Showing papers in "IEEE Transactions on Industrial Electronics in 2017"

Model Predictive Control for Power Converters and Drives: Advances and Trends

Abstract: Model predictive control (MPC) is a very attractive solution for controlling power electronic converters. The aim of this paper is to present and discuss the latest developments in MPC for power converters and drives, describing the current state of this control strategy and analyzing the new trends and challenges it presents when applied to power electronic systems. The paper revisits the operating principle of MPC and identifies three key elements in the MPC strategies, namely the prediction model, the cost function, and the optimization algorithm. This paper summarizes the most recent research concerning these elements, providing details about the different solutions proposed by the academic and industrial communities.

Review of Silicon Carbide Power Devices and Their Applications

Abstract: Silicon carbide (SiC) power devices have been investigated extensively in the past two decades, and there are many devices commercially available now. Owing to the intrinsic material advantages of SiC over silicon (Si), SiC power devices can operate at higher voltage, higher switching frequency, and higher temperature. This paper reviews the technology progress of SiC power devices and their emerging applications. The design challenges and future trends are summarized at the end of the paper.

Recent Advances in Consensus of Multi-Agent Systems: A Brief Survey

Abstract: In this paper, we mainly review the topics in consensus and coordination of multi-agent systems, which have received a tremendous surge of interest and progressed rapidly in the past few years. Focusing on different kinds of constraints on the controller and the self-dynamics of each individual agent, as well as the coordination schemes, we categorize the recent results into the following directions: consensus with constraints, event-based consensus, consensus over signed networks, and consensus of heterogeneous agents. We also review some applications of the very well developed consensus algorithms to the topics such as economic dispatch problem in smart grid and k -means clustering algorithms.

Time-Varying Formation Tracking for Second-Order Multi-Agent Systems Subjected to Switching Topologies With Application to Quadrotor Formation Flying

Abstract: Time-varying formation tracking analysis and design problems for second-order Multi-Agent systems with switching interaction topologies are studied, where the states of the followers form a predefined time-varying formation while tracking the state of the leader. A formation tracking protocol is constructed based on the relative information of the neighboring agents. Necessary and sufficient conditions for Multi-Agent systems with switching interaction topologies to achieve time-varying formation tracking are proposed together with the formation tracking feasibility constraint based on the graph theory. An approach to design the formation tracking protocol is proposed by solving an algebraic Riccati equation, and the stability of the proposed approach is proved using the common Lyapunov stability theory. The obtained results are applied to solve the target enclosing problem of a multiquadrotor unmanned aerial vehicle (UAV) system consisting of one leader (target) quadrotor UAV and three follower quadrotor UAVs. A numerical simulation and an outdoor experiment are presented to demonstrate the effectiveness of the theoretical results.

Deep Model Based Domain Adaptation for Fault Diagnosis

Abstract: In recent years, machine learning techniques have been widely used to solve many problems for fault diagnosis. However, in many real-world fault diagnosis applications, the distribution of the source domain data (on which the model is trained) is different from the distribution of the target domain data (where the learned model is actually deployed), which leads to performance degradation. In this paper, we introduce domain adaptation, which can find the solution to this problem by adapting the classifier or the regression model trained in a source domain for use in a different but related target domain. In particular, we proposed a novel deep neural network model with domain adaptation for fault diagnosis. Two main contributions are concluded by comparing to the previous works: first, the proposed model can utilize domain adaptation meanwhile strengthening the representative information of the original data, so that a high classification accuracy in the target domain can be achieved, and second, we proposed several strategies to explore the optimal hyperparameters of the model. Experimental results, on several real-world datasets, demonstrate the effectiveness and the reliability of both the proposed model and the exploring strategies for the parameters.

