Real-time multi-agent internet data transmission enhances the service capabilities of diverse applications, however, it poses a risk of sensitive location information being compromised. This study proposes a privacy-enhanced multi-agent framework integrating a randomized masking algorithm with a prescribed-time fault-tolerant (PTFT) consensus control strategy. The core contribution is the design of a jointly generated dynamic masking mechanism, which uses random numbers from both parties to obfuscate sensitive state data. This approach enhances the security of transmitted network data through increased randomness while eliminating the non-zero constraint of traditional privacy protection methods at the initial time. The piecewise mask function design ensures immediate recovery of genuine states post-initial masking, achieving precision control without privacy compromise. Additionally, the PTFT controller enables semi-global consensus within the prescribed time despite actuator faults, and ensures the boundedness of all signals in the closed-loop systems. Finally, the algorithm simulation experiment verifies the effectiveness of the proposed scheme and compares it with the traditional privacy protection method.

This work addresses the nonfragile fault-tolerant control for power cyber-physical systems (CPSs) under denial-of-service (DoS) attacks, in which the cyber attacks are considered to be strongly concealed. In the fact of power CPSs frequently subject to various attacks during the operation, the innovation is to construct a double-layer stochastic process composed of a semi-Markov chain and a sequence of observed mode to analyze the DoS attacks from the viewpoint of hidden semi-Markov chain. To address potential actuator failures and controller gain perturbations, an observed-mode-dependent nonfragile control scheme is developed. By constructing a mode-dependent Lyapunov function that incorporates both attack modes and observed modes, sufficient conditions are derived to ensure mean-square stability of the closed-loop system through the semi-Markov kernel (SMK) approach, which systematically handles the stochastic characteristics of dwell time (DT) distributions under incomplete attack mode information. Finally, a simulation example demonstrates the validity of the proposed approach.

This article primarily investigates the tracking control problem of high-order uncertain nonlinear systems with odd-rational-power under false data injection (FDI) attacks and malicious attacks, based on the fully actuated system (FAS) theory. Due to the corruption of the state information by an additional attack signal, the true state information cannot be directly used for controller design. To mitigate the impact of unknown FDI attacks, a coordinate transformation is applied using the attacked state. In addition, using a piecewise smooth function approaching a saturation function, a new lemma is proposed to deal with the unknown control gain of the prescribed-time control input saturation and malicious attacks problem. Theoretical analysis demonstrates that the tracking errors converge in the prescribed time and all closed-loop system signals remain bounded. Finally, a numerical example is provided, along with a practical case study based on a single-link robotic manipulator, to validate the effectiveness of the proposed method.

In this article, to augment benthonic organism features and suppress underwater background noises, simultaneously, a fuzzy channel–spatial attention-based benthonic organism detection (FCSA-BOD) scheme is proposed. Main contributions are as follows: 1) with the aid of spatial global average and maximum pooling, fuzzy channel attention (FCA) is originated to adaptively recalibrate channel responses by fusing discriminative and textural channel attention maps, which are derived from two independent single-hidden-layer feedforward networks, such that benthonic organism and background feature maps can be strengthened and suppressed, respectively; 2) by exploiting channel global average and maximum pooling, fuzzy spatial attention (FSA) is created to highlight spatial regions associated with benthonic organisms by fusing multiple spatial attention maps possessing completely different receptive fields, such that different-scale benthonic organism features on the same feature map can be significantly augmented, simultaneously; and 3) the FCSA-BOD scheme is eventually established in a modular manner by integrating FCA and FSA modules within a deep learning framework. Comprehensive experiments demonstrate that the proposed FCSA-BOD scheme outperforms state-of-the-art underwater detection approaches.

A data-drivenH∞control method with dynamic performance constraints is proposed for the problem of region stabilization control in the case of unknown system matrix. By establishing a system characterization model based on measured data, the generalized pole configuration theory is ingeniously combined with the data-driven control framework. Firstly, the real-time data generated from the system operation is used to construct a data-driven dynamic characterization. A generalized pole constraint-based regional constraints condition is established to accurately regulate the state convergence rate and damping response characteristics while ensuring the stability of the closed-loop system. The classicalH∞control framework is further integrated to construct a novel data-drivenH∞performance criterion to achieve the synergistic optimization of dynamic performance constraints and disturbance suppression capability. The method gets rid of the dependence on the a priori model of the system and solves the co-optimization problem of unknown system dynamic performance regulation and robustness enhancement under a unified framework. Numerical simulations and power system control case validations show that the integrated control performance of uncertain complex systems is significantly improved.

This study addresses the adaptive prescribed-time consensus tracking control problem for high-order multi-agent nonlinear systems with an improved privacy protection mechanism. A distinctive feature of the control method lies in the utilization of the fully actuated system approach to study high-order multi-agent systems, enabling system control without the need to simplify the high-order systems into the first-order systems. For agents requiring state information protection, the flexible privacy protection treats the output mask function as the transformation function, thereby protecting the privacy of all signals in the systems and allowing users to define the protection time. In addition, a prescribed-time scale function suitable for high-order systems is proposed by incorporating a constant term to avoid the singularity issue. This prescribed-time tracking control strategy ensures that the synchronization error converges to the prescribed region within the prescribed time, and prescribed time aligns with user-defined time in the privacy protection mechanism. Finally, the superiority of the proposed control method is verified through a numerical simulation comparison with different privacy protection method.

