The coexistence of high-frequency phasor measurement units (PMUs) and conventional SCADA systems raises the challenge of heterogenous-source and multi-rate measurements which significantly degrades the performance of power system dynamic state estimation. In this work, a deep reinforcement learning (DRL) assisted robust cubature Kalman filtering (CKF) scheme is proposed to handle measurements from hybrid sources and with different time scales. In specific, a multi-rate measurement function reconstruction approach is designed with an independent discretization mechanism to lift the present limitation of requiring an integer multiple relationship of the sampling rates from multiple sources in most of existing works. Embedded with this discretization mechanism, a deep reinforcement learning assisted two-parameter linear exponential smoothing method is proposed to reconstruct the slow measurement model with online adjustable estimation parameters. A generalized correntropy loss criterion is also included in the robust CKF to counter the non-Gaussian noise and the noise distribution variation caused by the reconstruction. Comparisons results demonstrate that the proposed DRL-based robust CKF method can achieve better accuracy and robustness under various operating scenarios.

This article investigates the state estimation problem for 2-D Markov jumping systems subjected to randomly occurring false data injection attack. To address this challenge, a novel probabilistic multi-interval ETP (PMIETP) is proposed, integrated with a time-varying saturation mechanism (TVSM). The PMIETP is designed by combining subinterval triggering thresholds with a probability distribution model, thereby enhancing system performance and adaptability under varying network conditions. To further mitigate the impact of maliciously injected data and improve estimation robustness, a TVSM-based estimator is developed, which employs an adaptive threshold to confine abnormal data within an acceptable range. In addition, a particle swarm optimization algorithm is employed to fine-tune design parameters, thereby reducing the conservativeness of linear matrix inequality conditions. Based on Lyapunov stability theory, sufficient criteria are derived to guarantee mean-square asymptotic stability and prescribed noise attenuation performance. Finally, a numerical simulation example demonstrates the effectiveness and superiorities of the proposed approach over existing methods.

This article addresses the adaptive neural network (NN)-based sliding-mode control (SMC) problem for sampled-data singularly perturbed systems under aperiodic sampling intervals and input dead zone nonlinearities. To accurately characterize the irregularity of sampling intervals, a nonhomogeneous sojourn probability approach is introduced. To accurately characterize the irregularity of sampling intervals, a nonhomogeneous sojourn probability approach is introduced. An adaptive NN scheme is utilized to estimate and effectively compensate for the nonlinear errors induced by input dead zones, thereby significantly enhancing the robustness and performance of the controlled system. Leveraging these considerations, a novel sliding-mode controller, specifically designed to accommodate variations in sampling period modes and singular perturbation parameters, is proposed. This control strategy guarantees the exponential ultimate boundedness of system states in the mean-square sense and ensures the reachability of the predefined sliding surface in the closed-loop system. The validity of the proposed theory is demonstrated through a practical example.

In this article, we address the problem of protocol-based sliding mode control for switched systems with multizone probabilistic time-varying delays. To effectively manage the dynamic behavior of stochastic switching systems, a novel switching rule that incorporates both sojourn probability information and sojourn time is proposed. By exploiting the random nature of time-varying transmission delays, a novel multizone probabilistic event-triggered protocol is developed. Unlike the common sliding model control law, by utilizing the coordinate transformation technique, a protocol-based sliding mode control law is implemented to realize the reachability of predetermined sliding domain. Finally, simulations involving a numerical example and an operational amplifier model are provided to validate the feasibility and efficacy of the proposed methodology.

Autonomous underwater vehicles (AUVs) face significant challenges in trajectory tracking due to nonlinear dynamics, actuator faults, and environmental disturbances. To address these issues, this article proposes a novel fault-tolerant control strategy that ensures fixed-time trajectory tracking with prescribed accuracy for underactuated AUVs. The proposed approach integrates boundary functions with a constraint-handling mechanism, enabling guaranteed tracking performance within a fixed time horizon while satisfying output constraints. Unlike existing approaches, the controller does not rely on accurate system models, parameter estimation, or external observers, and avoids the computation of virtual control derivatives, resulting in reduced computational complexity. Moreover, the control scheme maintains robustness against time-varying actuator faults and environmental disturbances without auxiliary adaptation or learning mechanisms. Simulation results demonstrate the effectiveness and superior performance of the proposed approach compared with existing methods, validating its capability to maintain tracking accuracy and closed-loop stability under adverse operating conditions.

To achieve fault diagnosis and prognosis, obtaining adequate and reliable life-cycle data is essential. However, this poses a challenge in current high-reliable Internet of Things (IoT) systems. Fortunately, accelerated degradation testing (ADT) can be employed to overcome this hurdle. Nevertheless, a dependable testing and measuring technique is required to construct an accurate model for ADT. This testing method plays a vital role in evaluating fault diagnosis, prognosis, lifetime, and maintenance decisions for reliable products under operational stress. To ensure effective testing, it is crucial to utilize appropriate models that account for the individual heterogeneity of products. However, the commonly used single stochastic models in ADT overlook the impact of this condition in real-world applications, resulting in misspecification problem. To address this limitation, we propose a novel mixed stochastic process model that integrates multi-Wiener processes and dynamic weights. In addition, we leverage interval analysis to analyze system lifetime, considering the limited data size. The estimation of unknown parameters in our mixed model is achieved using the Metropolis–Hastings algorithm. By analyzing stress relaxation data from electrical connectors, we demonstrate the superior accuracy of our mixed model over conventional single stochastic models in ADT.

A new load frequency control (LFC) technique for a multiarea steam-hydropower system (MASHPS) with parameter uncertainty is proposed in this research. A second-order sliding mode control (SMC) via double integrated sliding surface is meant to improve MASHPS frequency regulation, tie-line power management, and dependability. This strategy not only increases asymptotic stability and dependability of MASHPS, but it also reduces the chattering problem that is inherent in first-order SMC. Furthermore, the new linear matrix inequality based on Lyapunov stability is used to analyze the entire MASHPS stabilization. For the LFC research, the efficient achievement of the proposed technique is investigated in a two-area steam-hydropower system. Under parameter uncertainties and various assumed load disturbances from households, commercial buildings, and industries, the proposed second-order SMC via double integral sliding surface proves to be highly robust and improves the MASHPS response in terms of frequency regulation, tie-line power management, and system reliability when compared to other existing proposed methods with less uncertainty consideration. Overall, the results indicate that the novel approach is feasible for MASHPS LFC and power system reliability.

Accelerated degradation testing (ADT) data typically exhibit a time-stress-dependent structure, as well as random uncertainties due to time-varying effects and unit-to-unit variations. Existing ADT models based on Brownian motion with drift have successfully represented the fault/failure-based degradation behavior and random uncertainty by assuming that the drift parameter follows a Gaussian distribution. However, these models often lack robustness to outliers, leading to distorted analysis, affecting parameter estimation, model accuracy, decision-making, risk assessment, and potentially overlooking the influence of stress factors. A novel robust ADT model based on the Wiener process and its corresponding lifetime analysis method are proposed to address these issues. The proposed approach improves upon traditional ADT models by making the drift parameter follow a$t$-distribution rather than a Gaussian distribution, which can reduce sensitivity to outliers in real degradation processes. In addition, the proposed method allows for the simultaneous consideration of time-stress-dependent factors in the ADT model, facilitating the derivation of a closed-form robust ADT formulation. Subsequently, the lifetime is analyzed based on the ADT model using the first hitting time method in a probabilistic framework. The proposed method is applied to stress relaxation data of electrical connectors and compared to three other common methods.

This article proposes a distributed performance recovery method for multiagent systems with actuator faults in noncooperative games. The local agent (player) can only obtain the policy information of neighboring agents through the communication network. The strategies of nonneighbors in the cost function are unknown, and a leader–follower consensus algorithm is introduced to estimate nonneighbors' strategy. When the actuator faults occur in any agents and lead to performance degradation, i.e., the agents' strategy is biased from its optimal strategy. A distributed optimization control method is proposed to recover performance without changing the original control scheme. An observer-based residual feedback plug-and-play optimization method is used to ensure that the strategies of all agents can still converge to the optimal strategy (or close to the optimal strategy). Numerical case studies are applied to demonstrate the performance and effectiveness of the proposed method.

