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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.

Considering the influence of environmental noise and modeling error during the process of the robotic automatic grinding aero-engine blade, this study aims to propose a method based on the extended state observer (ESO) to reduce the fluctuation of normal grinding force.

First, the measurement range of the six-dimensional force sensor is calibrated according to the maximum acceleration of end-effector and grinding force. Second, the gravity and zero drift compensation model is built to compensate for measurement error. Finally, the switching function is designed based on the difference between the expected grinding force and the actual feedback value. When the value of function stays within the switching band, a nonlinear active disturbance rejection control (ADRC) loop is applied. When the function value reaches outside the switching band, an ESO-based sliding mode control (SMC) loop is applied.

The simulated and experimental results show that the proposed control method has higher robustness compared with proportion-integral-derivative (PID), Fuzzy PID and ADRC.

The processing parameters of this paper are obtained based on the single-factor experiment without considering the correlation between these variables. A new control strategy is proposed, which is not only used to control the grinding force of blades but also promotes the development of industrial control.

ESO is used to observe environmental interference and modeling errors of the system for real-time compensation. The segment control method consisting of ESO-based SMC and ESO-based ADRC is designed to improve the robustness. The common application of the two parts realizes suppression of fluctuation of grinding force.

The proposed Sliding Mode Control-Global Regressive Neural Network (SMC-GRNN) algorithm is an integration of Global Regressive Neural Network (GRNN) and Sliding Mode Control (SMC). Through this integration, a novel structure of GRNN is designed to enable online and. This structure is then combined with SMC to develop a stable adaptive controller for a class of nonlinear multivariable uncertain dynamic systems.

In this study, a new hybrid (SMC-GRNN) control method is innovatively developed.

A novel structure of GRNN is designed that can be learned online and then be integrated with the SMC to develop a stable adaptive controller for a class of nonlinear uncertain systems. Furthermore, Lyapunov stability theory is utilized to ensure the hidden-output weighting values of SMC-GRNN adaptively updated in order to guarantee the stability of the closed-loop dynamic system. Eventually, two different numerical benchmark tests are employed to demonstrate the performance of the proposed controller.

A novel structure of GRNN is originally designed that can be learned online and then be integrated with the sliding mode SMC control to develop a stable adaptive controller for a class of nonlinear uncertain systems. Moreover, Lyapunov stability theory is innovatively utilized to ensure the hidden-output weighting values of SMC-GRNN adaptively updated in order to guarantee the stability of the closed-loop dynamic system.

The purpose of this paper aims to investigate an effective algorithm for different types of disturbances rejection. New dynamics are designed based on disturbance. Observer-based sliding mode control (SMC) technique is used for approximation the disturbances as well as to stabilize the system effectively in presence of uncertainties.

This research work investigates the disturbances rejection algorithm for fixed-wing unmanned aerial vehicle. An algorithm based on SMC is introduced for disturbances rejection. Two types of disturbances are considered, the constant disturbance and the sinusoidal disturbance. The comprehensive lateral and longitudinal models of the system are presented. Two types of dynamics, the dynamics without disturbance and the new dynamics with disturbance, are presented. An observer-based algorithm is presented for the estimation of the dynamics with disturbances. Intensive simulations and experiments have been performed; the results not only guarantee the robustness and stability of the system but the effectiveness of the proposed algorithm as well.

In previous research work, new dynamics based on disturbances rejection are not investigated in detail; in this research work both the lateral and longitudinal dynamics with different disturbances are investigated.

As the stability is always important for flight, so the algorithm proposed in this research guarantees the robustness and rejection of disturbances, which plays a vital role in practical life for avoiding any kind of damage.

In the previous research work, new dynamics based on disturbances rejection are not investigated in detail; in this research work both the lateral and longitudinal dynamics with different disturbances are investigated. An observer-based SMC not only approximates the different disturbances and also these disturbances are rejected in order to guarantee the effectiveness and robustness.

In this research paper, an adaptive output-feedback robust active disturbance rejection control (RADRC) is designed for the multiple input multiple output (MIMO) quadrotor attitude model subject to unwanted uncertainties and disturbances (UUDs).

In order to achieve the desired control objectives in the presence of UUDs, the low pass filter (LPF) and extended high gain observer (EHGO) methods are used for the estimation of matched and mismatched UUDs, respectively. Furthermore, for solving the chattering incurred in the standard sliding mode control (SMC), a multilayer sliding mode surface is constructed. For formulating the adaptive output-feedback RADRC algorithm, the EHGO, LPF and SMC schemes are combined using the separation principle.