In this study, we propose an innovative prescribed-time consensus control strategy for nonlinear strict-feedback multi-agent systems (MASs) with privacy protection requirements. Firstly, compared with the existing privacy protection strategies, the mask function adopted in this paper remains unknown to all agents, including the sender, thus greatly improving the security level of information transmission. Secondly, the existing related research results basically overlook prescribed-time control in the context of privacy preservation, based on the backstepping method, a prescribed time performance function is adopted in this paper, so that the systems can make the tracking error within the defined accuracy range within a user-defined time. Finally, through the verification of MATLAB simulation experiments, the proposed control strategy not only effectively realizes the privacy-preserving consensus control of multi-agent systems, but also shows better control performance compared with the existing schemes. Note to Practitioners—This paper aims to develop a mask privacy protection prescribed-time control algorithm for information transmission between multiple agents. In the automation industry, the demand for privacy protection in multi-agent systems is critical, necessitating the implementation of robust measures during agent collaboration and data sharing to safeguard data confidentiality. Employing advanced privacy-preserving technologies is essential to prevent the exposure of sensitive information, thereby ensuring the security of corporate secrets and operational integrity, in compliance with the evolving stringent privacy regulations. In addition, prescribed-time control enables users to achieve preset accuracy within a predefined time, reducing industrial resource consumption and improving resource utilization in the automation industry.

This article addresses the variable convergence rate stability problem for nonlinear impulsive stochastic systems (NISSs). To solve the issue, a novel methodology of sliding mode surface design is presented by combining the definition of interval stability with the T-S fuzzy technique. A pioneering class of sliding mode controllers is constructed in accordance with the characteristics of the designed sliding mode surfaces and the sigmoid function. These controllers can intelligently adjust the convergence rate of the system according to practical requirements, thereby addressing the limitation of fixed convergence rate in existing results. Moreover, the proposed controllers can effectively suppress jitter and analyze the effects of different sigmoid functions on jitter suppression. Sufficient conditions are derived to ensure that the states of the NISSs reach the designed surfaces in finite time and to achieve variable convergence rate stability. The excellent performance of the proposed theoretical strategy in achieving adjustable rate convergence of the system is demonstrated through a simulation of the ball-beam system.

This article studies the adaptive fuzzy asynchronous (AFA) stabilization of discrete networked hidden stochastic semi-Markovian switching power systems under cyber attacks. Due to the complex network environment, cyber-attacks are taken into account, in which the fuzzy logic rule is adopted to describe the unknown deception attacks. Considering the mismatch mechanism between the controller and the system, an adaptive fuzzy controller runs asynchronously with the system, where the hidden semi-Markovian model is used to characterize the asynchronous mechanism. Based on the detected mode and the fuzzy logic rule, an AFA stabilizing controller is designed for the underlying system. Using the stochastic Lyapunov function related to the detected mode and system mode, sufficient criteria are given for the AFA controller design, ensuring that the underlying system is bounded stable in the mean square. Finally, the proposed scheme is verified by the simulated example.

This work studies the novel sliding mode control (SMC) of discrete nonlinear stochastic switching models under semi-Markovian parameter and incomplete semi-Markovian kernel (SMK). The characteristic of nonlinear system is described by an interval type-2 fuzzy (IT2F) model that can be recognized as a collection of several type-1 fuzzy models. The uncertainties in system parameters is efficiently captured using the lower and upper grades of membership. Based on the mode-dependent Lyapunov function and incomplete SMK, sufficient conditions are proposed to ensure the stability of sliding dynamics. Moreover, an IT2F SMC law based on learning strategy is developed such that the state signals are guided onto the predetermined sliding region and the mode switchings-induced chattering is effectively reduced. Finally, the IT2F SMC strategy is validated through the simulation of truck-trailer model.

This article investigates robust predictive control problem for unknown dynamical systems. Since the dynamics unavailability restricts feasibility of model-driven methods, learning robust predictive control (LRPC) framework is developed from the aspect of time consistency. Under feedback-like control causality, the robust predictive control is then reconstructed as spatial-temporal games, and we guarantee stability through time-consistent Nash equilibrium. For gradation clarity, our framework is specified as four-follow contents. First, multistep feedback-like control causality is drawn from time series analysis, and Takens' theorem provides theoretical support from steady-state property. Second, control problem is reconstructed as games, while performance and robustness partition the game into temporal nonzero-sum subgames and spatial zero-sum ones, respectively. Next, multistep reinforcement learning (RL) is designed to solve robust predictive control without system model. Convergence is proven through bounds analysis of oscillatory value functions, and properties of receding horizon are derived from time consistency. Finally, data-driven implementation is given with function approximation, and neural networks are chosen to approximate value functions and feedback-like causality. Weights are estimated with least squares errors. Numerical results verify the effectiveness.

This article discusses the decentralized tracking control problem of large-scale nonlinear systems characterized by unknown disturbances, time-varying delays, and polynomial growth conditions. To tackle the problem, a novel robust decentralized tracking method is developed. First, scaling gain transformations are introduced to obtain a transformed system. Subsequently, some stable decentralized controllers are skillfully constructed for an auxiliary system utilizing a recursive control design approach. By presenting a new domination control method together with the utilization of Lyapunov–Krasovskii (L–K) functional, the decentralized tracking controllers are designed and the system stability is guaranteed. Finally, the chemical reactor system model is employed to validate the feasibility of the strategy.

This article proposes a prescribed-time fuzzy optimal control approach for flexible-joint (FJ) robot systems utilizing the reinforcement learning (RL) strategy. The uniqueness of this method lies in its ability to ensure optimal tracking performance for n-link flexible joint robots within the prescribed-time frame, while the actor and critic fuzzy logic system effectively approximate the optimal cost and evaluates system performance. First, the optimal controllers with the auxiliary compensation term are constructed by utilizing the online approximation of the modified performance index function and RL actor-critic structure. The designed controller can deal with unknown structure impacts and avoid model identification. Besides, in designing the prescribed-time scale function, the introduced constant term not only prevents singularity but also allows flexible setting of constraint regions. The proposed scheme is theoretically verified to satisfy the Bellman optimality principle and ensure the tracking error converges to the desired zone within the prescribed time. Finally, the practicability of the designed control scheme is further demonstrated by the 2-link FJ robot simulation example.

