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This paper aims to present a sliding mode control method based on disturbance observer (DO) for improving the reaching law of permanent magnet synchronous motor (PMSM).

Aiming at the insufficiency of the traditional exponential reaching law used in sliding mode variable structure control, an exponential reaching law related to the speed error is proposed. The improved exponential reaching law can adaptively adjust the size of the constant velocity term in the reaching law according to the size of the speed error, so as to adaptively adjust the speed of the system approaching the sliding mode surface to overcome the control deviation and improve the dynamic and steady state performance. To improve the anti-interference ability of the system, a DO is proposed to observe the external disturbance of the system, and the observed value is used to compensate the system. The stability of the system is analyzed by Lyapunov theorem. The effectiveness of this method is proved by simulation and experiment.

Simulation and experiment show that the proposed method has the advantages of fast response and strong anti-interference ability.

The proposed method cannot observe the disturbance caused by the change of internal parameters of the system.

A sliding mode control method for PMSM is proposed, which has good control performance. The proposed method can effectively suppress chattering, ensure fast response speed and have strong anti-interference ability. The effectiveness of the algorithm is verified by simulation and experiment.

The purpose of this paper is to overcome the shortcoming that discrete variable structure control (VSC) system trajectory oscillates in a neighborhood of the origin.

Among all the proposed reaching laws, W. Gao's theory is most perfect. It makes great progress in revealing the motion mechanism of discrete‐time VSC systems. However, it has an obvious defect, i.e. the system trajectory oscillates in a neighborhood of the origin rather than converges to the origin. So, a new reaching law named variable rate reaching law to which the stability at the origin can be expected is proposed. The special feature of this new reaching law is that it is directly proportional to the norm of the state vector and can result in a sector‐shaped switching region. On the basis of analyzing the characteristic of the variable rate and the conventional reaching laws, a new combined control algorithm that discards the shortcomings of the two reaching laws and carries on their merits is formed, so satisfactory control performance can be achieved.

A new combined reaching law control algorithm, which uses the exponential rate reaching law in the reaching mode and in the front phase of the sliding mode, and uses the variable rate reaching law in the back phase of the sliding mode and in the steady‐state mode, is formed.

The paper is a very useful reference for control system designers.

The new control strategy is applied to the controller design for a brushless DC servomotor and good control performance is obtained.

The purpose of this paper is to design a hybrid robust tracking controller based on an improved radial basis function artificial neural network (IRBFANN) and a novel extended-state observer for a quadrotor system with various model and parametric uncertainties and external disturbances to enhance the resiliency of the control system.

An IRBFANN is introduced as an adaptive compensator tool for model and parametric uncertainties in the control algorithm of non-singular rapid terminal sliding-mode control (NRTSMC). An exact-time extended state observer (ETESO) augmented with NRTSMC is designed to estimate the unknown exogenous disturbances and ensure fast states convergence while overcoming the singularity issue. The novelty of this work lies in the online updating of weight parameters of the RBFANN algorithm by using a new idea of incorporating an exponential sliding-mode effect, which makes a remarkable effort to make the control protocol adaptive to uncertain model parameters. A comparison of the proposed scheme with other conventional schemes shows its much better performance in the presence of parametric uncertainties and exogenous disturbances.

The investigated control strategy presents a robust adaptive law based on IRBFANN with a fast convergence rate and improved estimation accuracy via a novel ETESO.

To enhance the safety level and ensure stable flight operations by the quadrotor in the presence of high-order complex disturbances and uncertain environments, it is imperative to devise a robust control law.

A new idea of incorporating an exponential sliding-mode effect instead of conventional approaches in the algorithm of the RBFANN is used, which makes the control law resistant to model and parametric uncertainties. The ETESO provides rapid and accurate disturbance estimation results and updates the control law to overcome the performance degradation caused by the disturbances. Simulation results depict the effectiveness of the proposed control strategy.

The purpose of this paper is to propose a novel event-triggered aperiodic intermittent sliding-mode control (ETAI-SMC) algorithm for master–slave bilateral teleoperation robotic systems to further save communication resources while maintaining synchronization precision.

By using the Lyapunov theory, a new event-triggered aperiodic intermittent sliding-mode controller is designed to synchronize master–slave robots in a discontinuous method. Unlike traditional periodic time-triggered continuous control strategy, a new ETAI condition is discussed for less communication pressure. Then, the exponential reaching law is adopted to accelerate sliding-mode variables convergence, which has a significant effect on synchronization performance. In addition, the authors use quantizers to make their algorithm have obvious progress in saving communication resources.

