In this paper, we tackled the robust quantized proximity control problem for spacecraft with uncertain system parameters, external disturbances and safety constraint. As a stepping stone, a nonconvex forbidden zone composed of a cylinder and an ellipsoid is established around the target spacecraft. Then, a novel repulsive potential function is employed to encode the collision-avoidance requirement

To deal with the coordination problem for multi-manipulator trajectory tracking systems with parametric uncertainties, this paper proposes a two-layer control scheme incorporating a model predictive strategy and an extended state observer. In the kinematic layer, the visual information is implemented and a visual servoing error model is derived by the image-based visual servoing strategy. A recurrent

The performance of traditional penalty boundary intersection (PBI) decomposition-based evolutionary algorithm is totally determined by the penalty factor. The fixed penalty factor causes the inbanlance between the convergence and the diversity when solving many-objective problems. So, an adaptive decomposition evolutionary algorithm based on environmental information (MaOEA/ADEI) is proposed to solve

Dividing wall distillation columns (DWDCs) are effective technologies for distillation process intensifications. Azeotropic DWDCs (ADWDCs) are effective process intensification technologies to further intensify distillation process through combination of azeotropic distillation (AD) with DWDC. Investigations on the controllability and operation ability of ADWDC are mainly employing the traditional

Compared with single fault, the occurrence and composition of coupling faults have more uncertainties and diversities, which make fault classification a challenging topic in academic research and industrial application areas. In this paper, the classification problems of coupling faults are addressed from a new perspective, which will provide diagnostic decisions for online operators to take immediate

Vibration-based feature extraction of multiple transient fault signals is a challenge in the field of rotating machinery fault diagnosis. Variational mode decomposition (VMD) has great potential for multiple faults decoupling because of its equivalent filtering characteristics. However, the two key hyper-parameters of VMD, i.e., the number of modes and balancing parameter, require to be predefined

System identification based on incomplete measurements has extensively been studied in recent years. In this paper, the difference between state space models identified based on single-output and multi-output data is studied. The purpose is to investigate the relation between a typical output signal and other input–output signals in observable systems. We investigate to what extent a multidimensional

Machine learning techniques have been successfully applied for the intelligent fault diagnosis of rolling bearings in recent years. This study has developed an Improved Multi-Scale Convolutional Neural Network integrated with a Feature Attention mechanism (IMS-FACNN) model to address the poor performance of traditional CNN-based models under unsteady and complex working environments. The proposed IMS-FACNN

Achieving robust longitudinal speed control for hybrid electric vehicles (HEVs) through precise position tracking of electric throttle control system (ETCS) can improve engine fuel economy and vehicle longitudinal speed performance. Whereas, nonlinearities resulting from friction, gearbox, and return springs of ETCS, uncertain system parameters related to production deviations and device aging, disturbance

This paper is concerned with the event-triggered sliding mode control (SMC) strategy for the discrete-time two-dimensional (2-D) systems represented by Roesser model with time delays. Firstly, the linear sliding surface functions combined with the event-triggered scheme are constructed for the 2-D Roesser model. Then sufficient conditions are established for the asymptotic stability of the reduced-order

Early fault detection in squirrel cage induction motor (SCIM) can minimize the downtime and maximize production. This paper presents an adaptive gradient optimizer based deep convolutional neural network (ADG-dCNN) technique for bearing and rotor faults detection in squirrel cage induction motor. Multiple MEMS accelerometers have been used for vibration data collection, and sensor data fusion is employed

The problem of sliding mode control (SMC) for a class of Markov jump systems (MJSs) is addressed in this paper based on a resource-aware triggering mechanism which realizes computational resources saving and disturbance attenuation simultaneously. By introducing the self-triggered policy, the next execution time is pre-computed for sampling, updating and executing by relying on the latest sampled information

Taking uncertain disturbances into account, the control problem of the translational oscillator with a rotational actuator (TORA) is considered in this paper. Motivated by the desire to attenuate influences from unknown disturbances/uncertainties, the disturbance-observer-based methodology is utilized. Specifically, a system model with a cascade structure for the TORA system is obtained after a series

Wheelset bearing is one of the crucial rotating elements in the train bogie. Detection of wheelset bearing defect comes with many challenges due to complex wheel/rail excitation and the horrible working condition. The parametric dictionary sparse representation provides a practical path to detect the weak fault of wheelset bearing. However, the parametric dictionary obtained by the classical correlation

A simple method of controller design for multi-input multi-output (MIMO) processes have been proposed in frequency domain. The intensive calculation for the inverse of the process transfer function matrix is simplified to a great extent by evaluating the process transfer function matrix at a low frequency point. The desired closed loop transfer functions are derived for the process using a single tuning