For the existing tracking control schemes of flexible-joint robots, precise sensor measurement is an implicit premise. However, idealized sensors are difficult to achieve due to manufacturing technology or other external factors. To this end, this paper further investigates the tracking control problem for flexible-joint robots with unknown measurement sensitivity. Specifically, for such multi-input multi-output Euler-Lagrange systems with completely unknown system dynamics, a novel measurement values-based adaptive control method is proposed by fusing sensitivity information and system variables into Lyapunov function candidates, where the restriction on system states in other the approximation lemma-based results is removed, since unknown nonlinearities are scaled by the structural characteristics of system variables. Above all, even if there are measurement errors, satisfactory tracking performance can be obtained by adjusting the design parameters, which is proved by rigorous theoretical analysis. Finally, hardware experiments further verify the effectiveness of the proposed method.Note to Practitioners—This work is motivated by the trajectory tracking control problem for flexible-joint robots under imprecise sensor measurements. Due to manufacturing technology limitations and component aging, there is inevitably a deviation between the measured values of sensors and real values, and this problem may become more prominent as the working environment of flexible-joint robots tends to become more complex. To our knowledge, most of the existing solutions for flexible-joint robots are developed based on precise sensor measurements, and they may fail to achieve satisfactory performance when real state information is not available. Moreover, the prior knowledge about model parameters and measurement sensitivity is difficult or impossible to exactly obtain in practice, which seriously hinders the further application of control methods that are dependent on system dynamics. To this end, this paper proposes a novel tracking control scheme based on measurement information for flexible-joint robots with unknown measurement sensitivity, where the dependence on model information is eliminated with the elaborately constructed Lyapunov function candidates, and the real tracking error is still adjusted to an acceptable range even if there are measurement errors. Preliminary experiments on a flexible-joint robot developed by Quanser company demonstrate the feasibility and effectiveness of the proposed method. In future studies, designing an effective scheme to achieve direct preset tracking control is the focus of the work.

This paper investigates the prescribed-time control for nonlinear systems with sensor faults and unknown control directions. Firstly, the prescribed-time control is realized based on scale function and descending power coordinate transformation. Then, effective nonlinear terms are designed in the controllers to compensate for state pollution caused by sensor faults. Furthermore, the Nussbaum gain function is introduced and it can be proven that the derivative of its variable is bounded, which is of significance to analyze the invariant set. The proposed control scheme guarantees that system states converge to zero within the specified time in advance and all signals of the closed-loop system are bounded. Finally, a numerical simulation and a practical simulation based on the Nomoto ship model intuitively show the feasibility of the scheme.Note to Practitioners—This study is motivated by achieving the prescribed-time control for nonlinear systems with sensor faults and unknown control directions. With the improvement of automation, the prescribed-time control that the convergence time can be arbitrarily specified in advance has been applied to many time-critical fields, such as missile guidance, emergency braking, and so on. It is noted that hypersonic weapons, uncalibrated visual servo control, and other practical models have the characteristic of unknown control direction, and most of the existing prescribed-time control schemes are not suitable for these practical systems. In addition, since the hovercraft power systems and aircraft systems are in a harsh environment for a long time, it can easily lead to sensor faults resulting in major accidents. In this study, based on the descending power coordinate transformation, the prescribed-time controllers with unique nonlinear terms are designed to deal with the above challenges.

This article investigates the extended dissipative finite-time boundedness (ED-FTB) problem for fuzzy switched systems under deception attacks. To improve the network resource efficiency, a multidomain probabilistic event-triggered mechanism (MDPETM) is proposed. The mode mismatched phenomenon is modeled based on the switching delay information between the controller mode and the system mode. To extract the true signal generated by the MDPETM, a virtual delay concept is developed. The constraint that the controller and the system must have the same premise variables is removed. Based on the MDPETM, mismatched fuzzy state feedback controllers are first devised which may not share the same modes with the system. Then, by establishing fuzzy basis and controller mode-dependent Lyapunov functionals, sufficient criteria free of nonlinear terms existing in the literature are derived, which ensure the ED-FTB of the closed-loop system under admissible delays and deception attacks. Finally, an application-oriented one-link robotic arm system is utilized to validate the theoretical results.

A slow time-delay assumption restricts the application of control approaches for numerous systems which are constantly affected by multiple uncertainties, including parameters, control coefficients, and the asymmetric dead-zone input. This work presents a new adaptive method for a class of high-order nonlinear delayed systems by removing the so-called slow time-delay assumption and multiple uncertainties. Remarkably, with a novel Lyapunov-Razumikhin (L-R) function and a direct fuzzy adaptive regulation scheme, a memoryless adaptive feedback controller is skillfully constructed to guarantee that the output tracks the given reference signal while keeping the boundedness of all closed-system signals. Finally, the presented scheme is applied to control a single-link robot system.

In this study, asynchronous sliding-mode control (SMC) for discrete-time networked hidden stochastic jump systems subjected to the semi-Markov kernel (SMK) and cyber attacks is investigated. Considering the statistical characteristic of the SMK, which is challenging to acquire in engineering, this study recognizes the SMK to be incomplete. Due to the mode mismatch between the original system and the control law in the operating process, a hidden semi-Markov model is proposed to describe the considered asynchronous situation. The main aim of this study is to construct an asynchronous SMC mechanism based on an incomplete SMK framework under the condition of random denial-of-service attacks so that the resulting closed-loop system can realize the mean-square stability. By virtue of the upper bound of the sojourn time in each mode, innovative techniques are developed for mean-square stability analysis under an incomplete SMK. Furthermore, an asynchronous SMC scheme is designed to achieve the reachability of the quasi-sliding mode. Finally, the effectiveness is verified using an electronic throttle model.