Distributed Formation Control of Networked Multi-Agent Systems Using a Dynamic Event-Triggered Communication Mechanism

Abstract: This paper addresses the distributed formation control problem of a networked multi-agent system (MAS) subject to limited communication resources. First, a dynamic event-triggered communication mechanism (DECM) is developed to schedule inter-agent communication such that some unnecessary data exchanges among agents can be reduced so as to achieve better resource efficiency. Different from most of the existing event-triggered communication mechanisms, wherein threshold parameters are fixed all the time, the threshold parameter in the developed event triggering condition is dynamically adjustable in accordance with a dynamic rule. It is numerically shown that the proposed DECM can achieve a better tradeoff between reducing inter-agent communication frequency and preserving an expected formation than some existing ones. Second, an event-triggered formation protocol is delicately proposed by using only locally triggered sampled data in a distributed manner. Based on the formation protocol, it is shown that the state formation control problem is cast into an asymptotic stability problem of a reduced-order closed-loop system. Then, criteria for designing desired formation protocol and communication mechanism are derived. Finally, the effectiveness and advantages of the proposed approach are demonstrated through a comparative study in multirobot formation control.

Disturbance/Uncertainty Estimation and Attenuation Techniques in PMSM Drives—A Survey

Abstract: This paper gives a comprehensive overview on disturbance/uncertainty estimation and attenuation (DUEA) techniques in permanent-magnet synchronous motor (PMSM) drives. Various disturbances and uncertainties in PMSM and also other alternating current (ac) motor drives are first reviewed which shows they have different behaviors and appear in different control loops of the system. The existing DUEA and other relevant control methods in handling disturbances and uncertainties widely used in PMSM drives, and their latest developments are then discussed and summarized. It also provides in-depth analysis of the relationship between these advanced control methods in the context of PMSM systems. When dealing with uncertainties, it is shown that DUEA has a different but complementary mechanism to widely used robust control and adaptive control. The similarities and differences in disturbance attenuation of DUEA and other promising methods such as internal model control and output regulation theory have been analyzed in detail. The wide applications of these methods in different ac motor drives (in particular in PMSM drives) are categorized and summarized. Finally, the paper ends with the discussion on future directions in this area.

Extended State Observer-Based Sliding-Mode Control for Three-Phase Power Converters

Abstract: This paper proposes an extended state observer (ESO) based second-order sliding-mode (SOSM) control for three-phase two-level grid-connected power converters. The proposed control technique forces the input currents to track the desired values, which can indirectly regulate the output voltage while achieving a user-defined power factor. The presented approach has two control loops. A current control loop based on an SOSM and a dc-link voltage regulation loop which consists of an ESO plus SOSM. In this work, the load connected to the dc-link capacitor is considered as an external disturbance. An ESO is used to asymptotically reject this external disturbance. Therefore, its design is considered in the control law derivation to achieve a high performance. Theoretical analysis is given to show the closed-loop behavior of the proposed controller and experimental results are presented to validate the control algorithm under a real power converter prototype.

General Airgap Field Modulation Theory for Electrical Machines

Abstract: This paper proposes a general field modulation theory for electrical machines by introducing magnetomotive force modulation operator to characterize the influence of short-circuited coil, variable reluctance, and flux guide on the primitive magnetizing magnetomotive force distribution established by field winding function multiplied by field current along the airgap peripheral. Magnetically anisotropic stator and rotor behave like modulators to produce a spectrum of field harmonics and the armature winding plays the role of a spatial filter to extract effective field harmonics to contribute the corresponding flux linkage and induce the electromotive force. The developed field modulation theory not only unifies the principle analysis of a large variety of electrical machines, including conventional dc machine, induction machine, and synchronous machine which are just special cases of the general field modulated machines, thus eliminating the problem of the machine theory fragmentation, but also provides a powerful guidance for inventing new machine topologies.

