive backstepping control of buck converters

In this paper, the issue of output voltage regulation in buck type dc-dc converters is addressed using a current sensorless control technique. The proposed strategy integrates a finite time current observer with an adaptive backstepping control scheme to yield a cost-effective and robust control mechanism. The overall controller stability in the sense of Lyapunov is proved. Applicability of the proposed control is verified experimentally on a buck converter in the laboratory. The control scheme is implemented on dSPACE DS1103 platform based on DSP TM320F240 processor. To examine the efficacy of the proposed method, the buck converter is subjected to a wide change in input voltage, load resistance and reference voltage. For comparison purpose, a conventional adaptive backstepping control scheme is evaluated under identical conditions of experimental study to examine the merit of the proposed control. The results obtained reveal that the proposed control is prompt in rejecting perturbations and achieves a smooth, reliable and satisfactory output voltage regulation with faithful and time bound estimation of inductor current. Thereby, this investigation demonstrates the validity of the proposed control in maintaining a stringent output voltage regulation in buck converters.     

Adaptive fuzzy asymptotically tracking control of full state constrained nonlinear system based on a novel Nussbaum-type function

This study carries out the problem of adaptive backstepping fuzzy tracking control for a class of full state constrained uncertain nonlinear system with unknown control directions. Based on Nussbaum-type functions and tan-type Barrier Lyapunov functions, a novel adaptive fuzzy tracking controller is proposed to guarantee that the system output tracking error asymptotically converges to zero, while the constraints on the states of system will not be violated during operation. Compared with the existing results, a better convergence effect is obtained for this class of systems. Stability analysis of the proposed closed-loop control system is supported by the Lyapunov stability theory. Finally, a simulation example is presented to illustrate the effectiveness of the proposed control strategy.     

Improving the performance of networked control systems with time delay and data dropouts based on fuzzy model predictive control

This paper proposes a fuzzy model predictive control (FMPC) combined with the modified Smith predictor for networked control systems (NCSs). The network delays and data dropouts are problems, which greatly reduce the controller performance. For the proposed controller, the model of the controlled system is identified on-line using the Takagi – Sugeno (T-S) fuzzy models based on the Lyapunov function. There are two internal loops in the proposed structure. The first is the loop around the FMPC, which predicts the future outputs. The other is the loop around the plant to give the error between the system model and the actual plant. The proposed controller is designed for controlling a DC servo system through a wireless network to improve the system response. The practical results based on MATLAB/SIMULINK are established. The practical results are indicated that the proposed controller is able to respond the networked time delay and data dropouts compared to other controllers.     

基于北部湾城市群下科技服务产业空间分工及其对外功能的测度研究

本文采用产值密度、专业化指数和相对专业化指数等指标测度北部湾城市群科技服务业集聚程度、科技服务业细分行业专业化及其优势行业分布状况,并以城市流强度指标对北部湾城市群对外功能进行测度。 总体而言,北部湾城市群科技服务业集聚程度处于偏低状态,北部湾城市群科技服务业专业化水平得到初步显示;北部湾城市群各城市科技服务业的优势行业表现为差异化态势;北部湾城市群各个城市的外向功能差异较大,并将其分为三个层级。最后提出促进北部湾城市群科技服务业协调发展政策建议。     

Synchronization of chaotic neural networks with time delay in the leakage term and parametric uncertainties based on sampled-data control

This paper considers the synchronization problem of coupled chaotic neural networks with time delay in the leakage term and parametric uncertainties using sampled-data control. Motivated by the achievements from both the stability of neural networks with time delay in the leakage term and the synchronization issue of coupled chaotic neural networks with parametric uncertainties, Lyapunov stability theory combining with linear matrix inequalities is employed to derive sufficient criteria ensuring the coupled chaotic neural networks to be completely synchronous. This paper presents an illustrative example and uses simulated results of this example to show the feasibility and effectiveness of the proposed sampled-data controller.