This paper studies the problem of path tracking of Autonomous Ground Vehicles (AGVs) in the presence of sideslip angles. An observer is designed to estimate both the sideslip angles and the vehicle yaw rate, based on which an observer-based controller is established such that the closed-loop system is stable and the vehicle follows a desired path accurately. In particular, the nonlinear vehicle dynamics

Nonlinear process modeling is a primary task in intelligent manufacturing, aiming at extracting high-value features from massive process data for further process analysis like process monitoring. However, it is still a challenge to develop nonlinear process models with robust representation capability for diverse process faults. From the new perspective of the correlation between process variables

For strict-feedback nonlinear systems (SFNSs) with unknown control direction, this paper synthesizes an asymptotic tracking controller by a combination of the dynamic surface control (DSC) technique, the Nussbaum gain technique (NGT) and fuzzy logic systems (FLSs). The SFNSs under study feature unknown nonlinear uncertainties and external disturbances. By utilizing the DSC technique with nonlinear

With the advancement of technology, electric equipment and loads have become more sensitive to problems related to power quality, such as voltage sag, swell, imbalances, and harmonics. To detect faults and to protect sensitive loads from these voltage distortions, a Dynamic Voltage Restorer (DVR) series compensator is among the best available cost-effective solutions. One of the main goals of the DVR

Riverside monitoring systems are used for controlling the passage of ships, counting them to prevent overcrowding in a port, or raising an alarm if the ship is unknown or not safe. This type of control and analysis is commonly carried out by many people who supervise CCTV in real time. In this paper, we present an alternative approach to automatic image analysis using a variety of artificial intelligence

This paper focuses on mean exponential stability and L1-gain analysis for nonlinear positive Markov jump systems (NPMJSs) based on a switching transition probability (STP), where sector nonlinear functions and delays are adopted. By developing a nonlinear stochastic copositive Lyapunov-Krasovskii functional (NSCLKF) approach, a sufficient condition for mean exponential stability of nonlinear positive

In this paper, a fractional-order SIRD mathematical model is presented with Caputo derivative for the transmission of COVID-19 between humans. We calculate the steady-states of the system and discuss their stability. We also discuss the existence and uniqueness of a non-negative solution for the system under study. Additionally, we obtain an approximate response by implementing the fractional Euler

Background: The 2019 novel coronavirus disease (COVID-19) has spread rapidly worldwide, and the outbreak of the disease was designated a global pandemic by the World Health Organization. Such outbreaks would certainly be catastrophic for some of the best-ranked health systems and would be more catastrophic in countries with more fragile health systems. Accordingly, the World Health Organization and

This study introduces a design of robust finite-time controllers that aims to solve the trajectory tracking of robot manipulators with full-state constraints. The control design is based on the construction of a distributed state constraint non-singular terminal sliding mode (CNTSM). The CNTSM design includes the gain self-adapting tuning method, which can ensure finite-time convergence to the sliding

Internal model control (IMC) tuned simple modified Smith predictor structure for integrating time delayed processes (IPTD) is reported here. Pole position at origin implies its non-self-regulating behaviour. Processes like distillation column, liquid supply to large storage tank, superheated steam flow to turbine etc. are usually found IPTD processes. Reported modified Smith predictor (MSP) design

Load frequency regulation is one of the most vital and complex ancillary services in a deregulated power system. Increasing penetration from renewable energy sources in an integrated power system (IPS) further escalates the related control complexity due to a considerable decrement in IPS’s effective inertia. This may incur additional costs and can even lead to the destabilization of IPS. To overcome

Intelligent fault diagnosis of rolling element bearings gains increasing attention in recent years due to the promising development of artificial intelligent technology. Many intelligent diagnosis methods work well requiring massive historical data of the diagnosed object. However, it is hard to get sufficient fault data in advance in real diagnosis scenario and the diagnosis model constructed on such

This paper is devoted to develop an adaptive fuzzy tracking control scheme for switched nonstrict-feedback nonlinear systems (SNFNS) with state constraints based on event-triggered mechanism. All state variables are ensured to keep the predefined regions by employing barrier Lyapunov function (BLF). The fuzzy logic systems are exploited to cope with the unknown dynamics existing the SNFNS. It proposes

Recently, deep reinforcement learning techniques have achieved tangible results for learning high dimensional control tasks. Due to the trial and error interaction, between the autonomous agent and the environment, the learning phase is unconstrained and limited to the simulator. Such exploration has an additional drawback of consuming unnecessary samples at the beginning of the learning process. Model-based