This article explores the$H_{\infty }$control problem based on convergence rate constraints for switched nonlinear systems under two types of asynchronous switching. First, this study investigates the variable convergence rate control issue for switched nonlinear systems. Combining the generalized pole placement idea and the Takagi–Sugeno fuzzy technique, a novel$H_{\infty }$control criterion is proposed, specifically focusing on convergence rate constraints. In addition, employing the persistent dwell-time switching to model two asynchronous scenarios in switched systems: time-delayed switching and mismatched switching, where the maximum asynchronous delay is permitted to exceed the subsystems dwell time. According to this new criterion, a novel$H_{\infty }$fuzzy controller is designed for switched nonlinear systems with asynchronous characteristics. It cannot only guarantee the asymptotic stability of the target closed-loop system but also precisely adjust the convergence rate of the system states, while also having certain anti-interference ability. Finally, numerical simulation and the tunnel diode circuit system control example prove the effectiveness of the method provided in this article.

Accurate prediction of solar irradiance is crucial for the effective utilization of solar energy. However, in real-world scenarios, complex irradiance patterns and prevalent incomplete data pose challenges to precise forecasting, resulting in additional uncertainties and instability. To address these issues, this study proposes a novel irradiance forecasting model that integrates a Mask-Transformer data imputation module and a prediction module centered around the typical patterns representation mechanism. The Mask-Transformer leverages a mask modeling mechanism to model the context of missing data, facilitating accurate estimation of missing values and reducing noise and uncertainty in the input data. The typical patterns representation mechanism comprises a series decomposition module and a feature fusion module, providing the module with the capability to mitigate nonlinearity and nonstationarity in solar irradiance data. This enhancement leads to improved short-term forecasting performance while maintaining long-term forecasting capabilities. Experimental results on two datasets demonstrate that the proposed model exhibits sufficient robustness and accuracy, making it effective in scenarios with incomplete data.

This study reports a fixed-time tracking control problem for strict-feedback nonlinear systems with quantized inputs and actuator faults where the total number of faults is allowed to be infinite. By taking advantage of radial basis function neural networks (RBFNNs), unknown nonlinear function terms in the system dynamic model can be effectively approached. In addition, based on the sector property of quantization nonlinearities and the structure of the actuator fault model, novel adaptive estimations and innovative auxiliary design signals are constructed to compensate for the influence caused by actuator faults and quantized inputs properly in the fixed-time convergence settings. Then, rigorous theoretical analysis manifests that the proposed control scheme can make the output tracking error converge to a small neighborhood of the origin within a fixed time, and the upper bound of the setting time not only does not depend on initial states of the system but also can be preassigned by selecting parameters appropriately. Meanwhile, all the signals in the closed-loop system remain bounded. Finally, a numerical example and a practical example of a single-link manipulator are presented to demonstrate the effectiveness of the proposed control algorithm.

In the realm of modern transportation, automated electric vehicles (AEVs) assume a seminal role in realizing the vision of intelligent and electrified mobility. The advancement of AEVs hinges on the utilization of smart Internet of Things (IoT) devices as indispensable components to propel their evolution. These devices not only amplify the operational capabilities of AEVs but also underpin their security in the face of escalating cyber threats. In this regard, this study proposes a novel IoT architectural paradigm, encompassing integration of model predictive control (MPC) and deep neural network (DNN) frameworks. The proposed architecture aims to enhance AEV performance, empowering them to counteract the disruptive impact of erroneous data intrusions that result from cyber breaches. To ensure the timely identification of potential threats without compromising privacy considerations, this study augments the framework to encompass trajectory prediction. This extension is achieved through dynamic programming (DP) to craft effective control strategies governing AEV motions, conjoined with DNNs adept in discerning deviations from projected behavioral norms within AEVs’ control signals. This cohesive symbiosis propels the expeditious detection of anomalies indicative of potential security breaches. As data privacy remains a paramount consideration, this work employs Homomorphic Encryption, enabling anomaly score computation on encrypted data, thereby upholding privacy standards. Various test scenarios are conducted to emphasize the effectiveness of the proposed IoT architecture with MPC, DP, and DNN to improve the performance of the AEV. The results attest that the proposed approach can tackle cyberattacks and data loss effectively which enhances the production process and decision making.

This article presents an asymptotic tracking controller for high-order uncertain nonlinear systems. Compared to the existing results, some restrictive assumptions are relaxed and the requirement of the variables being bounded is removed. At the same time, a prescribed time tracking control strategy is constructed to make that the system has better-transient performance during operation. Specifically, a global prescribed time function is proposed and an unconstrained variable is constructed to replace the constrained tracking error. Under this framework, an appealing feature of the designed controller is that the tracking error can converge to a specified range within a prescribed time and eventually converge to zero asymptotically starting from any initial conditions, where the prescribed time and accuracy can be directly given. Finally, the simulation results validate the effectiveness of the control strategy.

This article focuses on the adaptive stabilization control of high-order strict-feedback systems (SFSs) with unknown control directions. It addresses the challenge of dealing with unknown control directions in the fully actuated theory. To overcome this challenge, the Nussbaum gain technique and tuning functions directly based on the high-order fully actuated system (FAS) method are proposed to deal with the unknown control directions and system uncertainties, while avoiding the phenomenon of overparameterization. Additionally, a relative threshold event-triggered strategy is utilized to effectively avoid continuous controller updates, which helps conserve network resources used for signal transmission. The proposed controller guarantees the convergence of all system state variables to zero and ensures that all signals within the closed-loop system remain bounded. To validate the effectiveness of this approach, a numerical simulation and a practical example using the Nomoto ship model are conducted. Overall, this study contributes to the FAS field by providing a solution for adaptive stabilization control in high-order SFSs with unknown control directions.

This article focuses on the mean-field linear-quadratic Pareto (MF-LQP) optimal strategy design for stochastic systems in infinite horizon, which is with the$H_{\infty }$constraint when the system is disturbed by external interferences. The stochastic bounded real lemma (SBRL) with any initial state in infinite horizon is first investigated based on the stabilizing solution of the generalized algebraic Riccati equation (GARE). Then, by discussing the convexity of the cost functional, the stochastic indefinite MF-LQP control problem is defined and solved based on the MF-LQ theory and Pareto theory. When the worst case disturbance is considered in the collaborative multiplayer system, we show that the Pareto optimal strategy design with$H_{\infty }$constraint [or robust Pareto optimal strategy, (RPOS)] can be given via solving two coupled GAREs. When the worst case disturbance and the Pareto efficient strategy work, all Pareto solutions are obtained by a generalized Lyapunov equation. Finally, a practical example shows that the obtained results are effective.