The intelligent manufacturing system is a complex, large-scale, interconnected system composed of many intelligent agents, and there may be physical or information space couplings between the agents. A distributed monitoring system and optimization control method are proposed to ensure the system completes its tasks safely and efficiently. The distributed monitoring system based on the average consensus algorithm is equivalent to the centralized design method, in which the submonitoring system only requires local and neighbor subsystem information. The advantage of this design is that it uses local and interactive information to achieve global diagnosis. In addition, sending data from all subsystems to a central computing node is challenging to implement in large-scale manufacturing systems. Based on the centralized plug-and-play (PnP) optimization control method, an average consensus algorithm distributed manufacturing system PnP optimization control method is proposed. Its advantage is that it uses local information and interactive information to achieve global control optimization. On this basis, an integrated architecture for distributed fault detection and optimization control is developed. The simulation results verify the feasibility and effectiveness of proposed method.

Over the last decade, transfer learning has attracted a great deal of attention as a new learning paradigm, based on which fault diagnosis (FD) approaches have been intensively developed to improve the safety and reliability of modern automation systems. Because of inevitable factors such as the varying work environment, performance degradation of components, and heterogeneity among similar automation systems, the FD method having long-term applicabilities becomes attractive. Motivated by these facts, transfer learning has been an indispensable tool that endows the FD methods with self-learning and adaptive abilities. On the presentation of basic knowledge in this field, a comprehensive review of transfer learning-motivated FD methods, whose two subclasses are developed based on knowledge calibration and knowledge compromise, is carried out in this survey article. Finally, some open problems, potential research directions, and conclusions are highlighted. Different from the existing reviews of transfer learning, this survey focuses on how to utilize previous knowledge specifically for the FD tasks, based on which three principles and a new classification strategy of transfer learning-motivated FD techniques are also presented. We hope that this work will constitute a timely contribution to transfer learning-motivated techniques regarding the FD topic.

This article presents an adaptive control strategy for lane-keeping task of automatic steering systems via sliding-mode technique. First, considering the road-vehicle lateral dynamics, a standard single track steering model has been developed for the lane-keeping task. To cope with the time-variant nature of the longitudinal velocity and uncertainty of measurement, a class of interval type-2 fuzzy sets considered in this article are employed to reconstruct the steering system dynamics mathematical model. Based on the fuzzy system, an integral sliding surface is proposed and the asymptotic convergence criterion for the overall system is derived with extended dissipation. Furthermore, an adaptive control law is provided to achieve the reachability of the assigned sliding surface and improve the attenuation ability to unknown curvature and exogenous disturbance. Finally, several scenarios with different path-following tasks are given in the simulations. Results demonstrate that the proposed sliding-mode control method has the capability to track the road centerline and is robust to external unknown disturbances.

This article presents a novel compound aerial vehicle configuration called the hybrid quadplane with all-moving wings (HQWAWs), and provides a six-degrees-of-freedom (6-DOF) flight dynamics modeling and nonlinear controller design for this new configuration. Compared to traditional quadrotors, the HQWAW add two wings on left and right sides of the quadrotor. Each wing is a single structure that can rotate to any angle of attack independently, which is referred to as all-moving wing (AW) in this article. The dynamic modeling of this configuration takes into account a complete description of flight dynamics, including wing aerodynamics and the dynamics of motors and propellers. A comprehensive nonlinear flight controller is proposed using model feedforward and state feedback for the HQWAW that supports the whole flight envelope. The proposed HQWAW configuration is compared with a traditional quadrotor with all parameters being the same except for the absence of the AWs. A set of numerical results demonstrate that the proposed configuration can obtain flight flexibility beyond quadrotors, while effectively reducing energy consumption.

Due to limitations in large-area communication and computation, it can be challenging to apply centralized diagnosis and optimization control design approaches to cascaded systems. This work proposes a distributed diagnosis and optimization control approach, which is realized using data-driven techniques. Specifically, an adaptive observer-based subdiagnosis system design approach is proposed for cascaded systems using only the local input/output (I/O) data and the state estimations of adjacent subsystems. The state estimations from neighboring subsystems are treated as known inputs in the local subsystem. In the centralized design approach, the residual signals generated by all subsystem observers need to be sent to the central computing node to reconstruct controller parameters. The learning process of the local optimization controller only needs to be driven by the residual signals from local and adjacent subsystems, avoiding centralized calculation and reducing the computational burden of the central node. The learning process of the locally optimal controller only needs to be driven by residual signals from the local and neighboring subsystems. In the end, the simulation results verify the effectiveness of the proposed distributed approach.

This article considers the problem of distributed adaptive fuzzy event-based finite-time prescribed performance leader-following tracking control for heterogeneous nonlinear multiagent systems (NMASs) over a directed topology. Each agent is considered in a nonaffine nonstrict-feedback form under input saturation and output constraint which contains unknown dynamics and external disturbances. Fuzzy logic systems (FLSs) are exploited as an effective online approximation tool to tackle system uncertainties. By employing the unique property of FLS, the algebraic loop problem is overcome and by designing novel adaptive laws of the FLS weights, the computation burden is decreased significantly. The threshold of the event-triggered condition is improved compared to the conventional relative-threshold mechanism. A modified performance function called finite-time performance function is introduced to constrain the synchronization errors within the prescribed performance bounds in finite time. The dynamic surface control technique is then developed to avoid the issue of the “explosion of complexity.” Moreover, by developing a new decomposition for the controller gain function resulting from the mean-value theorem and introducing an auxiliary system, the input saturation nonlinearity that affects the nonaffine form stability is handled. Through the Lyapunov stability analyses, it is shown that the developed control algorithm ensures the closed-loop NMAS trajectory to be cooperatively semi-globally uniformly ultimately bounded. Additionally, the tracking errors are driven to a predefined region around zero in finite time. Finally, the efficiency of the established theoretical results is validated by the simulation studies.

Threats of cyberattacks have penetrated from disclosing critical user information to destroying/manipulating industrial control systems. Study on data security during network transmission has raised increasing attention in the systems and control community, which is found very necessary and timely in the context of Industry 4.0. In most existing approaches, the protection of the transmitted data from eavesdropping attacks and the detection of malicious integrity attacks are usually carried out separately. In this study, an integrated data-driven framework applicable at the control level is proposed to deal with secure transmission and attack detection simultaneously. In the framework, a secure correlation-based encryption/decryption approach and a trustworthiness judgement approach are proposed. Comprehensive discussions are made regarding the analysis of the sensitivity to attacks, the introduced time delay, and the design degree-of-free. Executable algorithms are presented, corresponding to which hardware is modularized and can work standalone independent from the configuration of the monitoring and control systems or any third-party authentication agencies. Evaluation results on a simulated two-area frequency-load control power grid system are provided to show the effectiveness and performance of the proposed approaches.

A finite-time control method is presented for n -link robots with actuator saturation under time-varying constraints of work space. Barrier Lyapunov functions (BLFs) are designed for ensuring that the robot remains under time-varying constraints of the work space. In order to deal with asymmetric saturation nonlinearity, we transform asymmetric saturation into a symmetric one by using a hyperbolic tangent function, which is introduced to avoid the discontinuous problem existing in the auxiliary system-based saturation method. Combining fuzzy-logic systems (FLSs) with the backstepping technique, a finite-time control policy is designed for ensuring the stability of the closed-loop system. With the use of the Lyapunov stability theory, all the error signals are proved to be semiglobal finite-time stable (SGFS). Finally, the experiment is carried out to verify the effectiveness of the finite-time method.

Three-tank system is an important benchmark in industrial process. However, so far, the research on the three-tank system is mainly limited to simulation studies, and the use of virtual simulators. In this article, a real-time three-tank system setup is used for practical investigations. Differing with the virtual simulator of a three-tank system, the setup can enable the setting of different types of faults (such as cloggings and leaks in the system, sensor faults, and actuator faults) through manual manipulation, users can choose the combinations of different valves and knobs in the setup, which is helpful to evaluate and compare methods for process monitoring and control. The relevant codes or modules can be applied directly that are developed in the MATLAB/Simulink environment. On this setup, two methods are used to verify the effectiveness in this study. One method is to solve the problem of process monitoring and fault detection; due to the fluctuation of the liquid level caused by flow, the input/output (I/O) data are preferred to be decomposed to different subspaces, which aims to identify the data-driven stable kernel representation. Moreover, the original controller of the three-tank system cannot match the system accurately, and therefore, needs to be modified. To solve the problem, a plug-and-play process control method (the other method used in this article) is applied, which adds a stable Youla parameterization matrix on the basis of the original controller. All controllers that internally stabilize the control loop improves the performance of the system without changing the original controller of the three-tank system. The experimental results of the two methods indicate that the proposed approach has strong practicality.