The findings of this research work include the design of an adaptive output-feedback RADRC with the ability to negate the UUDs as well as estimate the unknown states of the quadrotor attitude model. In addition, the chattering problem is addressed by designing a modified SMC scheme based on the multilayer sliding mode surface obtained by utilizing the estimated state variables. This sliding mode surface is also used to obtain the adaptive criteria for the switching design gain parameters involved in the SMC. Moreover, the requirement of high design gain parameters in the EHGO is solved by combining it with the LPF.

Designing the flight control techniques while assuming that the state variables are available is a common practice. In addition, to obtain robustness, the SMC technique is widely used. However, in practice, the state variables might not be available due to unknown parameters and uncertainties, as well as the chattering due to SMC reduces the performances of the actuators. Hence, in this paper, an adaptive output-feedback RADRC technique is designed to solve the problems of UUDs and chattering.

The aim of this research is to design a robust active disturbance attenuation control (RADAC) technique combined with an extended high gain observer (EHGO) and low pass filter (LPF).

For designing a RADAC technique, the sliding mode control (SMC) method is used. Since the standard method of SMC exhibits a chattering phenomenon in the controller, a multilayer sliding mode surface is designed for avoiding the chattering. In addition, to attenuate the unwanted uncertainties and disturbances (UUDs), the techniques of EHGO and LPF are deployed. Besides acting as a patch for disturbance attenuation, the EHGO design estimates the state variables. To investigate the stability and effectiveness of the designed control algorithm, the stability analysis followed by the simulation study is presented.

The major findings include the design of a chattering-free RADAC controller based on the multilayer sliding mode surface. Furthermore, a criterion of integrating the LPF scheme within the EHGO scheme is also developed to attenuate matched and mismatched UUDs.

In practice, the quadrotor flight is opposed by different kinds of the UUDs. And, the model of the quadrotor is a highly nonlinear underactuated model. Thus, the dynamics of the quadrotor model become more complex and uncertain due to the additional UUDs. Hence, it is necessary to design a robust disturbance attenuation technique with the ability to estimate the state variables and attenuate the UUDs and also achieve the desired control objectives.

Designing control methods to attenuate the disturbances while assuming that the state variables are known is a common practice. However, investigating the uncertain plants with unknown states along with the disturbances is rarely taken in consideration for the control design. Hence, this paper presents a control algorithm to address the issues of the UUDs as well as investigate a criterion to reduce the chattering incurred in the controller due to the standard SMC algorithm.

Active compliance control is the key technology for Tri-Co robots (coexisting–cooperative–cognitive robots) to interact with the environment and people. This study aims to make the robot arm shake hands compliantly with people; the paper proposed two closed-loop-compliant control schemes for the dynamic identification of cascade elbow joint.

The active compliance control strategy consists of inner and outer loops. The inner loop is the position control using sliding mode control with disturbance observer (SMCDO), in which a new saturation function is designed to replace the traditional signal function of sliding mode control (SMC) law so as to mitigate chatter. The outer loop is the admittance control to regulate the dynamic behaviours of the elbow joint, i.e. its impedance. The simulation is carried out to verify the performance of the proposed control scheme.

The results show that the chatter of traditional SMC can be effectively eliminated by using SMCDO with this saturation function. In addition, for the handshake task, the value of threshold force and elbow joint compliance is defined. Then, the threshold force tests, impact tests and elbow-joint compliance tests are carried out. The results show that, in the impedance model, the elbow joint compliance only depends on the stiffness parameters, not on the position control loop.

The effectiveness of the admittance control based on SMCDO can improve the adaptability of industrial manipulator in different working environments to some degree.

The admittance control with SMCDO completed trajectory tracking has higher accuracy than that based on SMC.

As the conventional multistep‐ahead prediction may be unsuitable in some cases, the purpose of this paper is to propose a novel method based on joint probability distributions, which provides the most probable estimation for the predicted trajectory.

Many real‐time series can be modeled in hidden Markov models. In order to predict these time series online, sequential Monte Carlo (SMC) method is applied for joint multistep‐ahead prediction.

The data of monthly national air passengers in China are analyzed, and the experimental results demonstrate that the method proposed and the corresponding online algorithms are effective.

In this paper, SMC method is applied for joint multistep‐ahead prediction. However, with the increasing of prediction step, the number of particles is increasing exponentially, which means that the prediction steps cannot be too large.