In this paper, for periodic motion tasks, combining adaptive PID-type sliding mode control (APIDSMC), model reference adaptive control (MRAC) and periodic adaptive learning control (PALC), a novel APIDSMC-PALC compensation approach towards energy efficiency is proposed to suppress the influence of torque ripple in permanent magnet synchronous motor (PMSM) servo systems. Using particle swarm optimization

In this manuscript, a new hybrid force/position control approach has been proposed for time-varying constrained reconfigurable manipulators. In order to design the controller, firstly a reduced-order dynamic model of time-varying constrained manipulator system is presented. The uncertainties in the dynamical model of the system are inevitable; therefore the model-based control approach is inadequate

Though Center of Gravity (CoG) defuzzification is a well-known and long-standing method in the history of fuzzy systems, because of its computational complexity, its use in the field of modeling of fuzzy controllers is almost nil. From literature, it appears that modeling of fuzzy Proportional Integral Derivative (FPID) controllers is rarely attempted using CoG defuzzification. In fact, none of the

This novel is concerned with the event-triggering robust fusion estimation problem for multi-rate systems (MRSs) subject to stochastic nonlinearities (SNs) and censored observations (COs). The considered multi-rate system includes several sensor nodes, and each sensor is with different sampling rate. To reflect the dead-zone-like censoring phenomenon, a Tobit-1 regression model with prescribed left-censoring

Extreme learning machine (ELM) has better operation efficiency in fault diagnosis. However, the recognition accuracy of ELM algorithm is actually affected by the activation function. Moreover, most of the testing dataset are coming from high precision and expensive sensors. In this paper, raw data are collected by a low-cost attitude sensor, which is installed on the mobile platform of a delta 3D printer

This paper describes a model predictive control method for Nine-Switch Inverter (NSI)-based ac drive systems. Two separate three-phase ac loads (dual-output mode operation) or a single six-phase load (six-phase mode operation) can be connected to output terminals of an NSI. Three different Finite Control Set Model Predictive Control (FCS-MPC) strategies are presented and described. Firstly, dual-output

A fixed-time trajectory following problem for quadrotors via output feedback is concerned. Based on the inner-outer separation design philosophy, the under-actuated quadrotor is formulated as a hierarchical structure composed by position and attitude dynamics. With an emphasis on removing the demand on unmeasured velocity and eliminating the negative effect of disturbances, fixed-time extended state

A new robust adaptive mixing control (RAMC) is proposed in order to accomplish trajectory tracking of a tilt-rotor unmanned aerial vehicle (UAV) configuration. This kind of system is a hybrid aerial vehicle that combines advantages of rotary-wing aircraft, like hovering flight and vertical take-off and landing (VTOL), and those of fixed-wing aircraft, as improved forward flight. Although the VTOL and

This paper addresses the issue of sliding mode control(SMC) for a class of interval type-2(IT2) fuzzy system subject to the fading channel. For relieving the transmission burden, a dynamic event-triggered mechanism is introduced to determine the transmission of the measurement signals via the sensor-to-controller(S/C) channel. In the communication channel, the transmitted signal may suffer from fading

In this paper, to reduce the computational and communication burden, the event-triggered distributed zero-sum differential game problem for multi-agent systems is investigated. Firstly, based on the Minimax principle, an adaptive event-triggered distributed iterative differential game strategy is derived with an adaptive triggering condition for updating the control scheme aperiodically. Then, to implement

Steam injection distribution optimization refers to the process of distributing steam injection in steam assisted gravity drainage (SAGD) oil field to maximize the total oil production. A novel optimization method that integrates long short-term memory (LSTM) neural network and dynamic programming is presented in this paper to solve the steam injection distribution optimization problem for the first

In this paper, we present a polynomial chaos-based framework for the trajectory optimization of an overhead crane system under uncertainty. The main research described in this paper is as follows. First, the deterministic trajectory optimization problem formulation of a two-dimensional overhead crane model is constructed. Based on this basic mathematical formulation, the uncertainty trajectory optimization

The lack of faulty condition data reduces the feasibility of supervised learning for fault detection or fault severity discrimination in new manufacturing technologies. To deal with this issue, one-class learning arises for building binary discriminative models using only healthy condition data. However, these models have not been extrapolated to severity discrimination. This paper proposes to extend

The problem of detecting and quantifying bar breakage harmonics in inverter-fed induction motors has not been solved by the time-frequency transforms present in the technical literature. The paper proposes a new transform, called dragon transform, to solve this problem. The dragon atoms are defined with shapes perfectly adapted to the harmonic trajectories in the time-frequency plane, no matter how