In the conventional approaches to the design of fault diagnosis systems, little effort is usually paid to the selection of the parity vectors. As a result, the systems' performance can be significantly affected. In this article, novel approaches are proposed to derive the parity vectors that construct optimized residual generators for linear and nonlinear systems. Based on the analysis on the parity space dimension, a novel parameterization of all parity relation-based residual generators is proposed. An iterative procedure that guarantees minimal regression error is then employed in the search for the optimal parameters. Considering that the traditional parity relation-based approaches are only suitable for linear systems, in this work, the proposed approach is also generalized to deal with strong nonlinearities, with the aid of data-driven Hammerstein function estimation. Furthermore, optimized residual generation algorithms are summarized for offline design and online implementation, the performance of which is evaluated thoroughly with a three-tank system, a numerical nonlinear example, as well as a case study on an industrial hot rolling mill process. Results show that residuals generated by the proposed approaches can significantly improve the sensitivity to small faults, and thus, the fault detection rate is improved compared with the traditional nonoptimized approach.

The fourteen papers in this special section focus on data-drive management of complex systems via plant-wide performance supervision. Currently, massive amounts of data are continuously being produced by social and industrial activities. Consequently, data-driven techniques have received considerable attention both in industry and academia in recent years, aiding scientists to manage and interpret the available data. The reasons behind such popularity of data-driven techniques are twofold. On the one hand, advanced data processing and information acquisition technologies have been developed to the extent that large amounts of data in different forms are available for big data analysis from descriptive to prescriptive. On the other hand, with the help of machine learning methodologies, the supervision and management systems can provide effective decisions for plant-wide optimal performance. Compared to the conventional model-based techniques, the data-driven ones can not only save the costly modeling procedures but also extract valuable information from available process data for real-time analysis and management. However, there are many complex and challenging problems in the data-driven supervision and management techniques, such as data-driven supervision on the safety, security, and robustness, as well as the performance-supervised management and their distributed designs. The papers in this section target recent results, trends, and practical developments in the data-driven methodologies of plant-wide performance supervision and management for complex systems, especially those related to process monitoring and machine learning activities with their industrial applications.

In recent years, with the rapid growth of rooftop photovoltaic (PV) generation in distribution networks, power system operators call for accurate forecasts of the behind-the-meter (BTM) load and PV generation. However, the existing forecasting methodologies are incapable of quantifying such BTM measurements as the smart meters can merely measure the net load time series. Motivated by this challenge, this article presents the spatiotemporal BTM load and PV forecasting (ST-BTMLPVF) problem. The objective is to disaggregate the historical net loads of neighboring residential units into their BTM load and PV generation and forecast the future values of these unobservable time series. To solve ST-BTMLPVF, we model the units as a spatiotemporal graph (ST-graph) where the nodes represent the net load measurements of units and edges reflect the mutual correlation between the units. An ST-graph autoencoder (ST-GAE) is devised to capture the spatiotemporal manifold of the ST-graph, and a novel spatiotemporal graph dictionary learning (STGDL) optimization is proposed to utilize the latent features of the ST-GAE to find the most significant spatiotemporal features of the net load. STGDL utilizes the captured features to estimate the historical BTM load and PV measurements, which are further used by a deep recurrent structure to forecast the future values of BTM load and PV generation at each unit. Numerical experiments on a real-world load and PV data set show the state-of-the-art performance of the proposed model, both for the BTM disaggregation and forecasting tasks.

The fault-tolerant control (FTC) issue is considered in this article for Markovian jump systems (MJSs) in which both nonlinearity and actuator faults exist simultaneously. The existed nonlinearity in the considered MJSs means that there exist limitations to employ the renown sliding mode control (SMC) method directly. In this work, the radial basis function (RBF) neural network (NN) technique is exploited to model the nonlinearity on which no knowledge whatsoever is available. Then, with the help of the adaptive backstepping method, an NN-based FTC approach is proposed to overcome the considered challenging case. The adverse effects, arising from the nonlinearity and the actuator faults can be completely compensated by the proposed adaptive controller. With the proposed controller and the adaptation laws, the bounded stability of the considered closed-loop plant can be guaranteed. Furthermore, only two types of adaptive parameters are adopted in the proposed approach to achieve the purpose of FTC, and this reduces the computational burden and thus extends its applicability. Finally, the effectiveness of the developed approach is demonstrated on a practical system: a wheeled mobile manipulator.

In this paper, the complex problems of internal forces and position control are studied simultaneously and a disturbance observer-based radial basis function neural network (RBFNN) control scheme is proposed to: 1) estimate the unknown parameters accurately; 2) approximate the disturbance experienced by the system due to input saturation; and 3) simultaneously improve the robustness of the system. More specifically, the proposed scheme utilizes disturbance observers, neural network (NN) collaborative control with an adaptive law, and full state feedback. Utilizing Lyapunov stability principles, it is shown that semiglobally uniformly bounded stability is guaranteed for all controlled signals of the closed-loop system. The effectiveness of the proposed controller as predicted by the theoretical analysis is verified by comparative experimental studies.

Due to the recent rapid developments in communication and networking technologies as well as computer science, the complexity of automatic control systems has increased significantly. To ensure the safety and the reliability of such systems under continuous operation, real-time supervision and control systems have now to run in parallel. These developments challenge scientists and engineers to come up with advanced fault diagnosis (FD) and fault-tolerant control (FTC) approaches that can monitor the abnormal changes in automatic control systems promptly. The objective is to maintain safe operating conditions that avoid severe performance degradation. The tasks involved in meeting the objective consist of fault detection, estimation, localization, isolation, feasible control strategies, and maintenance actions.

In the control theory, after the stability of the control system is guaranteed, improving the performance of the system has always been a hot topic. In this paper, based on the Plug-and-Play process monitoring and control architecture (PnP-PMCA), without changing the previously designed controller, new actuators plugged into original system will be presented. In addition, feedforward controller (one of the components of PnP-PMCA) related to tracking performance will also be designed. After the new actuator is inserted into the original system, the feedforward controller is adjusted by iterative method to achieve the desired tracking performance and the original controller is not modified. For the implementation of plug-and-play actuators and the effectiveness of the iterative feedforward controller, the three tank system benchmark experiment will be used to verify the feasibility of the above study.

Aiming at establishing reliable and flexible data-driven designs of process monitoring and control systems, this paper presents the latest study on the identification of the data-driven realization of the stable image representation (SIR) in the plug-and-play process monitoring and control architecture (PnP-PMCA). The core of this study is the identification of the multiplication operators from the reference signal to the control input and the system output measurement. This work is essential to the future research on the data-driven PnP process monitoring and control system designs. The correctness and the effectiveness of the proposed identification approach have been verified and demonstrated through randomly generated system and designed closed-loop.

This paper presents the study on the data-driven process monitoring system design for the dynamic processes with periodic rectangular wave disturbance. The basic idea of the proposed methods are to identify the stable kernel representation (SKR) of the dynamic process by projecting the process data into the row subspace of the periodic rectangular wave disturbance. With the help of the projection, the kernel subspace of the system can be further determined. Based on the identified data-driven SKR, fault detection are developed. The performance and effectiveness of the proposed scheme is verified and demonstrated through the numerical study on randomly generated systems.

This paper presents the latest study on the data-driven process monitoring system design for the dynamic processes with sinusoidal disturbance. In the previous study, it is understood that the row space of the deterministic disturbance is essential to the subspace method aided data-driven design. Based on the previous study, this paper first determines the row space of sinusoidal disturbance. By projecting the process data into the determined subspaces, the fault detection systems can be designed based on the identified kernel subspace of the system. The performance and effectiveness of the proposed scheme are verified and demonstrated through the numerical study on randomly generated systems.

An intelligent data-driven predictive control strategy is proposed in this paper. The predictive controller is designed by combining predictive control and local weighted projection regression. The presented control strategy needs less prior knowledge and has fewer parameters that are hard to determine compared to other data-driven predictive controller, e.g., the one in dynamic partial least square (PLS) framework. Furthermore, the proposed predictive controller performs better in the control of nonlinear processes and is able to update its parameters based on the online data. The predictive model validity and intelligence of the control strategy are guaranteed by the online updating strategy to a certain degree. The control performance of the proposed predictive controller against the model predictive control (MPC) in dynamic PLS framework is illustrated through the simulation of a typical numerical example and the benchmark of a continuous stirred tank heater system. It can be observed from the simulation that the proposed MPC strategy has higher prediction precision and stronger ability in coping with nonlinear dynamic processes which are quite common in practical applications, for instance, the industrial process.