The proposed control algorithm performance is validated by an experiment developed on a practical bilateral teleoperation system with two PHANToM Omni robotic devices. As a result, the synchronization error is limited within a small range and the control frequency is evidently reduced. Compared with a conventional control algorithm, the experimental results illustrate that the proposed control algorithm is more sensitive to system states changes and it can further save communication resources while guaranteeing the system synchronization accuracy, which is more practical for real bilateral teleoperation robotic systems.

A novel ETAI-SMC for bilateral teleoperation robotic systems is proposed to find a balance between reducing the control frequency and synchronization control precision. Combining the traditional sliding-mode control algorithm with the periodic intermittent control strategy and the event-triggered control strategy has produced obvious effect on our control performance. The proposed ETAI-SMC algorithm helps the controller be more sensitive to system states changes, which makes it possible to achieve precise control with lower control frequency. Moreover, we design an environment contact force feedback algorithm for operators to improve the perception of the slave robot working environment. In addition, quantizers and the exponential convergence law are adopted to help the proposed algorithm perform better in saving communication resources and improving synchronization precision.

The purpose of this paper is to implement a closed loop regulated bidirectional DC to DC converter for an application in the electric power system of more electric aircraft. To provide a consistent power supply to all of the electronic loads in an aircraft at the desired voltage level, good efficiency and desired transient and steady-state response, a smart and affordable DC to DC converter architecture in closed loop mode is being designed and implemented.

The aircraft electric power system (EPS) uses a bidirectional half-bridge DC to DC converter to facilitate the electric power flow from the primary power source – an AC generator installed on the aircraft engine’s shaft – to the load as well as from the secondary power source – a lithium ion battery – to the load. Rechargeable lithium ion batteries are used because they allow the primary power source to continue recharging them whenever the aircraft engine is running smoothly and because, in the event that the aircraft engine becomes overloaded during takeoff or turbulence, the charged secondary power source can step in and supply the load.

A novel nonsingular terminal sliding mode voltage controller based on exponential reaching law is used to keep the load voltage constant under any of the aforementioned circumstances, and its performance is contrasted with a tuned PI controller on the basis of their respective transient and steady-state responses. The former gives a faster and better transient and steady-state response as compared to the latter.

This research gives a novel control scheme for incorporating an auxiliary power source, i.e. rechargeable battery, in more electric aircraft EPS. The battery is so implemented that it can get regeneratively charged when primary power supply is capable of handling an additional load, i.e. the battery. The charging and discharging of the battery is carried out in closed loop mode to ensure constant battery terminal voltage, constant battery current and constant load voltage as per the requirement. A novel sliding mode controller is used to improve transient and steady-state response of the system.

The purpose of this paper is to develop sliding mode control with linear and nonlinear manifolds in discrete‐time domain for robot manipulators.

First, a discrete linear sliding mode controller is designed to an n‐link robot based on Gao's reaching law. In the second step, a discrete terminal sliding mode controller is developed to design a finite time and high precision controller. The stability analysis of both controllers is presented in the presence of model uncertainties and external disturbances. Finally, sampling time effects on the continuous‐time system outputs and sliding surfaces are discussed.

Computer simulations on a three‐link SCARA robot show that the proposed controllers are robust against model uncertainties and external disturbance. It was also shown that the sampling time has important effects on the closed loop system stability and convergence.

The proposed controllers are low cost and easily implemented in practice in comparison with continuous‐time ones.

The novelty associated with this paper is the development of an approach to finite time and robust control of n‐link robot manipulators in discrete‐time domain. Also, obtaining an upper bound for the sampling time is another contribution of this work.

The purpose of this paper is to describe a new method for selective harmonic elimination in a two-level three-phase inverter-fed direct torque controlled (DTC) permanent magnet synchronous motor (PMSM) drive to suppress unwanted resonant frequencies.

The design methodology is based on random space vector pulse-width modulation (RSVPWM) of PMSM drives. MATLAB simulations support the validity of suggested structure.

The simulation results of the proposed algorithm exhibit the development of a proper gap at the selected frequency in the frequency spectra of the motor input currents and voltages as well as lowering the ripples in the PMSM electromagnetic torque, stator current and flux linkage responses in compared with traditional DTC.