In this paper, the issue of position tracking control of servo system with external disturbance is studied. A novel fractional-order sliding mode control (FOSMC) strategy is proposed. To be specific, firstly, the motor parameter drift, additional unknown disturbance and the uncertainty of load torque are considered as lumped disturbance, and a nonlinear disturbance observer (NDO) is designed to estimate

In this paper, the cascade control (CC), a structure popular among practitioners in many industry sectors, is used to bridge the two prominent research methodologies in SMC and ADRC for the purpose of multi-axis motion synchronization. In particular, the cascade sliding mode control(C-SMC) is first proposed, from which the cascade active disturbance rejection control (C-ADRC) is derived. This allows

In bearings defect diagnosis applications, information fusion has been widely used to improve identification accuracy for different types of faults, which may lead to high-dimensionality and information redundancy of the data and thus degenerate the classification performance. Therefore, it is a major challenge for machinery fault diagnosis to extract optimal features from high-dimensional and redundant

In recent years, vibration-based intelligent fault diagnosis of high-voltage circuit breakers (HVCBs) exhibits excellent performance. It requires a reliable machine learning method to develop an automatically diagnostic model to recognize the mechanical state from vibration signals. However, the traditional machine learning methods tend to produce unstable diagnostic results under the case of sampling

A multi-microgrid system, including several microgrids and distributed energy resources, is always threatened by numbers of faults and attacks as a consequence of which malfunctioning can occur on a large scale. Thus, minimizing the effects of such disruptions is of paramount importance. This paper addresses the problem of mitigating a multi-microgrid system that faces false data injection and replay

Initial positioning errors and the low adaptability of a priori digital elevation maps result in large positioning uncertainty intervals in the initial stage of terrain-aided navigation (TAN). This produces pseudo-peaks and mismatches in the initial position likelihood function and renders the convergence of the particle filter (PF) slow and unstable, while even causing divergence. Thus, the occurrence

Only vision-based navigation is the key of cost reduction and widespread application of indoor mobile robot. Consider the unpredictable nature of artificial environments, deep learning techniques can be used to perform navigation with its strong ability to abstract image features. In this paper, we proposed a low-cost way of only vision-based perception to realize indoor mobile robot navigation, converting

This study describes the design, instrumentation and control of an exoskeleton for lower limb children rehabilitation with nine degrees of freedom. Three degrees of freedom in each leg exert the movements of hip, knee and ankle in the sagittal plane, and three control the drive track system composed by a caterpillar-like robot. The control scheme presents a model free decentralized output feedback

In this paper we explore fuzzy string matching in an automatic ticket classification and processing system. We compare performance of the following string similarity algorithms: Longest Common Subsequence (LCS), Dice coefficient, Cosine Similarity, Levenshtein (edit) distance and Damerau distance. Through optimisation, we accomplished a 15% improvement in the ratio of false positives to true positive

The paper discusses the issue of global asymptotic stabilization for non-smooth variable order nonlinear switched systems with partial unstable modes. The existence and uniqueness of solution for the considered system is firstly verified by utilizing Gronwall–Bellman inequality and the inductive method. Then, a slow switching strategy is performed for the stable modes and the unstable modes are handled

The work deals with composite iterative learning model predictive control (CILMPC) for uncertain batch processes via a two dimensional Fornasini-Marchesini (2D-FM) model. A novel equivalent error system is first presented which is composed of state error and tracking error. Then an iterative learning predictive updating law is constructed by 2D state feedback control and the ‘worst’ case linear quadratic

Gaussian processes (GP) regression is a powerful probabilistic tool for modeling nonlinear dynamical systems. The downside of the method is its cubic computational complexity with respect to the training data that can be partially reduced using pseudo-inputs. The dynamics can be represented with an autoregressive model, which simplifies the training to that of the static case. When simulating an autoregressive

Due to the extremely complex mechanism and strong non-linear characteristics of industrial processes, data-driven soft sensor technologies play a key role in the intelligent measurement of process industries. However, the information of the collected process data in the steady stage is quite limited and unreliable, causing the small sample problem. As a result, it becomes an intractable challenge to

With the emerging of Internet of Things and smart sensing techniques, enormous monitoring data has been collected by prognostics and health management (PHM) systems. Predicting the Remaining useful life (RUL) of mechanical components from monitoring data has always been a challenging task in many industries, yet determining RUL accurately is identified as one of the most demanded outcomes of PHM systems