A data-driven model predictive control (MPC) that combines modified partial least squares (PLSs) and MPC is proposed in this paper. A theoretical comparison among traditional MPC, MPC in PLS framework and in modified PLS framework is presented, which demonstrates that the proposed MPC approach has high prediction precision and the ability in coping with dynamics in the process compared to MPC in traditional PLS framework. Furthermore, the proposed MPC requires no prior knowledge, and the simplicity in computation makes it possible to update the prediction model online. The model validity and intelligence of the control strategy are guaranteed by the model updating strategy to a certain degree. Steady-state performance and dynamic response of the proposed MPC is testified through a tracking control simulation of the benchmark of a continuous stirred tank heater system, which illustrates that the advantages of the proposed MPC.

The robust parameter and output estimation for linear parameter varying (LPV) time-delay system with output data contaminated with outliers and subjected to randomly missing measurements are considered in this paper. The outliers, missing data, and the time-delay are widely existed in practical industry and have imposed extra difficulties on complex process modeling. The robust probability model to describe the LPV time-delay system is constructed with the student's t -distribution and the estimation problems are formulated in the framework of generalized expectation-maximization algorithm. The time-delay and parameter varying process properties, the outliers, and randomly missing measurements are taken into consideration comprehensively in the derivations of proposed algorithm and the unknown model parameters, scale parameter, degree of freedom parameter, the time-delay, and the noise-free output data are estimated simultaneously. The numerical example and a practical chemical process are used to present the efficacy of proposed algorithm.

Data-driven fault detection plays an important role in industrial systems due to its applicability in case of unknown physical models. In fault detection, disturbances must be taken into account as an inherent characteristic of processes. Nevertheless, fault detection for nonlinear processes with deterministic disturbances still receive little attention, especially in data-driven field. To solve this problem, a just-in-time learning-based data-driven (JITL-DD) fault detection method for nonlinear processes with deterministic disturbances is proposed in this paper. JITL-DD employs JITL scheme for process description with local model structures to cope with processes dynamics and nonlinearity. The proposed method provides a data-driven fault detection solution for nonlinear processes with deterministic disturbances, and owns inherent online adaptation and high accuracy of fault detection. Two nonlinear systems, i.e., a numerical example and a sewage treatment process benchmark, are employed to show the effectiveness of the proposed method.

In the deployment of interval type-2 fuzzy systems, one of the most important steps is the type reduction. The commonly used center of sets type reducer requires the solution of two nonlinear constrained optimization problems. Frequently used approaches to solve them are the Karnik-Mendel algorithms and their variants. However, these algorithms suffer from the need for sorting, which is known to be computationally very expensive. Using the reformulations proposed in this paper for center of sets type reducer, it is possible to eliminate the need for sorting. This makes interval type-2 fuzzy systems more appropriate for cost-sensitive real-time applications. Extensive simulations are presented to illustrate the faster convergence speed of the proposed method over six other enhanced variants of the Karnik-Mendel algorithm as applied to center of sets type reduction of interval type-2 fuzzy systems.

Accurate wind speed forecasting is a fundamental requirement for large-scale integration of wind power generation. However, the intermittent and stochastic nature of wind speed makes this task challenging. Artificial neural networks (ANNs) are widely used in this area; however, they may fail to provide the accuracy that may be required. This is due to applying shallow architectures with error-prone hand-engineered features. This paper proposes a deep neural network (DNN) architecture with stacked autoencoder (SAE) and stacked denoising autoencoder (SDAE) for ultrashort-term and short-term wind speed forecasting. Autoencoders (AEs) are applied for unsupervised feature learning from the unlabeled wind data and a supervised regression layer is applied at the top of the AEs for wind speed forecasting. Several uncertain factors exist in the wind data that degrade the accuracy of current methodologies. In order to improve the accuracy, rough neural networks are incorporated in the proposed deep learning models to develop novel rough extensions of SAE and SDAE that are robust to wind uncertainties. Experimental results show that the proposed rough DNN models outperform classic DNNs and previous models that apply shallow architectures in the view of lower RMSE and mean absolute error measurements.

This paper presents an adaptive neural network (NN)-based fault-tolerant control approach for the compensation of actuator failures in nonlinear systems with time-varying delay. The novelty of this paper lies in the fact that both the lock in place and loss of effectiveness faults, unmodeled dynamics, and dynamic disturbances are catered for simultaneously. Furthermore, this is achieved by the adaptation of only one parameter, which simplifies the computation of the control effort, and therefore extends its applicability. In the approach, the Razumikhin lemma and a dynamic signal are employed. It is shown that the output of the system converges to a neighborhood of the reference signal and the semiglobal boundedness of all signals is guaranteed. A simulation example is used to illustrate the validity and efficacy of the approach.

This paper focuses mainly on the approximation-based fuzzy adaptive fault-tolerant control problem for nonlinear systems with unmodeled dynamics and unknown control directions. With the Nussbaum gain technique and a dynamic signal introduced, the difficulties from the unknown control directions and unmodeled dynamics are successfully overcome. Then, by taking advantage of the adaptive fuzzy control method and backstepping technology, we develop a fuzzy adaptive failure compensation control strategy and guarantee the semi-global boundedness for all signals. A simulation example is carried out to demonstrate the validity of the theoretical findings.

This work aims to study the fault classification problem in complicated industrial processes. Two modified multi-classification methods of Support Vector Machine (SVM), i.e., Principal Component Analysis based Support Vector Machine (PCA-SVM) as well as Kernel Principal Component Analysis based Support Vector Machine (KPCA-SVM), are respectively proposed to classify multi-fault for the underlying process. The continuous stirred tank heater (CSTH) benchmark is adopted in simulation to validate the effectiveness of the proposed approaches. Simulation results indicate that compared with the original PCA-SVM, KPCA-SVM generates a higher classification rate for the underlying process at the cost of larger computation loads.

In this paper, a fuzzy spiking neural network structure is developed for the wheel slip regulation problem of an Antilock Braking System. Sliding mode control theory is utilized in the derivation of the update rules for the neural network's weights as well as the parameters of the fuzzy membership functions. Gaussian membership functions are used to convert the sensor readings into the neural networks inputs and the spike response model is employed to denote the effect of the incoming spikes on the postsynaptic membrane potential. The use of the Lyapunov stability method for the derivation of the parameter update rules leads to a stable system response even in the existence of external disturbances.

The articles in this special issue present the latest developments and achievements in industrial cyber-physical systems (ICPSs). The papers in this issue cover key areas on architecture, design,enabling technologies, and applications of ICPSs. Additionally, they present emerging trends and visions of ICPSs for future investigations.

In this paper, a novel H∞-based adaptive fuzzy control is presented for the synchronization of fractional-order chaotic systems. A self-evolving nonsingleton type-2 fuzzy neural network (SE-NST2FNN) is proposed for the estimation of the unknown functions in the dynamics of the system. The effects of the approximation error and the external disturbances are eliminated by designing an adaptive compensator, such that the H∞norm of the synchronization error is minimized and asymptotically stability is achieved. The consequent parameters of SE-NST2FNN are tuned based on the adaptation laws that are derived from Lyapunov stability analysis. The antecedent part and the rule database of SE-NST2FNN are optimized based on a clustering method and the modified invasive weed optimization algorithm, respectively. The effectiveness of proposed control scheme is verified by simulation examples.

Modeling stage of complex aerial vehicles requires tremendous man power and expertise because of their highly nonlinear dynamics as well as complex inter couplings. In this paper, we investigate a model free controller design which benefits from type-2 fuzzy neural networks with elliptic type-2 fuzzy membership functions to control a 2-DOF helicopter without the need of a priori knowledge about the mathematical model for the system. In order to train the parameters of the consequent part of the type-2 fuzzy neural network, a cost function based on the integral of the square of the sliding surface is defined. The solution of this cost function is an optimal training algorithm for the parameters of the consequent part of the type-2 fuzzy neural network. The simulation results show that having neither a priori knowledge about the mathematical model of the system nor its parameters, the proposed control algorithm is able to track the reference signals for both yaw and pitch angles by eliminating the steady state error. In addition, the simulation results show the superiority of the proposed controller over its type-1 counterpart in the presence of measurement noise in the system.

This paper aims to discuss the H∞control problem of nonlinear stochastic switched systems in case where both global asymptotically stable in the mean (GASiM) subsystems and unstable subsystems coexist. An average dwell time (ADT) scheme is established to show us that the system is GASiM, if the activation time of GASiM subsystems is comparatively longer than that of unstable ones. Further, some conditions upon the H∞performance of the stochastic switched system are provided. The effectiveness of the proposed result is illustrated by a simulation example.