The finite-time event-triggered stabilization is studied for a class of discrete-time nonlinear Markov jump singularly perturbed models with partially unknown transition probabilities (TPs). T-S fuzzy strategy is adopted to characterize the related nonlinear Markov jump singularly perturbed models. The control objective is to make sure that the system states remain within a bounded domain during a fixed-time interval. First, a mode-dependent event-triggered scheme is constructed to reduce the communication burden and save the network bandwidth. On that basis, by using a new Lyapunov function, a developed finite-time stability criterion is derived for the corresponding system to avoid an ill-conditioned issue due to a small singular perturbation parameter. Moreover, the mode-dependent fuzzy controller gain and the event-triggered parameter are co-designed under the framework of partially unknown TPs. Finally, the feasibility of the main results is provided to verify the finite-time event-triggered control strategy.

The event-triggered sliding-mode control (SMC) for discrete-time networked Markov jumping systems (MJSs) with channel fading is investigated by means of a genetic algorithm. In order to reduce resource consumption in the transmission process, an event-triggered protocol is adopted for networked MJSs. A key feature is that the signal transmission is inevitably affected by fading phenomenon due to delay, random noise, and amplitude attenuation in a networked environment. With the aid of a common sliding surface, an event-triggered SMC law is designed by adjusting the system network mode. Under the framework of stochastic Lyapunov stability, sufficient conditions are constructed to ensure the mean-square stability of the closed-loop networked MJSs, and the sliding region is reached around the specified sliding surface. Moreover, based on the iteration optimizing accessibility of objective function, an effective SMC approach under genetic algorithm is proposed to minimize the convergence region around the sliding surface. Finally, the effectiveness of the proposed method is proved by the F-404 aircraft model.

This study mainly concentrates on adaptive asymptotic tracking control for input-quantized strict-feedback nonlinear systems subjected to multiple unknown control directions. Novel improved lemmas, which relax the conditions for handling unknown control coefficients in the existing theoretical results, are certificated that can be applied to resolve the tracking problem for nonlinear systems under input quantification and unknown control directions simultaneously. Furthermore, by incorporating positive integral time-varying functions and the disintegration of the hysteresis quantizer into the controller design, the asymptotic tracking control is successfully achieved. Moreover, all signals in the closed-loop system are guaranteed to be bounded. Ultimately, a comparing numerical simulation and a practical simulation of a Nomoto ship model are presented to validate the feasibility of the proposed control algorithm.

In this study, a graph regularized algorithm for early expression detection (EED), called GraphEED, is proposed. EED is aimed at detecting the specified expression in the early stage of a video. Existing EED detectors fail to explicitly exploit the local geometrical structure of the data distribution, which may affect the prediction performance significantly. According to manifold learning, the data in real-world applications are likely to reside on a low-dimensional submanifold embedded in the high-dimensional ambient space. The proposed graph Laplacian consists of two parts: 1) a k -nearest neighbor graph is first constructed to encode the geometrical information under the manifold assumption and 2) the entire expressions are regarded as the must-link constraints since they all contain the complete duration information and it is shown that this can also be formulated as a graph regularization. GraphEED is to have a detection function representing these graph structures. Even with the inclusion of the graph Laplacian, the proposed GraphEED has the same computational complexity as that of the max-margin EED, which is a well-known learning-based EED, but the detection performance has been largely improved. To further make the model appropriate in large-scale applications, with the technique of online learning, the proposed GraphEED is extended to the so-called online GraphEED (OGraphEED). In OGraphEED, the buffering technique is employed to make the optimization practical by reducing the computation and storage cost. Extensive experiments on three video-based datasets have demonstrated the superiority of the proposed methods in terms of both effectiveness and efficiency.

This article addresses the practically predefined-time adaptive fuzzy tracking control problem of strict-feedback nonlinear stochastic systems, where the system under consideration includes stochastic disturbances and uncertain parameters. First, in this study, practically predefined-time stochastic stabilization (PPSS) in the p th moment sense is introduced, and a Lyapunov-type criterion for PPSS is proposed to assure the stabilization of the system considered. With these ideas, based on the backstepping design method, a semiglobally practically predefined-time adaptive fuzzy tracking control algorithm is proposed with a fuzzy system used to approximate the unknown part of the system. Moreover, the settling time of the system response can be arbitrarily adjusted in a mean-value sense, and such freedom can be used to improve the stochastic finite-/fixed-time control results. Finally, a practical example and a numerical example of a comparison are provided to validate the effectiveness of the proposed control strategy.

Nonsmooth nonlinear systems can model many practical processes with discontinuous property and are difficult to be stabilized by classical control methods like smooth nonlinear systems. This article considers the output-feedback adaptive neural network (NN) control problem for nonsmooth nonlinear systems with input deadzone and saturation. First, the nonsmooth input deadzone and saturation is converted to a smooth function of affine form with bounded estimation error by means of the mean-value theorem. Second, with the help of approximation theorem and Filippov's differential inclusion theory, the given nonsmooth system is converted to an equivalent smooth system model. Then, by introducing a proper logarithmic barrier Lyapunov function (BLF), an output-feedback adaptive NN strategy is set up by constructing an appropriate observer and adopting the adaptive backstepping technique. A new stability criterion is established to guarantee that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB). Finally, comparative simulations through Chua's oscillator are offered to verify the effectiveness of the proposed control algorithm.

The fuzzy observer-based output feedback control of continuous-time nonlinear two-dimensional (2-D) systems is studied by Takagi–Sugeno (T–S) fuzzy models in this work. The plant information of the 2-D systems evolves along two independent directions dynamically. The nonlinear 2-D systems are firstly expressed by T–S fuzzy models with parameter uncertainties. By a Lyapunov method together with some convexification techniques, two methods are developed for fuzzy observer-based output feedback controller synthesis of the underlying fuzzy 2-D systems, and novel output feedback controller synthesis results are proposed within a convex optimization setup. Simulation studies are provided to illustrate the validity of the proposed methods.