Synchroextracting Transform

Abstract: In this paper, we introduce a new time-frequency (TF) analysis (TFA) method to study the trend and instantaneous frequency (IF) of nonlinear and nonstationary data. Our proposed method is termed the synchroextracting transform (SET), which belongs to a postprocessing procedure of the short-time Fourier transform (STFT). Compared with classical TFA methods, the proposed method can generate a more energy concentrated TF representation and allow for signal reconstruction. The proposed SET method is inspired by the recently proposed synchrosqueezing transform (SST) and the theory of the ideal TFA. To analyze a signal, it is important to obtain the time-varying information, such as the IF and instantaneous amplitude. The SST is to squeeze all TF coefficients into the IF trajectory. Differ from the squeezing manner of SST, the main idea of SET is to only retain the TF information of STFT results most related to time-varying features of the signal and to remove most smeared TF energy, such that the energy concentration of the novel TF representation can be enhanced greatly. Numerical and real-world signals are employed to validate the effectiveness of the SET method.

High-Efficiency High-Power-Density LLC Converter With an Integrated Planar Matrix Transformer for High-Output Current Applications

Abstract: Isolated high-output current DC/DC converters are critical for future data center power architecture. LLC converters with matrix transformer are suitable for these applications due to its high efficiency and high power density. Different matrix transformer structures are investigated in this paper. To improve the current design practice, a high-frequency transformer loss model is developed and a detailed design methodology is proposed. Moreover, a novel matrix transformer structure is proposed to integrate four elemental transformers into one magnetic core with simple four-layer print circuit board windings implementation and further reduced core loss. By pushing switching frequency up to megahertz with GaN devices, the proposed matrix transformer can achieve high efficiency, high power density, and automatic manufacturing for magnetic components. A 1-MHz 380 V/12 V 800-W LLC converter with GaN devices is demonstrated. The prototype achieves a peak efficiency of 97.6% and a power density of 900 W/in3.

Manipulability Optimization of Redundant Manipulators Using Dynamic Neural Networks

Abstract: For solving the singularity problem arising in the control of manipulators, an efficient way is to maximize its manipulability. However, it is challenging to optimize manipulability effectively because it is a nonconvex function to the joint angles of a robotic arm. In addition, the involvement of an inversion operation in the expression of manipulability makes it even hard for timely optimization due to the intensively computational burden for matrix inversion. In this paper, we make progress on real-time manipulability optimization by establishing a dynamic neural network for recurrent calculation of manipulability-maximal control actions for redundant manipulators under physical constraints in an inverse-free manner. By expressing position tracking and matrix inversion as equality constraints, physical limits as inequality constraints, and velocity-level manipulability measure, which is affine to the joint velocities, as the objective function, the manipulability optimization scheme is further formulated as a constrained quadratic program. Then, a dynamic neural network with rigorously provable convergence is constructed to solve such a problem online. Computer simulations are conducted and show that, compared to the existing methods, the proposed scheme can raise the manipulability almost 40% on average, which substantiates the efficacy, accuracy, and superiority of the proposed manipulability optimization scheme.

Data-Driven Optimal Consensus Control for Discrete-Time Multi-Agent Systems With Unknown Dynamics Using Reinforcement Learning Method

Abstract: This paper investigates the optimal consensus control problem for discrete-time multi-agent systems with completely unknown dynamics by utilizing a data-driven reinforcement learning method. It is known that the optimal consensus control for multi-agent systems relies on the solution of the coupled Hamilton–Jacobi–Bellman equation, which is generally impossible to be solved analytically. Even worse, most real-world systems are too complicated to obtain accurate mathematical models. To overcome these deficiencies, a data-based adaptive dynamic programming method is presented using the current and past system data rather than the accurate system models also instead of the traditional identification scheme which would cause the approximation residual errors. First, we establish a discounted performance index and formulate the optimal consensus problem via Bellman optimality principle. Then, we introduce the policy iteration algorithm which motivates this paper. To implement the proposed online action-dependent heuristic dynamic programming method, two neural networks (NNs), 1) critic NN and 2) actor NN, are employed to approximate the iterative performance index functions and control policies, respectively, in real time. Finally, two simulation examples are provided to demonstrate the effectiveness of the proposed method.