A very useful advice for researchers who study time‐series forecasts.

A novel method of multistep‐ahead prediction based on joint probability distribution is proposed and SMC method is applied to prediction time series online. This paper is aimed at those researchers who focus on time‐series forecasts.

In this paper, an adaptive fuzzy sliding mode controller (AFSMC) is developed for the formation control of a team of autonomous underwater vehicles (AUVs) subjected to unknown payload mass variations during their mission.

A sliding mode controller (SMC) is designed to drive the state trajectories of the AUVs to a switching surface in the state space. The payload mass variation results in parameter variation in AUV dynamics leading to actuator failure. This further leads to loss of communication among the members of the team. Hence, an adaptive SMC based on fuzzy logic is developed to maintain the coordinated motion of AUVs with payload mass variation.

The results are obtained by employing adaptive SMC for AUVs with and without payload variations and are compared. It is observed that the proposed adaptive SMC exhibits improved performance and tracks the desired trajectory in less time even with variation in the payload. The adaptive fuzzy control algorithm is developed to handle variation in payload mass variation. Lyapunov theory is used to establish stability of AFSMC controller.

Perfect alignment is assumed between centres of gravity (OG) and buoyancy (OB), thus AUVs maintaining horizontal stability during motion. The AUVs’ body centres are aligned with centres of gravity (OG), thus the distance vector being rg = [0,0,0]T. As it is a tracking problem, sway motion cannot be neglected as the AUVs are travelling in a curved locus, hence susceptible to Coriolis and centripetal forces. The AUV is underactuated as only two thrusters at the stern plate that are employed for the surge and yaw controls and error in Y- direction are controlled by adjusting control input in surge and heave direction. Control inputs to the thruster are constants, and depth control is achieved by adjusting the rudder angle.

AUVs are employed in military mission or surveys, and they carry heavy weapons or instrument to be deployed at or picked from specific locations. Such tasks lead to variation in payload, causing overall mass variation during an AUV’s motion. A sudden change in the mass after an AUV release or pick load results in variation in depth and average velocity.

The proposed controller can be useful for military missions for carrying warfare and hydrographic surveys for deploying instruments.

A proposed non-linear SMC has been designed, and its performances have been verified in terms of tracking error in X, Y and Z directions. An adaptive fuzzy SMC has been modelled using quantized state information to compensate payload variation. The stability of AFSMC controller is established by using Lyapunov theorem, and reachability of the sliding surface is ensured.

In the process of discussing the relationship between volume and price in the stock market, the purpose of this paper is to consider how to take the flow of foreign capital into consideration, to determine whether the inclusion of volume information really contributes to the prediction of the volatility of the stock price.

By comparing the relative advantages and disadvantages of the two main non-parametric methods mainstream, and taking the characteristics of the time series of the volume into consideration, the stochastic volatility with Volume (SV-VOL) model based on the APF-LW simulation method is used in the end, to explore and implement a more efficient estimation algorithm. And the volume is incorporated into the model for submersible quantization, by which the problem of insufficient use of volume information in previous research has been solved, which means that the development of the SV model is realized.

Through the Sequential Monte Carlo (SMC) algorithm, the effective estimation of the SV-VOL model is realized by programming. It is found that the stock market volume information is helpful to the prediction of the volatility of the stock price. The exchange market volume information affects the stock returns and the price-volume relationship, which is achieved indirectly through the net capital into stock market. The current exchange devaluation and fluctuation are not conducive to the restoration and recovery of the stock market.

It is still in the exploratory stage that whether the inclusion of volume information really contributes to the prediction of the volatility of the stock price, and how to incorporate the exchange market volume information. This paper tries to determine the information weight of the exchange market volume according to the direct and indirect channels from the perspective of causality. The relevant practices and conclusions need to be tested and perfected.

Previous studies have neglected the influence of the information contained in the exchange market volume on the volatility of stock prices. To a certain extent, this research makes a useful supplement to the existing research, especially in the aspects of research problems, research paradigms, research methods and research conclusion.

SV model with volume information can not only effectively solve the inefficiency of information use problem contained in volume in traditional practice, but also further improve the estimation accuracy of the model by introducing the exchange market volume information into the model through weighted processing, which is a useful supplement to the existing literature. The SMC algorithm realized by programming is helpful to the further advancement and development of non-parametric algorithms. And this paper has made a useful attempt to determine the weight of the exchange market volume information, and some useful conclusions are drawn.