This article investigates the real-time localization problem of dynamic multiagent systems with repetitive operation characteristics under directed graph. A distributed localization estimation algorithm based on iterative learning is proposed. The barycentric coordinates calculated based on the relative distance are used to estimate the real coordinates of the agent. Different from the traditional estimation methods along the time axis, the proposed method utilizes the information of iteration axis simultaneously. In this method, the current estimation coordinates are updated by using the estimation coordinates of the same sampling time in previous iteration, the estimation accuracy is improved, and the velocity constraint is removed. Additionally, the real-time localization problem of dynamic multiagent systems under arbitrary deployment is concerned. An improved distributed localization estimation algorithm with signed coefficients based on iterative learning is proposed. Meanwhile, the results are also extended to the localization estimation of multiagent systems with arbitrary deployment in 3-D space. By introducing Richardson iteration and infinite norm, the global asymptotic convergence of the proposed methods is guaranteed. Finally, numerical simulations and the Qbot-2e robot experiment are provided to show the effectiveness and validity of the obtained results.

This study concentrates on the tracking control problem for nonlinear systems subject to actuator saturation. To improve the performance of the controller, we propose a fixed-time tracking control scheme, in which the upper bound of the convergence time is independent of the initial conditions. In the control scheme, first, a smooth nonlinear function is employed to approximate the saturation function so that the controller can be designed under the framework of backstepping. Then, the effect of input saturation is compensated by introducing an auxiliary system. Furthermore, a fixed-time adaptive neural network control method is given with the help of fixed-time control theory, in which the dynamic order of controllers is reduced to a certain extent since there is only one updating law in the entire control design. Through rigorous theoretical analysis, it is concluded that the proposed control scheme can guarantee that: 1) the output tracking error can converge to a small neighborhood near the origin in a fixed time and 2) all signals in the closed-loop system are bounded. Finally, a numerical example and a practical example based on the single-link manipulator are provided to verify the effectiveness of the proposed method.

The problem of finite-time consensus tracking is investigated for multiagent systems with unknown control gain functions and hysteresis quantized inputs. Existing control methods utilizing command filtered technique have certain limitations for unknown control gains. Motived by this, the study is concerned with establishing a novel control strategy based on a modified command filtered technique with estimation-like terms and key compensation terms. Furthermore, fuzzy logic systems participate in command filter design while processing unknown items. It is theoretically testified that the proposed scheme not only guarantees the finite-time consensus tracking performance but also remains that all the resulting closed-loop signals are bounded. Finally, the simulations on numerical and practical examples prove the validity of the control scheme.

This study reports the adaptive asymptotic tracking control problem for flexible-joint (FJ) robot systems, the output tracking error can be kept within the prescribed range in the initial stage of system operation, as time approaches infinity, the asymptotic tracking result can be obtained. The prescribed performance function and the positive integrable time-varying function are introduced simultaneously in the control design of FJ robot systems for the first time. The control scheme is designed under the frame of the adaptive backstepping method and command filtered technique, which successfully avoids the problem of complexity explosion. The radial basis function neural networks are used to deal with unknown uncertainties and the adaptive laws are designed to approximate the norms of weight vectors and approximation errors. Finally, the feasibility of the proposed scheme is proved by the simulation and the experiment of the 2-link FJ robot on the Quanser platform.

In this article, the problem of reachable set estimation (RSE) and the design of aperiodic sampled-data controller for Takagi–Sugeno fuzzy Markovian jump systems (T–S FMJSs) with unit-energy bounded disturbance (UEBD) and unit-peak bounded disturbance (UPBD) inputs are taken into consideration. First, sufficient conditions that all states of the T–S FMJSs are encompassed by ellipsoids under zero initial conditions are acquired via constructing a mode-dependent two-sided loop-based Lyapunov function and applying a linear matrix inequality approach. Second, the RSE is taken into account in the design of the state feedback aperiodic sampled-data controller with the aim that the resulting ellipsoid encompasses the reachable set of the closed-loop system. Finally, a nonlinear mass-spring model and a tunnel diode circuit model demonstrate the efficiency of the presented approach. In addition, this method is able to obtain a larger sampled-data period than other literature, thus saving bandwidth and reducing communication resources.

This article studies the finite-time tracking control problem for the single-link flexible-joint robot system with actuator failures and proposes an adaptive fuzzy fault-tolerant control strategy. More precisely, the issue of "explosion of complexity" is successfully solved by incorporating the command filtering technology and the backstepping method. The unknown nonlinearities are identified with the help of the fuzzy logic system. An event-triggered mechanism with the relative threshold strategy is exploited to save communication resources. Furthermore, the proposed control design can guarantee that the tracking error converges to a small neighborhood of origin within a finite time by taking full advantage of the finite-time stability theory. Finally, the simulation example is presented to further verify the validity of the proposed control method.

The fault detection issue is investigated for complex stochastic delayed systems in the presence of positivity constraints and semi-Markov switching parameters. By choosing a mode-dependent fault detection filter (FDF) as a residual generator, the corresponding fault detection is formulated as a positive [Formula: see text] filter problem. Attention is focused on the design of a mode-dependent FDF to minimize the error between the residual signal and the fault signal. The designed FDF features good sensitivity of the faults and robustness against the external disturbances. Subsequently, by means of the linear copositive Lyapunov functional (LCLF), stochastic stability is proposed to satisfy an expected [Formula: see text]-gain performance. Some solvability conditions for the desired mode-dependent FDF are established with the help of a linear programming approach. Finally, an application example of a data communication network model is provided to demonstrate the effectiveness of the theoretical findings.