Active Disturbance Rejection Adaptive Control of Hydraulic Servo Systems

Abstract: This paper presents an active disturbance rejection adaptive control scheme via full state feedback for motion control of hydraulic servo systems subjected to both parametric uncertainties and uncertain nonlinearities. The proposed controller is derived by effectively integrating adaptive control with extended state observer via backstepping method. The adaptive law is synthesized to handle parametric uncertainties and the remaining uncertainties are estimated by the extended state observer and then compensated in a feedforward way. The unique features of the proposed controller are that not only the matched uncertainties but also unmatched uncertainties are estimated by constructing two extended state observers, and the parameter adaptation law is driven by both tracking errors and state estimation errors. Since the majority of parametric uncertainties can be reduced by the parameter adaptation, the task of the extended state observer is much alleviated. Consequently, high-gain feedback is avoided and improved tracking performance can be expected. The proposed controller theoretically achieves an asymptotic tracking performance in the presence of parametric uncertainties and constant disturbances. In addition, prescribed transient tracking performance and final tracking accuracy can also be guaranteed when existing time-variant uncertain nonlinearities. Comparative experimental results are obtained to verify the high tracking performance nature of the proposed control strategy.

Direct Remaining Useful Life Estimation Based on Support Vector Regression

Abstract: Prognostics is a major activity in the field of prognostics and health management It aims at increasing the reliability and safety of systems while reducing the maintenance cost by providing an estimate of the current health status and remaining useful life (RUL) Classical RUL estimation techniques are usually composed of different steps: estimations of a health indicator, degradation states, a failure threshold, and finally the RUL In this work, a procedure that is able to estimate the RUL of equipment directly from sensor values without the need for estimating degradation states or a failure threshold is developed A direct relation between sensor values or health indicators is modeled using a support vector regression Using this procedure, the RUL can be estimated at any time instant of the degradation process In addition, an offline wrapper variable selection is applied before training the prediction model This step has a positive impact on the accuracy of the prediction while reducing the computational time compared to existing indirect RUL prediction methods To assess the performance of the proposed approach, the Turbofan dataset, widely considered in the literature, is used Experimental results show that the performance of the proposed method is competitive with other existing approaches

An Overview of Dynamic-Linearization-Based Data-Driven Control and Applications

Abstract: A brief overview on the model-based control and data-driven control methods is presented. The data-driven equivalent dynamic linearization, as a foundational analysis tool of data-driven control methods for discrete-time nonlinear systems, is introduced in detail with motivations and distinct features. The prototype model-free adaptive control schemes by using the dynamic linearization to an unknown nonlinear plant model, as well as the alternative model-free adaptive control methods by using the dynamic linearization to an unknown ideal nonlinear controller, are discussed. Furthermore, the extensions of the dynamic linearization to unknown nonlinear repetitive systems and the corresponding model-free adaptive iterative learning control methods are also overviewed and summarized. This work highlights the characteristics and comments of the different model-free adaptive control schemes in detail to facilitate the understanding of the readers. Finally, some perspectives on data-driven control methods in information-rich age are given.

Damping Methods for Resonances Caused by LCL-Filter-Based Current-Controlled Grid-Tied Power Inverters: An Overview

Abstract: Grid-tied voltage source inverters using LCL filter have been widely adopted in distributed power generation systems (DPGSs). As high-order LCL filters contain multiple resonant frequencies, switching harmonics generated by the inverter and current harmonics generated by the active/passive loads would cause the system resonance, and thus the output current distortion and oscillation. Such phenomenon is particularly critical when the power grid is weak with the unknown grid impedance. In order to stabilize the operation of the DPGS and improve the waveform of the injected currents, many innovative damping methods have been proposed. A comprehensive overview on those contributions and their classification on the inverter- and grid-side damping measures are presented. Based on the concept of the impedance-based stability analysis, all damping methods can ensure the system stability by modifying the effective output impedance of the inverter or the effective grid impedance. Classical damping methods for industrial applications will be analyzed and compared. Finally, the future trends of the impedance-based stability analysis, as well as some promising damping methods, will be discussed.