The proposed algorithm is a revised form of the RSVPWM technique used in a closed-loop structure along with a sliding mode speed controller which is capable to deal with nonlinear motor loads in an online manner. This study can be beneficial for the designers of AC motor drive system who attempt to find a modulation method that can create a selective gap in the power spectrum density of the motor input voltages and currents, therefore, promote an acoustically pleasant drive or alleviate unwanted motor vibrations.

This paper aims to design an adaptive nonlinear strategy capable of timely detection and reconstruction of faults in the attitude’s sensors of an autonomous aerial vehicle with greater accuracy concerning other conventional approaches in the literature.

The proposed scheme integrates a baseline nonlinear controller with an improved radial basis function neural network (IRBFNN) to detect different kinds of anomalies and failures that may occur in the attitude’s sensors of an autonomous aerial vehicle. An integral sliding mode concept is used as auto-tune weight update law in the IRBFNN instead of conventional weight update laws to optimize its learning capability without computational complexities. The simulations results and stability analysis validate the promising contributions of the suggested methodology over the other conventional approaches.

The performance of the proposed control algorithm is compared with the conventional radial basis function neural network (RBFNN), multi-layer perceptron neural network (MLPNN) and high gain observer (HGO) for a quadrotor vehicle suffering from various kinds of faults, e.g. abrupt, incipient and intermittent. From the simulation results obtained, it is found that the proposed algorithm’s performance in faults detection and estimation is relatively better than the rest of the methodologies.

For the improvement in the stability and safety of an autonomous aerial vehicle during flight operations, quick identification and reconstruction of attitude’s sensor faults and failures always play a crucial role. Efficient fault detection and estimation scheme are considered indispensable for an error-free and safe flight mission of an autonomous aerial vehicle.

The proposed scheme introduces RBFNN techniques to detect and estimate the quadrotor attitude’s sensor faults and failures efficiently. An integral sliding mode effect is used as the network’s backpropagation law to automatically modify its learning parameters accordingly, thereby speeding up the learning capabilities as compared to the conventional neural network backpropagation laws. Compared with the other investigated techniques, the proposed strategy achieve remarkable results in the detection and estimation of various faults.

This study aims to construct a novel maximum power tracking control system for the direct drive permanent magnet synchronous generator (PMSG) of the wind energy conversion system (WECS) to solve the following problems: how to effectively eliminate the system’s model parameter disturbances and speed up the dynamic performance of the system; and how to eliminate harmonics in WECS under different wind speeds.

To obtain the maximum output power of PMSG at WECS under different wind speeds, the following issues should be considered: (1) how to effectively eliminate the system’s model parameter disturbances and speed up the dynamic performance of the system; and (2) how to suppress system harmonics. For Problem 1, adding d–q compensation factors to active disturbance rejection control (ADRC) for the current loop realizes the d–q axis decoupling control, which speeds up the dynamic performance of the system. For Problem 2, the resonant controller is introduced into the ADRC for the current loop to suppress harmonic current in WECS under different wind speeds.

The simulation results demonstrate that the proposed control method is simpler and more reliable than conventional controllers for maximum power tracking.

Compared with traditional controllers, the proposed controller can speed up the dynamic performance of the system and suppress the current harmonic effectively, thus better achieving maximum power tracking.

This paper aims to propose an innovative adaptive control method for lower-limb rehabilitation robots.

Despite carrying out various studies on the subject of rehabilitation robots, the flexibility and stability of the closed-loop control system is still a challenging problem. In the proposed method, surface electromyography (sEMG) and human force-based dual closed-loop control strategy is designed to adaptively control the rehabilitation robots. A motion analysis of human lower limbs is performed by using a wavelet neural network (WNN) to obtain the desired trajectory of patients. In the outer loop, the reference trajectory of the robot is modified by a variable impedance controller (VIC) on the basis of the sEMG and human force. Thenceforward, in the inner loop, a model reference adaptive controller with parameter updating laws based on the Lyapunov stability theory forces the rehabilitation robot to track the reference trajectory.

The experiment results confirm that the trajectory tracking error is efficiently decreased by the VIC and adaptively correct the reference trajectory synchronizing with the patients’ motion intention; the model reference controller is able to outstandingly force the rehabilitation robot to track the reference trajectory. The method proposed in this paper can better the functioning of the rehabilitation robot system and is expandable to other applications of the rehabilitation field.

The proposed approach is interesting for the design of an intelligent control of rehabilitation robots. The main contributions of this paper are: using a WNN to obtain the desired trajectory of patients based on sEMG signal, modifying the reference trajectory by the VIC and using model reference control to force rehabilitation robot to track the reference trajectory.