This article focuses on global finite-time output feedback stabilization for uncertain nonlinear systems with unknown measurement sensitivity. The existence of the continuous measurement error resulting from limited accuracy of sensors invalidates the existing design strategies depending on the use of the precise output in the construction of an observer, which highlights the contribution of this article. Essentially, different from related works, we propose a new finite-time convergent observer by avoiding the use of the information on nonlinearities. By combining the homogeneous domination with the addition of a power integrator method, an output feedback controller composed of multiple nested sign functions is successfully developed. Finally, the effectiveness of the presented scheme is exhibited by a numerical example.

This article focuses on the design of a novel adaptive fuzzy event-triggered tracking control approach for a category of high-order uncertain nonlinear systems with prescribed performance requirements, in which a high-order tan-type barrier Lyapunov function (BLF) is employed to handle and analyze the output tracking error, fuzzy systems are adopted to identify the totally unknown nonlinear functions, and only one gain function rather than parameter estimation functions is designed to cancel out all unknowns appearing in fuzzy systems. As a result, complicated calculations are avoided and a structured simple control is achieved. The proposed controller not only ensures that the tracking error is always within a predefined region but also reduces the communication burden from the controller to the actuator. Finally, comparison simulations are presented to verify the effectiveness of the proposed control schemes.

The issue of adaptive output-feedback stabilization is investigated for a category of stochastic nonstrict-feedback nonlinear systems subject to unmeasured state and unknown control directions. By combining the event-triggered mechanism and backstepping technology, an adaptive fuzzy output-feedback controller is devised. In order to make the controller design feasible, a linear state transformation is introduced into the initial system. At the same time, the Nussbaum function technology is used to overcome the difficulties caused by unknown control directions, and the state observer solves the problem of the unmeasured state. Based on the fuzzy-logic system and its structural characteristics, the issue of unknown nonlinear function with nonstrict-feedback structure in the system is tackled. The designed controller could not only guarantee all signals of closed-loop systems are bounded in probability but also save communication resources effectively. Finally, numerical simulation and ship dynamics example are given to confirm the effectiveness of the proposed method.

This article investigates the$H_{\infty }$stochastic tracking control problem for uncertain fuzzy Markovian hybrid switching systems by using a fuzzy switching dynamic adaptive control approach. The long and the short is to construct multiple piecewise stochastic Lyapunov functions which provide an effective tool for designing hybrid switching law and fuzzy switching dynamic adaptive law. A hybrid switching law, including both stochastic switching and deterministic switching, is designed to represent more general switching scenarios, which can improve the$H_{\infty }$adaptive tracking performance through offering a running time before stochastic switching for the adaptive control strategy to work well. A fuzzy switching dynamic adaptive control technique is developed such that all signals of the tracking error equation are bounded, and the system state trajectory tracks the reference model state trajectory under a disturbance attenuation level as closely as possible. Finally, an application study verifies the effectiveness of the acquired methods.

This article focuses on the$H_{\infty }$adaptive tracking problem of uncertain switched systems. A key point of the study is to set up a multiple piecewise Lyapunov function framework which provides an effective tool for designing an adaptive switching controller consisting of a state-feedback and time-driven switching signal and a time-driven adaptive law. The proposed switching signal guarantees the solvability of the$H_{\infty }$adaptive tracking problem for uncertain switched systems. Significantly, it provides plenty of adjusting time for the adaptive tracking control strategy to damp the transient caused by switching and avoids frequent switching. A novel time-driven adaptive switching controller is established such that the tracking error asymptotically converges to zero and all the signals in the error dynamic system are bounded under an achieved disturbance attenuation level. The solvability criterion ensuring an$H_{\infty }$adaptive tracking performance is established for the uncertain switched systems, where the solvability of the$H_{\infty }$adaptive tracking problem for individual subsystems is not required. Finally, the proposed method is applied to the electro-hydraulic unit.

This article investigates the stochastic stability analysis and stabilization problems for discrete-time Takagi–Sugeno fuzzy semi-Markov jump systems with upper-bounded sojourn time. The fuzzy rules can be different for different system modes. Consequently, the membership functions for fuzzy rules are dependent on the system modes. Allowing for the fact that semi-Markov kernel (SMK) are difficult to fully obtain in practice, the elements in the SMK of the underlying systems are deemed to be partly known, which is more general than both semi-Markov jump systems with completely available SMK and Markov jump systems with unknown transition probabilities. Afterward, the stability and stabilization conditions are established by part of the known SMK information and then by all the known SMK information. In the end, the validity and the superiority of our proposed theoretical results are exemplified via a single-link robot arm and a truck-trailer model.

This article addresses an event-triggered adaptive fuzzy tracking control problem for nonlinear systems with unknown control directions. To achieve the practical tracking, fuzzy logic systems are employed to approximate unknown nonlinear function. Nussbaum functions are adopted to address the problem of the unknown control directions. The newly designed controller not only guarantees the tracking error that converges to an arbitrary small neighborhood near the zero but also reduces the communication burden from the controller to the actuator. Moreover, the feasibility of the proposed event-triggered mechanism is verified by excluding Zeno behavior. Finally, the simulation result proves the effectiveness of the designed control scheme.

This research aims to propose an innovative smart system we developed for early failure detection of Automatic Tool Change (ATC) systems. Input data is the system's tool magazine door open/close signals. Then, 41 indicators from 26 machines are obtained from statistics-based feature extraction methods. Under the guidance of predefined risk levels, nine high-ranking top level indicators are selected using correlation and regression analysis. In addition, some lightweight supervised learning algorithms are used to build and train the model to solve the classification problem of the system states, such as Normal, Caution, and Danger. The experimental results confirm that the high-ranking indicators can achieve the most prominent and stable performance under a series of tests. Under 10-fold cross-validation, the average accuracy is 89.43 %, which is 19~38 % higher than those of other feature groups. Among them, the Naive Bayes algorithm obtains the best accuracy of 94.2 %. This proves that the proposed smart system can effectively grasp the health status of the ATC systems.