This paper contributes to an efficiently computational algorithm of collaborative learning model predictive control for nonlinear systems and explores the potential of subsystems to accomplish the task collaboratively. The collaboration problem in the control field is usually to track a given reference over a finite time interval by using a set of systems. These subsystems work together to find the

The rapid development of technology and economy has led to the development of chemical processes, large-scale manufacturing equipment, and transportation networks, with their increasing complexity. These large systems are usually composed of many interacting and coupling subsystems. Moreover, the propagation and perturbation of uncertainty make the control design of such systems to be a thorny problem

In this article, the H∞ filtering issue is considered for discrete-time nonlinear networked systems subject to event-triggered communication scheme, dynamic quantization, and stochastic cyber attacks. The considered nonlinear networked system is described by the Takagi-Sugeno (T-S) fuzzy model. The event-triggered policy and the dynamic quantizer will be considered to realize the effective use of the

Aiming at the problem of poor prediction performance of rolling bearing remaining useful life (RUL) with single performance degradation indicator, a novel based-performance degradation indicator RUL prediction model is established. Firstly, the vibration signal of rolling bearing is decomposed into some intrinsic scale components (ISCs) by piecewise cubic Hermite interpolating polynomial-local characteristic-scale

Several methods to improve stabilization conditions of Takagi-Sugeno fuzzy descriptor systems (TSFDS) are presented. First, we prove that with a modified non-quadratic fuzzy Lyapunov function and a PDC controller, stabilization problems of TSFDS are reformulated as checking negativity of ”triple” fuzzy summations, and then relaxed methods of T-S fuzzy systems can be directly applied to descriptor systems

A combined fractional tracking controller based on backstepping and active disturbance rejection control (ADRC) is proposed in this work to control uncertain fractional-order systems (FOSs). The proposed method is constructed in two parts: first, all internal and external uncertainties are estimated by a fractional-order extended state observer (FO-ESO). Second, fractional backstepping method is used

In this paper, the issue of iterative learning fault diagnosis (ILFD) and fault tolerant control (FTC) is studied for stochastic repetitive systems with Brownian motion. Different from existing fault diagnosis (FD) methods, a state/fault simultaneous estimation observer based on iterative learning method is designed. The convergence condition of the ILFD algorithm is given. By employing the fault estimation

Forecasting solar irradiance is of utmost importance in supplying renewable energy efficiently and timely. This paper aims to experiment five variants of recurrent neural networks (RNN), and develop effective and reliable 5-minute short term solar irradiance prediction models. The 5 RNN classes are long-short term memory (LSTM), gated recurrent unit (GRU), Simple RNN, bidirectional LSTM (Bi-LSTM),

Path planning problem is attracting wide attention in autonomous system and process industry system. The existed research mainly focuses on finding the shortest path from the source vertex to the termination vertex under loose constraints of vertex and edge. However, in realistic, the constraints such as specified vertexes, specified paths, forbidden paths and forbidden vertexes have to be considered

The Perturb and Observe (P&O) Maximum Power Point Tracking (MPPT) algorithm in solar Photovoltaics (PV) is popular owing to its simplicity. However, its drawbacks, (i) operating point divergence and (ii) tradeoff between fast convergence and balanced state oscillations decelerate the usage. Most of the developments in the literature to overcome these drawbacks increase complexity. To retain simplicity

Recently, Governments of several countries are taking steps to unlock their countries from the lockdown state to avoid the adverse effect on economy. Disinfection chains of Ultraviolet (UV)-C lamps supported by holding stands have been placed between the space of object columns for exposure-based disinfection. These chains can be folded easily for carrying purpose. Also, the length of the system can

This paper studies a novel adaptive fractional-order non-singular terminal sliding mode (FO-NTSM) control strategy for omnidirectional mobile robot manipulator (OMRM) with unknown parameters and external disturbances. Firstly, we adopt the fuzzy wavelet neural networks (FWNNs) to estimate the dynamic uncertainty of the OMRM because it has superior function approximation capability. Secondly, we design

This paper designs a novel distributed interval observer for Linear Time Invariant (LTI) systems with additive disturbances. The technique of observer construction relies on the Internal Positive Representations (IPRs) of systems and synchronizing region approach, which ensures that the error system is stably and positive. Each observer estimates the upper and lower bounds (ULBs) of the system states

This paper deals with the parameter estimation of Hammerstein-Wiener (H-W) nonlinear systems which have unknown time delay. The linear variable weight particle swarm method is formulated for such time delay systems. This algorithm transforms the nonlinear system identification issue into a function optimization issue in the parameter space, then utilizes the parallel searching ability of the particle