Sliding Mode Direct Yaw-Moment Control Design for In-Wheel Electric Vehicles

Abstract: The direct yaw-moment control system can significantly enhance vehicle stability in critical situations. In this paper, the direct yaw-moment control strategies are proposed for in-wheel electric vehicles by using sliding mode (SM) and nonlinear disturbance observer (NDOB) techniques. The ideal sideslip angle at the center of gravity and the yaw rate are first calculated based on a linear two degree of freedom vehicle model. Then, the actual sideslip angle is identified and estimated by constructing a state observer. On this basis, a traditional discontinuous SM direct yaw-moment controller is designed to guarantee that the sideslip angle and the yaw rate will approach the ideal ones as closely as possible. To tackle the chattering problem existing in the traditional SM controller, a second-order sliding mode (SOSM) controller is further designed by taking the derivative of the controller as the new control, which implies that the actual control can be an integration of the SOSM controller. Finally, to avoid the large gains in the derived controllers, by combining the NDOB with the derived controllers, the composite control schemes are also proposed. In comparison with the discontinuous first-order SM controller, the proposed SOSM controller is shown to be more effective.

Distributed Containment Maneuvering of Multiple Marine Vessels via Neurodynamics-Based Output Feedback

Abstract: In this paper, a neurodynamics-based output feedback scheme is proposed for distributed containment maneuvering of marine vessels guided by multiple parameterized paths without using velocity measurements. Each vessel is subject to internal model uncertainties and external disturbances induced by wind, waves, and ocean currents. In order to recover unmeasured velocity information as well as to identify unknown vessel dynamics, an echo state network (ESN) based observer using recorded input–output data is proposed for each vessel. Based on the observed velocity information of neighboring vessels, distributed containment maneuvering laws are developed at the kinematic level. Next, in order to shape the transient motion profile for vessel kinetics to follow, finite-time nonlinear tracking differentiators are employed to generate smooth reference signals as well as to extract the time derivatives of kinematic control laws. Finally, ESN-based dynamic control laws are constructed at the kinetic level. The stability of the closed-loop system is analyzed via input-to-state stability and cascade theory. Simulation results are provided to illustrate the efficacy of the proposed neurodynamics-based output feedback approach.

A Decentralized Dynamic Power Sharing Strategy for Hybrid Energy Storage System in Autonomous DC Microgrid

Abstract: Power allocation is a major concern in hybrid energy storage system. This paper proposes an extended droop control (EDC) strategy to achieve dynamic current sharing autonomously during sudden load change and resource variations. The proposed method consists of a virtual resistance droop controller and a virtual capacitance droop controller for energy storages with complementary characteristics, such as battery and supercapacitor (SC). By using this method, battery provides consistent power and SC only compensates high-frequency fluctuations without the involvement of conventionally used centralized controllers. To implement the proposed EDC method, a detailed design procedure is proposed to achieve the control objectives of stable operation, voltage regulation, and dynamic current sharing. System dynamic model and relevant impedances are derived and detailed frequency domain analysis is performed. Moreover, the system level stability analysis is investigated and system expansion with the proposed method is illustrated. Both simulations and experiments are conducted to validate the effectiveness of the proposed control strategy and analytical results.