We are living in an era of data deluge and as a result, the term ``big data'' is appearing in many contexts, from meteorology, genomics, complex physics simulations, biological and environmental research, finance and business to healthcare. As the name implies, big data literally means large collections of data sets containing abundant information. However, it has some special characteristics that distinguish it from “very large data”or “massive data”that are simply enormous collections of simple-format records, typically equivalent to enormous spreadsheets. Big data, being generally unstructured and heterogeneous, is extremely complex to deal with via traditional approaches, and requires real-time or almost real-time analysis. A short definition can therefore be that “big data” refers to data sets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze.

A novel type-2 fuzzy membership function (MF) in the form of an ellipse has recently been proposed in literature, the parameters of which that represent uncertainties are de-coupled from its parameters that determine the center and the support. This property has enabled the proposers to make an analytical comparison of the noise rejection capabilities of type-1 fuzzy logic systems with its type-2 counterparts. In this paper, a sliding mode control theory-based learning algorithm is proposed for an interval type-2 fuzzy logic system which benefits from elliptic type-2 fuzzy MFs. The learning is based on the feedback error learning method and not only the stability of the learning is proved but also the stability of the overall system is shown by adding an additional component to the control scheme to ensure robustness. In order to test the efficiency and efficacy of the proposed learning and the control algorithm, the trajectory tracking problem of a magnetic rigid spacecraft is studied. The simulations results show that the proposed control algorithm gives better performance results in terms of a smaller steady state error and a faster transient response as compared to conventional control algorithms.

Two major challenges in networked control systems are the time-varying networked-induced delays and the packet losses. To alleviate these problems, this study presents a novel fuzzy sliding mode controller, where a fuzzy system is used to estimate the nonlinear dynamical system online, and the networked-induced delay is handled by Pade approximation. The problem of packet losses is handled by viewing them as large time-varying delays in the system. The sliding mode-based design procedure used ensures the stability and the robustness of the proposed controller in the presence of disturbances and time-varying networked-induced time delays. Using an appropriate Lyapunov function, it is proved that the tracking error converges to the neighborhood of zero asymptotically. Furthermore, since the adaptation laws of the parameters are derived by using of the Lyapunov function, these laws are also found to be stable. Simulation results show that the proposed fuzzy sliding mode controller is capable of controlling nonlinear dynamical systems over a network, which is subject to bounded external disturbances, time-varying network-induced delays, and packet losses with adequate performance.

This paper investigates the problem of robust model predictive control (RMPC) with saturations and packet dropouts. In this model, polytopic uncertainties are adopted to describe the inconsistency arising from the discretization process of sampling, while the occurrence probabilities of packet dropouts are time-varying and saturations are taken into account to describe input and output signals. The problem of exponential RMPC with saturations and packet dropouts is solved and characterized by a convex optimization problem. The developed results of RMPC are then applied to networked flotation processes, which are made up of three layers: direct control layer, set-point control layer, and optimization layer. The RMPC is used for compensating the output information from the optimization layer to the direct control layer such that the desired economic objective can be achieved. Simulations are presented to show the effectiveness of the proposed method.

Two-mode adaptive controllers have two phases of operation: a learning phase and an operation phase. This paper presents a two-mode indirect adaptive control approach for the synchronization of chaotic systems, using a hierarchical interval type-2 fuzzy neural network (HT2FNN). Its contribution to the existing literature is the adaptation laws derived for the parameters of the membership functions, based on Lyapunov stability analysis. Since, in hierarchical case, each T2FNN has only two inputs, the computing of the derivatives is much simpler than the case in classical interval type-2 FNN. Moreover, a novel approach is presented for the compensation of the approximation error. The tuning of the parameters of the membership functions (MF) and the use of an interval type-2 FNN ensures that the estimation error is very small so that it can be negligible. Furthermore, the number of MF required is seen to be less than that needed with type-1 fuzzy sets. The simulation results confirm the efficacy of the proposed scheme in the synchronization of a uncertain nonidentical chaotic systems.

Mechatronic systems operating in industrial environments are subject to a variety of threats because of harsh conditions. Industrial systems usually use commercial off-the shelf (COTS) equipment which are not robust and safe against hostile conditions and therefore require fault-tolerance considerations. This paper presents a novel and efficient method for online detection of control flow errors, called software-based control flow checking (SCFC). It is implemented purely in software and does not manipulate the hardware architecture of the system. Redundant instructions and signatures are embedded into the program at compile time and are utilized for control flow checking at run time. The signatures of the basic blocks are derived from the program graph. It is shown in the paper that SCFC method can increase single detection capability to 14.7% and the fault coverage to 6.12% averagely in comparison with other methods without any increase in memory and performance overheads. In the paper, besides experimental evaluations, analytical evaluations are also carried out, based on probability principles. The detection ability of each method used is thus computed. These computations verify the experimental results and show that SCFC can detect more errors than other methods suggested in literature. Considering the memory limitations in some (such as space) applications and the trend towards the requirement for faster execution of programs, we suggest a novel metric called fitness parameter which incorporates these. It is a better measure than the previously proposed ones since it considers the fault coverage, the memory overhead and the execution time (performance overhead) of each method simultaneously, as well as the detection capability.

This paper presents a data-driven method for the task of fault detection in nonlinear systems. In the proposed approach, locally weighted projection regression (LWPR) is employed to serve as a powerful tool for modeling the nonlinear process with locally linear models. In each local model, partial least squares (PLS) regression is performed and PLS-based fault detection scheme is applied to monitor the regional model. The diagnosis for the global process is based on the normalized weighted mean of all the local models. Both conventional and quality-related statistical indicators are designed to compute the test statistics. Two nonlinear systems, a numerical one and a benchmark, are used to demonstrate the effectiveness of the proposed method.

In this paper, a deterministic sensor deployment method based on wavelet transform (WT) is proposed. It aims to maximize the quality of coverage of a wireless sensor network while deploying a minimum number of sensors on a 3-D surface. For this purpose, a probabilistic sensing model and Bresenham's line of sight algorithm are utilized. The WT is realized by an adaptive thresholding approach for the generation of the initial population. Another novel aspect of the paper is that the method followed utilizes a Cat Swarm Optimization (CSO) algorithm, which mimics the behavior of cats. We have modified the CSO algorithm so that it can be used for sensor deployment problems on 3-D terrains. The performance of the proposed algorithm is compared with the Delaunay Triangulation and Genetic Algorithm based methods. The results reveal that CSO based sensor deployment which utilizes the wavelet transform method is a powerful and successful method for sensor deployment on 3-D terrains.

Model-free approaches such as Artificial Neural Networks and Fuzzy Controllers are widely used in the control of Antilock Braking System (ABS) due to its strongly nonlinear structure and uncertainties involved. In this paper the design of a Spiking Neural Network (SNN) controller is considered for the regulation of the wheel slip value at its optimum value. For the training of the network a gradient descent based approach is followed. To formulate the generation of a new spike train from the incoming spikes, the Spike Response Model (SRM) is used. Delay coding is utilized to convert real numbers into spike times. The control algorithm is applied to a quarter vehicle model, and it is verified through simulations indicating fast convergence and good performance of the designed controller.

The optimal selection of parameters for time-delay embedding is crucial to the analysis and the forecasting of chaotic time series. Although various parameter selection techniques have been developed for conventional uniform embedding methods, the study of parameter selection for nonuniform embedding is progressed at a slow pace. In nonuniform embedding, which enables different dimensions to have different time delays, the selection of time delays for different dimensions presents a difficult optimization problem with combinatorial explosion. To solve this problem efficiently, this paper proposes an ant colony optimization (ACO) approach. Taking advantage of the characteristic of incremental solution construction of the ACO, the proposed ACO for nonuniform embedding (ACO-NE) divides the solution construction procedure into two phases, i.e., selection of embedding dimension and selection of time delays. In this way, both the embedding dimension and the time delays can be optimized, along with the search process of the algorithm. To accelerate search speed, we extract useful information from the original time series to define heuristics to guide the search direction of ants. Three geometry- or model-based criteria are used to test the performance of the algorithm. The optimal embeddings found by the algorithm are also applied in time-series forecasting. Experimental results show that the ACO-NE is able to yield good embedding solutions from both the viewpoints of optimization performance and prediction accuracy.