The multimodal non-identical generators having close frequency modes may complicate the design of subsynchronous damping controller. In this paper, the Band-Pass Filter (BPF) based Subsynchronous Damping Controller (SSDC) is designed with SSSC to damp multimode SSR. The system is adapted from IEEE First Benchmark Model (FBM) with two non-identical generators in parallel. One of the generators has a

Online diagnosis for sucker rod pumping well has great significances for rapidly grasping operations of the oil well. Feature extraction of the working condition and determination of the online diagnostic algorithm are two indispensable parts. In this paper, five feature vectors are extracted using Freeman chain codes. Then, an optimized density peak clustering (DPC) method is proposed to realize online

The internal recirculation plays an important role on the different biological processes of wastewater treatment plants because it has a great influence on the concentration of pollutants, especially nutrients. Usually, the internal recirculation flow rate is kept fixed or manipulated by control techniques to maintain a fixed nitrate set-point in the last anoxic tank. This work proposes a new control

This paper proposes a new filtering scheme applied to a linearized model of a nonlinear representation for combustion systems, whose parameters are obtained by means of optical sensors. To ensure a robust representation regarding the chosen operation point and external disturbances variations, a linear parameter-varying (LPV) state-space representation is proposed in terms of noise disturbances and

This paper studies the energy-constraint output formation control for swarm systems with leaderless and leader-following topology structures. Most existing results on output formation with the dynamic output feedback protocols focus on the swarm systems without the energy constraint, but it is well known that the energy constraint is critically important for practical applications. In order to analyze

Coal is an important energy source worldwide. Objectively and accurately predicting coal consumption is conducive to healthy coal industry development, because such predictions can provide references and warnings that are useful in formulating energy strategies and implementing environmental policies. Population size and area economic development are the main factors that affect coal consumption. Considering

The article compares the dynamic performances of the pump-controlled, valve-controlled and prime-mover controlled hydromotor drive systems for a wide range of operating conditions. The control strategy adopted for each of them varies the supply flow to the hydromotor that gives the regulated drive speed. In this respect, the Simscape models are developed for the said systems that are validated experimentally

This paper proposes a hybrid condition monitoring approach, which integrates bond graph model-based diagnostic technique and data-driven remaining useful life (RUL) prediction, for a nonlinear mechatronic system. In this approach, various degrading faults can be considered and the physical degradation model is not required for RUL prediction. Firstly, an integrated fault signature matrix is proposed

This paper describes the design and implementation of intelligent dynamic models for fault detection and isolation of V94.2(5)/MGT-70(2) single-axis heavy-duty gas turbine system. The series-parallel structure of nonlinear autoregressive exogenous (NARX) models are used for fault detection, which initiate greater robustness and stability against uncertainties and perturbations. Moreover, to improve

In this paper, a model-free incremental adaptive optimal fault-tolerant controller with prescribed performance is proposed for nonlinear systems subject to actuator faults. Considering the system actuator redundancy, an actuator grouping scheme is introduced according to the actuator functions. An incremental adaptive fault observer is designed to estimate the faults. The recursive least squares (RLS)

In the engineering practice, lacking of data especially labeled data typically hinders the wide application of deep learning in mechanical fault diagnosis. However, collecting and labeling data is often expensive and time-consuming. To address this problem, a kind of semi-supervised meta-learning networks (SSMN) with squeeze-and-excitation attention is proposed for few-shot fault diagnosis in this

The work described herein compares the performance of different optimized controllers, viz. proportional-integral, proportional–integral–derivative (PID) with filter, two-degree-of-freedom (2DOF)-PID, 3DOF-PID, fractional-order-PID, cascade PI-PID, tilt-integral-derivative (TID), and cascade-TID (CC-TID) controllers in frequency regulation of a hybrid energy distributed power system (HEDPS). The HEDPS

In this paper, the robust filtering problem for uncertain complex networks with time-varying state delay and stochastic nonlinear coupling based on H∞ performance criterion is studied. The random connections of coupling nodes are represented by utilizing independent random variables and the multiple fading measurements phenomenon is characterized by introducing diagonal matrices with independent stochastic

Run-to-failure experiment is efficient and effective to investigate bearing deterioration process. Periodic transient waveform carries rich information of health conditions of bearings but the transient waveform matching is a challenging problem for evaluating bearing fatigue life because the shapes and parameters of the waveform vary with the evolution of the bearing degradation. A wavelet function