Deadbeat Model-Predictive Torque Control With Discrete Space-Vector Modulation for PMSM Drives

Abstract: This paper proposes an alternative strategy of finite-control-set model-predictive torque control (MPTC) to reduce the computational burden and the torque ripple and decouple the switching frequency from the controller sampling time. An improved discrete space-vector modulation (DSVM) technique is utilized to synthesize a large number of virtual voltage vectors. The deadbeat (DB) technique is used to optimize the voltage vector selection process, avoiding enumerating all the feasible voltage vectors. With this proposed method, only three voltage vectors are tested in each predictive step. Based on the improved DSVM method, the three candidate voltage vectors are calculated by using a novel algebraic way. This new strategy has the benefits of both the MPTC method and the DB method. The effectiveness of the proposed strategy is validated based on a test bench.

Adaptive Impedance Control for an Upper Limb Robotic Exoskeleton Using Biological Signals

Abstract: This paper presents adaptive impedance control of an upper limb robotic exoskeleton using biological signals. First, we develop a reference musculoskeletal model of the human upper limb and experimentally calibrate the model to match the operator’s motion behavior. Then, the proposed novel impedance algorithm transfers stiffness from human operator through the surface electromyography (sEMG) signals, being utilized to design the optimal reference impedance model. Considering the unknown deadzone effects in the robot joints and the absence of the precise knowledge of the robot’s dynamics, an adaptive neural network control incorporating with a high-gain observer is developed to approximate the deadzone effect and robot’s dynamics and drive the robot tracking desired trajectories without velocity measurements. In order to verify the robustness of the proposed approach, the actual implementation has been performed using a real robotic exoskeleton and a human operator.

Grey Wolf Optimizer Algorithm-Based Tuning of Fuzzy Control Systems With Reduced Parametric Sensitivity

Abstract: This paper proposes an innovative tuning approach for fuzzy control systems (CSs) with a reduced parametric sensitivity using the Grey Wolf Optimizer (GWO) algorithm. The CSs consist of servo system processes controlled by Takagi–Sugeno–Kang proportional-integral fuzzy controllers (TSK PI-FCs). The process models have second-order dynamics with an integral component, variable parameters, a saturation, and dead-zone static nonlinearity. The sensitivity analysis employs output sensitivity functions of the sensitivity models defined with respect to the parametric variations of the processes. The GWO algorithm is used in solving the optimization problems, where the objective functions include the output sensitivity functions. GWO's motivation is based on its low-computational cost. The tuning approach is validated in an experimental case study of a position control for a laboratory nonlinear servo system, and TSK PI-FCs with a reduced process small time constant sensitivity are offered.

A Fast Reliable Image Quality Predictor by Fusing Micro- and Macro-Structures

Abstract: A fast reliable computational quality predictor is eagerly desired in practical image/video applications, such as serving for the quality monitoring of real-time coding and transcoding. In this paper, we propose a new perceptual image quality assessment (IQA) metric based on the human visual system (HVS). The proposed IQA model performs efficiently with convolution operations at multiscales, gradient magnitude, and color information similarity, and a perceptual-based pooling. Extensive experiments are conducted using four popular large-size image databases and two multiply distorted image databases, and results validate the superiority of our approach over modern IQA measures in efficiency and efficacy. Our metric is built on the theoretical support of the HVS with lately designed IQA methods as special cases.

New Perspectives on Droop Control in AC Microgrid

Abstract: Virtual impedance, angle droop, and frequency droop control play important roles in maintaining system stability, and load sharing among distributed generators (DGs) in microgrid. These approaches have been developed into three totally independent concepts, but a strong correlation exists. In this letter, their similarities and differences are revealed. Some new findings are established as follows: 1) the angle droop control is intrinsically a virtual inductance method; 2) virtual inductance method can also be regarded as a special frequency droop control with a power derivative feedback; and 3) the combination of virtual inductance method and frequency droop control is equivalent to the proportional–derivative type frequency droop, which is introduced to enhance the power oscillation damping. These relationships provide new insights into the design of the control methods for DGs in microgrid.