Networked control systems (NCSs) have, in recent years, brought many innovative impacts to control systems. However, great challenges are also met due to the network-induced imperfections. Such network-induced imperfections are handled as various constraints, which should appropriately be considered in the analysis and design of NCSs. In this paper, the main methodologies suggested in the literature to cope with typical network-induced constraints, namely time delays, packet losses and disorder, time-varying transmission intervals, competition of multiple nodes accessing networks, and data quantization are surveyed; the constraints suggested in the literature on the first two types of constraints are updated in different categorizing ways; and those on the latter three types of constraints are extended.

The articles in this special section focus on advancements in theories and industrial applications of networked control systems in the industrial informatics industry.

Automatic guidance of agricultural vehicles would lighten the job of the operator, while accuracy is needed to obtain an optimal yield. Accurately navigating a tractor consists of controlling different dynamic subsystems (steering and speed). Instead of modeling the subsystem interaction prior to model-based control, we have developed a control algorithm which learns the interactions on-line from the measured feedback error. In this approach, a PD controller is working in parallel with a type-2 fuzzy neural network. While the former ensures the stability of the related subsystem, the latter learns the system dynamics and becomes the leading controller. In this study, two combinations of a PD controller with a type-2 fuzzy neural network are implemented: one for the yaw dynamics and one for the traction dynamics. The interactions between these subsystems are thus not taken into account explicitly, but considered as disturbances to be handled by the subsystem controllers. A novel sliding mode control theory-based learning algorithm is used to train the type-2 fuzzy neural networks, and the convergence of the parameters is shown by using a Lyapunov function.

This paper aims at further investigating the pinning impulsive control of complex dynamical networks. In detail, we introduce a novel approach for analyzing the synchronization stability of complex networks with impulsive signals. Moreover, we prove that one random selective impulsive controller can always pin a directed strongly connected complex network to its homogeneous solution under suitable coupling strength and impulsive signal. A simple example is then given to validate the above theoretical results.

In conventional fuzzy modeling and control, to obtain an optimal fuzzy system, a commonly used approach is to tune the parameters of the membership functions. However, if the membership functions carry significant expert knowledge about the system, this may be lost or distorted during the optimization process. In order to prevent such a loss of valuable information, parameterized conjunction operators may be used and their parameters can be tuned instead. In this paper such an approach is adopted to optimize a type-1 fuzzy neural system (FNS), used for slip control of a Quarter Car Model (QCM). The simulation results presented indicate the efficacy of the approach in meeting the desired objectives even under noisy conditions.

The rapid development of radio frequency identification (RFID) technology creates the challenge of optimal deployment of an RFID network. The RFID network planning (RNP) problem involves many constraints and objectives and has been proven to be NP-hard. The use of evolutionary computation (EC) and swarm intelligence (SI) for solving RNP has gained significant attention in the literature, but the algorithms proposed have seen difficulties in adjusting the number of readers deployed in the network. However, the number of deployed readers has an enormous impact on the network complexity and cost. In this paper, we develop a novel particle swarm optimization (PSO) algorithm with a tentative reader elimination (TRE) operator to deal with RNP. The TRE operator tentatively deletes readers during the search process of PSO and is able to recover the deleted readers after a few generations if the deletion lowers tag coverage. By using TRE, the proposed algorithm is capable of adaptively adjusting the number of readers used in order to improve the overall performance of RFID network. Moreover, a mutation operator is embedded into the algorithm to improve the success rate of TRE. In the experiment, six RNP benchmarks and a real-world RFID working scenario are tested and four algorithms are implemented and compared. Experimental results show that the proposed algorithm is capable of achieving higher coverage and using fewer readers than the other algorithms.

Uncertainty is an inevitable problem in real-time industrial control systems and, to handle this problem and the additional one of possible variations in the parameters of the system, the use of sliding mode control theory-based approaches is frequently suggested. In this paper, instead of using a conventional sliding mode controller, a sliding mode control theory-based learning algorithm is proposed to train the fuzzy neural networks in a feedback-error-learning structure. The parameters of the fuzzy neural network are tuned by the proposed algorithm not to minimize the error function but to ensure that the error satisfies a stable equation. The parameter update rules of the fuzzy neural network are derived, and the proof of the learning algorithm is verified by using the Lyapunov stability method. The proposed method is tested on a real-time servo system with time-varying and nonlinear load conditions.

This study presents a novel direct model reference fuzzy controller. It relaxes the special conditions on the reference model that is required by some of the approaches described in the literature, as well as covering a more general class of Takagi-Sugeno (T-S) systems. The stability of the proposed method is proved using a proper Lyapunov function. In addition, the effects of modeling errors on the proposed controller are considered, and a robust modification algorithm to alleviate this problem is introduced and analyzed. The proposed method is then simulated on a flexible joint robot in a feedback linearization form and on Chua's chaotic electrical circuit. Finally, it is implemented and tested on a nonlinear dc motor with nonlinear state-dependent disturbance.

In this paper, the noise reduction property of type-2 fuzzy logic (FL) systems (FLSs) (T2FLSs) that use a novel type-2 fuzzy membership function is studied. The proposed type-2 membership function has certain values on both ends of the support and the kernel and some uncertain values for the other values of the support. The parameter tuning rules of a T2FLS that uses such a membership function are derived using the gradient descend learning algorithm. There exist a number of papers in the literature that claim that the performance of T2FLSs is better than type-1 FLSs under noisy conditions, and the claim is tried to be justified by simulation studies only for some specific systems. In this paper, a simpler T2FLS is considered with the novel membership function proposed in which the effect of input noise in the rule base is shown numerically in a general way. The proposed type-2 fuzzy neuro structure is tested on different input-output data sets, and it is shown that the T2FLS with the proposed novel membership function has better noise reduction property when compared to the type-1 counterparts.

Type-2 fuzzy logic systems are proposed as an alternative solution in the literature when a system has a large amount of uncertainties and type-1 fuzzy systems come to the limits of their performances. In this study, an adaptive type-2 fuzzy-neuro system is designed for the position control of a servo system with an intelligent sensor. The sensor gives different resistance values with respect to the stretch of it, and it is supposed to be used in an robotic arm position measurement system. These kinds of sensors can be used in human-assistance robots that have soft surfaces in order not to damage the humans. However, these sensors have time-varying gains and uncertainties that are not very easy to handle. Moreover, they generally have a hysteresis on their input-output relations. The simulation results show that the control algorithm developed gives better performances when compared to conventional type-1 fuzzy controllers on such a highly nonlinear, uncertain system.

In this paper, we present a new method of interval fuzzy model identification. Unlike the previously introduced methods, this method uses recursive least square methods to estimate the parameters. The idea behind interval fuzzy systems is to introduce optimal lower and upper bound fuzzy systems that define the band which contains all the measurement values. This results in lower and upper fuzzy models or a fuzzy model with a set of lower and upper parameters. The model is called the interval fuzzy model (INFUMO). This type of modeling has various applications such as nonlinear circuits modeling. There has been tremendous amount of activities to use linear matrix inequality based techniques to design a controller for this type of fuzzy systems. The fact that the actual desired data must lie between upper and lower fuzzy systems, introduces some constrains on the identification process of the lower and upper fuzzy systems. We would introduce a cost function which includes the violation of constrains and try to find an adaptation law which minimizes this cost function and at the same time tries to be less conservative.

The aircraft arrival sequencing and scheduling (ASS) problem is a salient problem in air traffic control (ATC), which proves to be nondeterministic polynomial (NP) hard. This paper formulates the ASS problem in the form of a permutation problem and proposes a new solution framework that makes the first attempt at using an ant colony system (ACS) algorithm based on the receding horizon control (RHC) to solve it. The resultant RHC-improved ACS algorithm for the ASS problem (termed the RHC-ACS-ASS algorithm) is robust, effective, and efficient, not only due to that the ACS algorithm has a strong global search ability and has been proven to be suitable for these kinds of NP-hard problems but also due to that the RHC technique can divide the problem with receding time windows to reduce the computational burden and enhance the solution's quality. The RHC-ACS-ASS algorithm is extensively tested on the cases from the literatures and the cases randomly generated. Comprehensive investigations are also made for the evaluation of the influences of ACS and RHC parameters on the performance of the algorithm. Moreover, the proposed algorithm is further enhanced by using a two-opt exchange heuristic local search. Experimental results verify that the proposed RHC-ACS-ASS algorithm generally outperforms ordinary ACS without using the RHC technique and genetic algorithms (GAs) in solving the ASS problems and offers high robustness, effectiveness, and efficiency.