This paper proposes a neural network-based model predictive control (MPC) method for robotic manipulators with model uncertainty and input constraints. In the presented NN-based MPC structure, two groups of radial basis function neural networks (RBFNNs) are considered for online model estimation and effective optimization. The first group of RBFNNs is introduced as a predictive model for the robotic

This paper introduces a modified second order sliding mode algorithm with fixed-time stability analysis based on the Lyapunov function approach. An existing second order sliding mode algorithm is generalized, which provides superior features on convergence rate, accuracy, and robustness against a class of perturbations. The performance of the proposed algorithm is compared with existing algorithms

This paper addresses the design of a continuous super twisting control combined with a high-order sliding mode observer for a class of nonlinear systems. An analysis of closed-loop stability is provided by means of Lyapunov approach, where sufficient conditions are given to ensure the convergence and to select the gains of the proposed controller. Numerical simulation and experimental results illustrate

The conception of electromechanical oscillations initiates in the power network when there is an installation of the generator in parallel with the existent one. Further, the interconnection of multiple areas, extension in transmission, capricious load characteristics, etc. causes low-frequency oscillations in the consolidated power network. This paper proposes variants of a booming population-based

This paper presents a MILP (mixed integer linear programming) based formulation for the coordination of multiple robots. We consider robots that must follow closed intersecting paths persistently. We propose an off-line planning of velocity profiles preventing the need of online collision avoidance maneuvers or path replanning. Our robot model considers minimum and maximum speed constraints, which

A stable transition from free-motion to contact phase is extremely significant for a UAV interacting with its objects and environment. Oscillations may occur at the contact point if the force controller of the UAV fails to accommodate to the environment parameters, which are usually unknown to the UAV. In this paper, the acceleration feedback control is proposed to enhance the robustness of a classic

Prescribed-time Lag consensus, as a special case of prescribed-time cluster lag consensus, is first investigated. The task is to design a control protocol for each follower so that the multiagent system (MAS) achieves lag consensus in any specified time. To achieve this goal, we propose a new distributed controller, in which the control gains are designed as time-varying functions related to the pre-specified

This paper proposes a control structure suitable for high-order non-minimum phase (NMP) processes. In general, dead-time compensators (DTCs) firstly predict the process output with zero error at a steady-state and then a primary controller with an integrator is designed based on the delay-free model. The main advantage of the proposed structure is that the primary controller is only a state feedback

This paper investigates the H∞ control problems of singular fractional-order interval systems with commensurate order 0<α<1. Firstly, novel conditions for the bounded real lemma in the sense of H∞ norm for nominal singular fractional-order systems are proposed, based on which the sufficient conditions for checking the admissibility and H∞ performance of singular fractional-order interval systems are

The problem of sliding mode control for a class of Takagi-Sugeno fuzzy model-based nonlinear one-sided Lipschitz systems is investigated in the paper. Due to the state components are not available, a state observer is designed based on an event-triggering mechanism. Meanwhile, the output measurements transmitted through the communication channels suffer from signal delays. Based on the estimated state

It is meaningful to study the control problems of nonlinear systems with uncertain parameters and external disturbances, especially for those subject to state constraints. In this paper, a state transformation approach is proposed to address time-varying asymmetric state constraints. By this state transformation, the state constrained problem is transformed into the boundedness problem of the transformed

The paper discusses the cooperative tracking problem of partially known cyber-physical multi-agent networked systems. In this system, there exist two cascaded chances for cyber-attacks. The local agent is of networked system type that is subjected to unknown false data-injection and replay cyber-attacks that are dissimilar in the sensor-controller and the controller-sensor network parts. The communication

In the present paper, an active disturbance rejection control(ADRC) scheme via radial basis function(RBF) neural networks is designed for adaptive control of non-affine nonlinear systems facing hysteresis disturbance in which RBF neural network approximation is utilized to tackle the system uncertainties and ADRC is designed to real-time estimate and compensate disturbance with unknown backlash-like

This paper develops a method for estimating the state of deterioration of a friction drive system and presents its use for predicting and controlling the Remaining Useful Life (RUL) of such a system. The friction drive system is assumed to be affected by endogenous uncertainties and exogenous disturbances. The proposed method is intended for on-line estimation of the contact surface deterioration and

In the hydraulic systems, the non-structural uncertainties such as the nonlinear friction will reduce the tracking accuracy for the hydraulic servo system. In this paper, an incomplete differential-based improved adaptive backstepping integral sliding mode control (ID-BIABISMC) is proposed to realize the position control for the hydraulic servo system based on the friction compensation. The backstepping-based