Fixed-Time Leader-Following Consensus for Second-Order Multiagent Systems With Input Delay

Abstract: This paper studies fixed-time leader-following lag consensus problem of second-order multiagent systems with input delay. Using fixed-time distributed observer, we obtain the leader's states for each followers. An extension of the Artstein's reducing transformation is employed to transform the delayed error system into a second-order system without time delay and a novel nonsingular terminal sliding mode protocol is proposed to achieve fixed-time consensus. The presented sliding mode controller can avoid singularity, eliminate chattering, and achieve exact convergence. It is mathematically proved that the presented protocol can achieve exact fixed-time leader-following lag consensus. Moreover, the upper bound of convergence time only depends on observer parameters, controller parameters, network parameters, and delay time, which makes it possible to determine the convergence time offline regardless of initial condition. The presented protocol is applied to coordinated lag tracking control of single-link robotic manipulators and the results validate the effectiveness of the proposed fixed-time protocol.

Soft Magnetic Material Status and Trends in Electric Machines

Abstract: This paper gives an overview on the history and trends of magnetic materials used in electrical machines and motors. The presented materials include silicon–iron, nickel–iron, and cobalt–iron lamination steels, as well as amorphous and nanocrystalline magnetic materials and soft magnetic composites. Development trends and current usage of these selected materials are presented, giving an outlook on the new magnetic material research with regard to electrical machine applications.

Corrections to “Extended State Observer-Based Integral Sliding Mode Control for an Underwater Robot With Unknown Disturbances and Uncertain Nonlinearities”

Abstract: This paper develops a novel integral sliding mode controller (ISMC) for a general type of underwater robots based on multiple-input and multiple-output extended-state-observer (MIMO-ESO). The difficulties associated with the unmeasured velocities, unknown disturbances, and uncertain hydrodynamics of the robot have been successfully solved in the control design. An adaptive MIMO-ESO is designed not only to estimate the unmeasurable linear and angular velocities, but also to estimate the unknown external disturbances. An ISMC is then designed using Lyapunov synthesis, and an adaptive gain update algorithm is introduced to estimate the upper bound of the uncertainties. Rigorous theoretical analysis is performed to show that the proposed control method is able to achieve asymptotical tracking performance for the underwater robot. Experimental studies are also carried out to validate the effectiveness of the proposed control, and to show that the proposed approach performs better than a conventional potential difference (PD) control approach.

DOB-Based Neural Control of Flexible Hypersonic Flight Vehicle Considering Wind Effects

Abstract: This paper investigates the disturbance observer (DOB)-based neural adaptive control on the longitudinal dynamics of a flexible hypersonic flight vehicle (HFV) in the presence of wind effects The coupling effect between flexible states and rigid body, and the accessional angle of attack (AOA) due to wind, is modeled as unknown disturbance, where the nonlinear DOB is constructed using the neural approximation For the weight update in neural networks (NNs), a novel algorithm is proposed with the additional prediction error derived from the serial–parallel estimation model (SPEM) using both neural approximation and disturbance estimation Different from previous work, the wind effect is taken into the hypersonic flight dynamics for realistic analysis, and the novel controller is designed using compound estimation, where the NN and the DOB are constructed to deal with aerodynamic uncertainty and unknown disturbance Simulation studies of a flexible HFV with wind effects show that the proposed controller can achieve high tracking accuracy, while the compound estimation can closely follow the system uncertainty with fast convergence

Adaptive Integral Sliding Mode Control With Time-Delay Estimation for Robot Manipulators

Abstract: This paper presents an adaptive robust controller for robot manipulators using adaptive integral sliding mode control and time-delay estimation (TDE). The proposed controller employs TDE to estimate robot dynamics with uncertainties such as parameter variations and disturbances, an integral sliding surface to eliminate the reaching phase together with the noise-sensitive switching action used in the conventional sliding mode control, and adaptation gain dynamics to achieve the applicable high accuracy. Experimental studies using a programmable universal machine for assembly-type industrial robot manipulator are conducted to verify the effectiveness of the proposed control. The proposed controller is robust, chattering free, and highly accurate.