For the driving safety of electric vehicle (EV), intelligent diagnosis based on artificial hydrocarbon networks (AHNs) is proposed to detect mechanical faults of in-wheel motor (IWM) which is a promising force pattern of EV. AHNs, a novel mathematical model of supervised learning algorithm, can encapsulate or inherit or mix any information, then are adapted to deal with serious external interference

Static gains are often required for control, diagnosis and optimization of nonlinear dynamic systems. This paper proposes a new approach to estimate static gains for nonlinear dynamic systems from steady-state values hidden in historical data. First, steady-state values of system inputs and outputs are extracted by automatically finding data segments in steady-state conditions. Second, static gains

This paper studies the iterative learning control (ILC) algorithm for first-order hyperbolic systems. Unlike most of the ILC literature of distributed parameter systems, in the iteration domain, that require identical desired trajectories. Here the desired trajectories are iteratively varying and described by a high-order internal model (HOIM). The HOIM-based P-type ILC design is firstly introduced

Stochastic resonance (SR) is an effective tool to enhance weak signal by utilizing noise to reach a certain synergistic effect, which has been widely studied in the field of weak signal detection. Currently, using SR to enhance the weak fault feature of wind turbine faces two challenges: First, it is difficult for SR to select the optimal system parameters, while the traditional adaptive method based

In this paper, two non-singleton interval type-2 fuzzy PID (NIT2F-PID) controllers for the high precision electro-optical tracking system (ETS) are presented to improve anti-interference ability. Specifically, two types of non-singleton fuzzifiers, including type-1 (T1) and type-2 (T2), are considered to construct the proposed NIT2F-PID (N1IT2F-PID and N2IT2F-PID) controllers. Faced with the optimization

Exploring the drift maneuver could extend the dynamic control envelope and application range of autonomous vehicles. This paper presents a novel autonomous drift controller for a distributed drive electric vehicle, whose configuration provides more possibilities for drift. In the upper-level controller, a control channel recombination method transforms the over-actuated system into a standard square

In this paper, a robust global fast terminal attractor based full flight trajectory tracking control law has been developed for the available regular form which is operated under matched uncertainties. Based on the hierarchical control principle, the aforesaid model is first subdivided into two subsystems, i.e., a fully-actuated subsystem and an under-actuated subsystem. In other words, the under-actuated

We investigate the spatiotemporal dynamics and control of an epidemic using a partial differential equation (PDE) based Susceptible-Latent-Infected-Recovered (SLIR) model. We first validate the model using empirical COVID−19 data corresponding to a period of 45 days from the state of Ohio, United States. Upon optimizing the model parameters in the learning phase of the analysis using actual infection

The research on intelligent fault diagnosis has yielded remarkable achievements based on artificial intelligence-related technologies. In engineering scenarios, machines usually work in a normal condition, which means limited fault data can be collected. Intelligent fault diagnosis with small & imbalanced data (S&I-IFD), which refers to build intelligent diagnosis models using limited machine faulty

Recently, substantial research has explored the development of deep-learning-based methods to diagnose faults in rotating machinery. For these diagnosis methods, it is difficult to obtain high target diagnosis accuracy when the amount of labeled data obtained pertaining to the rotating machinery under study is insufficient or in cases involving a discrepancy in the distribution types found in the training

Transient impulses caused by local defects are critical for the fault detection of rotating machines. However, they are extremely weak and overwhelmed in the strong noise and harmonic components, making the transient features are very difficult to be extracted. This paper proposes an adaptive multi-scale improved differential filter (AMIDIF) to enhance the identification of transient impulses for rotating

This paper deals with estimation of interval controls for interval Linear Time Invariant (LTI) plants using pole placement technique. Generally, the parameters associated with LTI plant system are assumed to be crisp values, but due to the occurrence of error in experimentation, measurement or due to maintenance induced errors, the parameters are imprecise. As such, the LTI problem reduces to interval

Compared with other additive manufacturing processes, the metal-based additive manufacturing (MAM) can build higher precision and higher density parts, and have unique advantages in the applications to automotive, medical, and aerospace industries. However, the quality defects of builds, such as dimensional accuracy, layer morphology, mechanical and metallurgical defects, have been hindering the wide

In this article, a hybrid control approach is provided to control the micro-electro-mechanical system (MEMS) triaxial gyroscope as a multi-input multi-output (MIMO) system. Control design includes a fast non-singular terminal sliding mode control (FNTSMC) as a main part of the proposed hybrid control method and since the MEMS gyroscope performance is affected by parameter variations, quadrature errors