In this paper the identification and control of dynamic plants using type-2 TSK fuzzy neural system (FNS) is considered. The systems constructed on the base of type-1 fuzzy systems cannot directly handle the uncertainties associated with information or data in the knowledge base of the process. One possible way to alleviate the problem is to resort to the use of type-2 fuzzy systems. In this paper, a type-2 TSK fuzzy neural system (FNS), is proposed and its gradient learning algorithm is derived. Its performance for identification and control of time-varying plants is evaluated and compared with other approaches seen in the literature; the time-varying nature of the plants being handled as uncertainties in the plant coefficients which can be described by type-2 fuzzy sets.

The control of an antilock braking system (ABS) is a difficult problem due to its strongly nonlinear and uncertain characteristics. To overcome this difficulty, the integration of gray-system theory and sliding-mode control is proposed in this paper. This way, the prediction capabilities of the former and the robustness of the latter are combined to regulate optimal wheel slip depending on the vehicle forward velocity. The design approach described is novel, considering that a point, rather than a line, is used as the sliding control surface. The control algorithm is derived and subsequently tested on a quarter vehicle model. Encouraged by the simulation results indicating the ability to overcome the stated difficulties with fast convergence, experimental results are carried out on a laboratory setup. The results presented indicate the potential of the approach in handling difficult real-time control problems.

In this paper, a grey neuro-adaptive control algorithm is suggested for Antilock Braking Systems (ABS). The concept of grey system theory, which has a certain prediction capability, offers an alternative approach to conventional control methods. A multilayer neural network and a grey predictor, GM(1,1) model, are combined in the approach proposed in the paper. The grey neural network controller is examined under several different operating conditions and it is shown that the proposed control algorithm anticipates the upcoming values of wheel slip and optimal wheel slip, and takes the necessary action to keep the wheel slip at the desired value. The simulation results indicate that the proposed controller has the ability to control the nonlinear system accurately with little oscillations and with no steady-state error.

Antilock braking system (ABS) exhibits strongly nonlinear and uncertain characteristics. To overcome these difficulties, robust control methods should be employed. In this paper, a grey sliding mode controller is proposed to track the reference wheel slip. The concept of grey system theory, which has a certain prediction capability, offers an alternative approach to conventional control methods. The proposed controller anticipates the upcoming values of wheel slip, and takes the necessary action to keep wheel slip at the desired value. The control algorithm is applied to a quarter vehicle model, and it is verified through simulations indicating fast convergence and good performance of the designed controller. Simulated results are validated on real time applications using a laboratory experimental setup.

In this study, we have developed a novel method to obtain high resolution images using an ordinary digital camera. Using successively taken images, our fusion algorithm has a profound effect in the quality of the image which contains richer independent pixels than the originals do. This method, which aims to increase the spatial resolution by adding dependent data to the picture, is superior over classical super-resolution methods. Seven DOF industrial robotic arm, PA10-7C, by Mitsubishi is utilized for the experimental part of the work.

Two-wheeled cart falls under kinematically deficient manipulator when considered from cooperating manipulator approach. Kinematically deficient manipulators are those that have fewer degrees of freedom than necessary to achieve any admissible configuration in their operational space. When multiple manipulators, some or all of which are kinematically deficient, cooperate to perform a common task, the constrained forces at the contact points cannot be solved directly due to rank deficiency of the Jacobian. This paper addresses this challenge associated with the computation of constrained forces at the contact points by introducing a novel approach called "pseudo joint". Forward dynamical model utilizing pseudo joint has been driven for cooperating kinematically deficient manipulators. Dynamics of two-wheeled cart has been given to demonstrate the methodology.

In this paper, two different backstepping neural network (NN) control approaches are presented for a class of affine nonlinear systems in the strict-feedback form with unknown nonlinearities. By a special design scheme, the controller singularity problem is avoided perfectly in both approaches. Furthermore, the closed loop signals are guaranteed to be semiglobally uniformly ultimately bounded and the outputs of the system are proved to converge to a small neighborhood of the desired trajectory. The control performances of the closed-loop systems can be shaped as desired by suitably choosing the design parameters. Simulation results obtained demonstrate the effectiveness of the approaches proposed. The differences observed between the inputs of the two controllers are analyzed briefly.

Complexity of learning dynamics constitutes a prime difficulty in online neurocontrol schemes involving gradient computations in parameter update rules. This is because such complexities can make closed loop system sensitive to uncertainties. In this paper, we discuss a learning control approach, which is based on the sliding mode control (SMC) techniques instead of gradient computations. Due to properties of SMC, learning process becomes robust to uncertainties. In order to test the control scheme, we have chosen a robotic manipulator as the test bed. Experimental results show that the control approach achieves a good tracking performance.

The features of a novel robust adaptive learning algorithm in analog multilayer feed forward networks are presented. It implements sliding mode control strategy. The zero level set of the learning error is considered as a sliding surface in the space of neural network learning parameters. A sliding mode trajectory can be brought on and reached in finite time on such a sliding manifold. The algorithm is applied to on-line learning of a non-monotonic function and manipulator forward dynamics identification. The learning neural structures come into some of the advantages of variable structure systems, such as high speed of learning and robustness.

Gives a survey of fuzzy rule base reduction methods. The complexity reduction methods originate from two aspects depending on different design methodologies. The first model design type comes from the original idea of Zadeh, it proposes models which are built based on expert knowledge, hence the rule base is set up manually. These models feature linguistic, and hence semantically interpretable fuzzy terms, and rules with fuzzy sets as consequents. Secondly, data-driven fuzzy model design has become more popular. For fitting the model to the approximated function, these models, usually having rules with consequents which are linear function of the inputs, use tremendously large number of rules, and do not take into account the complexity and interpretability of the model. This feature also emerged in the issue of rule base reduction for such systems. The paper aims at summarizing the efforts in the complexity reduction field briefly.

An approach to fuzzy modeling based on the tuning of parametric conjunction operations is proposed. First, some methods for the construction of parametric generalized conjunction operations simpler than the known parametric classes of conjunctions are considered and discussed. Second, several examples of function approximation by fuzzy models, based on the tuning of the parameters of the new conjunction operations, are given and their approximation performances are compared with the approaches based on a tuning of membership functions and other approaches proposed in the literature. It is seen that the tuning of the conjunction operations can be used for obtaining fuzzy models with a sufficiently good performance when the tuning of membership functions is not possible or not desirable.

A general backpropagation algorithm is proposed for feedforward neural network learning with time varying inputs. The Lyapunov function approach is used to rigorously analyze the convergence of weights, with the use of the algorithm, toward minima of the error function. Sufficient conditions to guarantee the convergence of weights for time varying inputs are derived. It is shown that most commonly used backpropagation learning algorithms are special cases of the developed general algorithm.

The novel features of an adaptive PID-like neurocontrol scheme for nonlinear plants are presented. The controller tuning is based on an estimate of the command-error on its output by using a neural predictive model. A robust online learning algorithm, based on the direct use of sliding mode control (SMC) theory is applied. The proposed approach allows handling of the plant-model mismatches, uncertainties and parameters changes. The results show that both the plant model and the controller inherit some of the advantages of SMC, such as high speed of learning and robustness.

This paper discusses the stabilizability of artificial neural networks trained by utilizing the gradient information. The method proposed constructs a dynamic model of the conventional update mechanism and derives the stabilizing values of the learning rate. This is achieved by integrating the error backpropagation (EBP) technique with variable structure systems (VSS) methodology, which is well known with its robustness to environmental disturbances. In the simulations, control of a three degrees of freedom anthropoid robot is chosen for the evaluation of the performance. For this purpose, a feedforward neural network structure is utilized as the controller.

In this paper, a novel method for the establishment of a sliding mode in a nonlinear system is presented. The method discussed aims to minimize a cost measure, which is a function of the switching surface. For this purpose, an adaptive fuzzy controller is selected and the parameters of the defuzzifier are adjusted such that the cost is minimized and the system is enforced to behave in sliding mode. The paper considers a 2-DOF direct drive robotic manipulator as the test bed and a standard fuzzy system is used as the controller. The results obtained clearly stipulate that a sliding motion can be achieved by appropriately tuning the parameters of the controller.

This paper discusses the stabilizability of artificial neural networks trained by utilizing the gradient information. The method proposed constructs a dynamic model of the conventional update mechanism and derives the stabilizing values of the learning rate. The stability in this context corresponds to the convergence in adjustable parameters of the neural network structure. It is shown that the selection of the learning rate as imposed by the proposed algorithm results in stable training in the sense of Lyapunov. Furthermore, the algorithm devised filters out the high frequency dynamics of the gradient descent method. The excitation of this dynamics typically occurs in the presence of noise and abruptly changing the parameters of the mapping being learned. This adversely influences the learning performance that can be attained during a training cycle. A natural consequence following this excitation is divergence in parameter space. The method analyzed in this paper integrates the gradient descent technique with variable structure systems methodology, which is well known for its robustness to environmental disturbances. In the simulations, control of a three degrees of freedom anthropoid robot is chosen for the evaluation of the performance. For this purpose, a feedforward neural network structure is utilized as the controller. Highly nonlinear dynamics of the plant, existence of a considerable amount of observation noise, and the adverse effects of gravitational forces constitute the difficulties to be alleviated by the neurocontroller trained with the proposed method. In order to come up with a fair comparison, the results obtained with the pure gradient descent technique with the same initial conditions are also presented and discussed. © 2000 John Wiley & Sons, Inc.

Currently, fuzzy controllers are the most popular choice for hardware implementation of complex control surfaces because they are easy to design. Neural controllers are more complex and hard to train, but provide an outstanding control surface with much less error than that of a fuzzy controller. There are also some problems that have to be solved before the networks can be implemented on VLSI chips. First, an approximation function needs to be developed because CMOS neural networks have an activation function different than any function used in neural network software. Next, this function has to be used to train the network. Finally, the last problem for VLSI designers is the quantization effect caused by discrete values of the channel length (L) and width (W) of MOS transistor geometries. Two neural networks were designed in 1.5 microm technology. Using adequate approximation functions solved the problem of activation function. With this approach, trained networks were characterized by very small errors. Unfortunately, when the weights were quantized, errors were increased by an order of magnitude. However, even though the errors were enlarged, the results obtained from neural network hardware implementations were superior to the results obtained with fuzzy system approach.

Develops a training methodology for computationally intelligent systems utilizing gradient information in parameter updating. The devised scheme uses the first-order dynamic model of the training procedure and applies the variable structure systems approach to control the training dynamics. This results in an optimal selection of the learning rate, which is continually updated as prescribed by the adopted strategy. The parameter update rule is then mixed with the conventional error backpropagation method in a weighted average. The paper presents an analysis of the imposed dynamics, which is the response of the training dynamics driven solely by the inputs designed by a variable structure control approach. The analysis continues with the global stability proof of the mixed training methodology and the restrictions on the design parameters. The simulation studies presented are focused on the advantages of the proposed scheme with regards to the compensation of the adverse effects of the environmental disturbances and its capability to alleviate the inherently nonlinear behavior of the system under investigation. The performance of the scheme is compared with that of a conventional backpropagation. It is observed that the method presented is robust under noisy observations and time varying parameters due to the integration of gradient descent technique with variable structure systems methodology. In the application example studied, control of a two degrees of freedom direct-drive robotic manipulator is considered. A standard fuzzy system is chosen as the controller in which the adaptation is carried out only on the defuzzifier parameters.

In this study, a novel method for identification and control of nonlinear systems is developed. The method proposed realizes the dynamics of a system by employing the Runge-Kutta method at the upper level. The intermediate level of the strategy constructs the architecture utilizing an adaptive neuro fuzzy inference system. The overall system is able to imitate the behavior of a complex dynamic system with a few rules or to control the system with high accuracy. The proposed method has been applied to a two degrees of freedom direct drive SCARA robot.

On the basis of Lagrangian mechanics uniform structures made of simple and standardized procedures and closed form analytical formulas were previously used to develop an adaptive control for SCARA robots in dynamic interaction with an unmodeled environment. This standardized form contains well defined free parameters by tuning of which the complex effect of the behavior of the controlled system as well as that of the external interactions can be 'imperfectly' modeled and learned. These structures, free parameters and procedures play exactly the same role as that of the traditional artificial neural networks (ANNs) or fuzzy controllers: the structures and the procedures are fit for a wide class of problems more or less similar to each other, while parameter tuning corresponds to learning the concrete properties of a particular element of this wider set. While in the case of the standard soft computing methods there is no reliable a priori information on the number of the concrete elements in the uniform structures, the proposed method has definite indication for this by using the Lie parameters of the orthogonal group. In the initial stage of learning the proposed methods also use a standardized ancillary procedure, a very simple form of regression analysis based controller compensating the remnant errors whenever appropriate. In this paper certain details of the uniform control and parameter-tuning are discussed on the basis of simulation results. It can be concluded that the approach is promising therefore experimental investigations are under preparation.

It is argued that inference procedures of fuzzy models do not always require commutativity and associativity of the operations used. This raises the possibility of considering nonassociative and noncommutative conjunction and disjunction operations. Such operations are investigated in this paper and different methods for their generation are proposed. A number of new types of conjunction operations that are simpler than the known parametric classes of T-norms are given and, as an application example, the approximation of a function by a fuzzy inference system is considered.

In the application of sliding mode controllers, the main problem encountered is that a whole knowledge of the system dynamics (or inverse dynamics) and the system parameters is required to be able to compute the equivalent control. This is actually very rare in practice. In this paper, a feedforward neural network is proposed to compute the equivalent control. The weights of the net are updated such that the additional control term of the sliding mode converges to zero. Experimental studies carried out on a direct drive robot arm indicate that the proposed approach is a good candidate for trajectory control applications.

By using a fuzzy entropy approach, three sets of new generalized operators are presented. After a general discussion on fuzzy entropy, the concept of an elementary entropy function of a fuzzy set is introduced. Using this mapping, the generalized intersections and unions are defined as mappings that assign the least and the most fuzzy membership grade to each of the elements of the domain of the operators, respectively. It is shown that these operators can be constructed from the conventional min and max operations. Next, two modified sets of operations are introduced. The second part of the paper investigates the applicability of the new operators in fuzzy logic controllers. Simulations have been carried out so as to determine the effects of the operators on the performance of the fuzzy controllers. It is concluded that the first set of operators does not provide stable control, but the performance of the fuzzy controller can be improved by using the modified operations for a class of plants.

A number of approaches based on sliding mode control methodology are proposed in the literature for the position control of robotic manipulators. An important problem in this context is chattering, that is high frequency oscillations in the velocity. In this paper, a novel approach is considered which eliminates chattering if the controller parameters are set suitably. A fuzzy adaptation scheme is devised for the online adaptation of the parameters of this method. Simulation results show that the fuzzy rule based adaptation scheme ensures good controller performance without chattering.

A synergistic combination of neural networks with sliding mode control is proposed. As a result, the chattering is eliminated and error performance of SMC is improved. In such an approach, the determination of the structure of NN, i.e. number of layers, number of neurons at each layer, etc. does not come up as a problem because these are directly related to the SMC. A Lyapunov function is selected for the design of the SMC and gradient descent is used for weight adaptation of the neural network. The criterion that is minimized for gain adaptation is selected as the sum of the squares of the control signal and the sliding function. This novel approach is applied to control of a SCARA type robot manipulator and simulation results are given.

In this paper a theoretical approach to performance improvement of fuzzy logic robot controllers is presented. The approach is based on two new sets of T-operations introduced by the authors. The T-norms and T-conorms are defined as minimum and maximum entropy operations. Simulation has been carried out so as to compare the effects of the new and the conventional T-operators in case of a 4 DOF rigid-link flexible-joint SCARA type robot. It is concluded that by fixing the other parameters of the controller certain sets of T-operations can improve the performance of the controller.

This paper is concerned with the feedback linearization control of a flexible joint anthropoid robot. The theory to derive a linearizing control law for a certain type of dynamic equations is reviewed and the model for a particular manipulator is derived. Based on this model a linearization controller is designed and its tracking performance is compared with classical controllers.>

A vision-controlled robot conveyor tracking system is presented here. The vision system utilizes a constant in-time sensing field. In its normal mode of operation, this system takes a picture, analyses the image, recognises the object, extracts position information from it. Utilising this information along with the velocity information from the conveyor belt, the robot maneuvers to track and grasp the recognised object.>

A recently proposed method of control, namely model algorithmic control (MAC) or, equivalently, model predictive heuristic control (MPHC) is analyzed with a view to its implementation for the position control system. The formulation of the MPHC strategy to positional servo system is presented; both the regulators and the tracking problems are studied and the simulation and experimental results obtained indicate that the MPHC results in a good performance even under the conditions of large time-varying changes in the parameters of the system.