This article reports our study on a reduced adaptive fuzzy decoupling control for our lower limb exoskeleton system which typically is a multi-input-multi-output (MIMO) uncertain nonlinear system. To show the applicability and generality of the proposed control methods, a more general MIMO uncertain nonlinear system model is considered. By decoupling control, the entire MIMO system is separated into several MISO subsystems. In our experiments, such a system may have problems (even unstable) if a traditional fuzzy approximator is used to estimate the complicated coupling terms. In this article, to overcome this problem, a reduced adaptive fuzzy system together with a compensation term is proposed. Compared to traditional approaches, the proposed fuzzy control approach can reduce possible chattering phenomena and achieve better control performance. By employing the proposed control scheme to an actual 2-DOF lower limb exoskeleton rehabilitation robot system, it can be seen from the experimental results that, as expected, it has good performance to track the model trajectory of a human walking gait. Therefore, it can be concluded that the developed approach is effective for the control of a lower limb exoskeleton system.

This article is to tackle the event-based state-feedback control problem for interval type-2 (IT2) fuzzy systems subject to the fading channel. For saving communication resources, a dynamic event-triggered (ET) mechanism is utilized to decide the data transmission from sensors to the controller. A time-varying random process is employed to characterize the fading phenomenon in the unpredictable communication network. By considering the effect of channel fading, a nonparallel distribution compensation (non-PDC) IT2 fuzzy controller is synthesized and its number of rules and membership functions (MFs) can be freely selected. As a consequence, the closed-loop fuzzy system possesses imperfectly matched MFs. By taking the global membership boundary information into stability analysis, the membership-function-dependent analysis method is employed to handle these imperfectly matched MFs and to obtain relaxed criteria. Besides, sufficient criteria are obtained so that the resulting closed-loop IT2 fuzzy system can achieve stochastic stability despite fading measurements. The effectiveness of the proposed method is illustrated by a mass-spring-damper system and a numerical example.

This article reports our study on asynchronous H∞filtering for fuzzy singular Markovian switching systems with retarded time-varying delays via the Takagi-Sugeno fuzzy control technique. The devised parallel distributed compensation fuzzy filter modes are described by a hidden Markovian model, which runs asynchronously with that of the original fuzzy singular Markovian switching delayed system. The fuzzy asynchronous filtering dealt with in this article contains synchronous and mode-independent filtering as special cases. Novel admissibility and filtering conditions are derived in terms of linear matrix inequalities so as to ensure the stochastic admissibility and the H∞performance level. Simulation examples including a singlelink robot arm are employed to demonstrate the correctness and effectiveness of the proposed fuzzy asynchronous filtering technique.

A novel adaptive fuzzy control scheme is designed in this article for a class of stochastic nonstrict feedback nonlinear systems with output constraint and unknown control coefficients. A reduced adaptive fuzzy system is proposed to approximate the unknown function which contains all state variables of the whole system and ensures that the backstepping design method works normally for nonstrict feedback nonlinear systems. With the use of this reduced adaptive fuzzy control method and a combination of Barrier Lyapunov Function control design and Nussbaum gain technique, a novel adaptive fuzzy controller is proposed to guarantee that the output tracking error always meets the given constraint requirement in a sense of probability and the resulting closed-loop states are bounded in probability. Finally, an example is presented to confirm the effectiveness of the designed method.

In this article, the issue of finite-time command filter-based adaptive fuzzy tracking control based on an event-triggered scheme for stochastic strict-feedback nonlinear systems is studied. By using a fuzzy logic system, finite-time command filter with compensation signals, an event-triggered adaptive controller is designed. The newly designed controller not only guarantees the property of finite-time convergence, but also reduces the communication burden from the controller to the actuator. Meanwhile, the problem of complexity explosion caused by the backstepping method is avoided by using command filter technology. The proposed controller can ensure that the output signal tracks the given reference signal under the bounded error. Finally, the simulation result proves the effectiveness of the proposed control method.

This article is concerned with the issue of asynchronous feedback control for fuzzy degenerate jump systems with mode-dependent time-varying delays via Takagi–Sugeno fuzzy control technique under observer-based event-driven characteristic. The improved observer and event trigger transmit not only state estimate signals but also the information of Markovian jump modes to the controller under the networked nonperiodic sampling scheme. Applying parallel distributed compensation technique, the asynchronous fuzzy feedback controller is devised, and the asynchronous fuzzy controller modes are depicted by a hidden Markovian model, where the modes of fuzzy controller run asynchronously with that of the original degenerate fuzzy jump systems. Exponentially decreasing function is employed to devise the triggering threshold, which can ensure not only the admissibility of the delayed fuzzy degenerate jump systems but also the effectiveness of event-driven strategy. Original stochastic admissibility and asynchronous feedback control conditions are characterized by linear matrix inequalities. A numerical example and a single-link robot arm model are applied to illustrate the correctness and validity of the proposed observer-based event-driven asynchronous fuzzy feedback control technique.

In this article, a global finite-time active disturbance rejection control (ADRC) scheme is proposed for tracking control of redundant parallel manipulators with unknown bounded uncertainties. This approach combines an ADRC and a global finite-time control for high accuracy trajectory tracking control. Based on the nonsingular fast terminal sliding mode control, the proposed approach can remove the condition in the original ADRC that the derivative of the uncertainties is required to be bounded. The extended state observer is employed to handle the real-time estimation of the total uncertainty. It can be found that the proposed scheme not only can converge fast to the semi-global finite-time stable equilibrium but also can have superior tracking control performance. In summary, compared to existing approaches, the proposed scheme can have several advantages, such as uncertainty rejection, easy implementation, robustness, chattering-free, high precision, and no need for prior knowledge of bounded uncertainties. The simulation results validate the effectiveness of the proposed method.

This article is concerned with the improvement of robust control methodology and its application in stabilizing a class of high-order nonlinear systems with multiple unknown time-varying sensitivities, and discusses how to use the proposed control strategy on humanoid robot manipulation. The novel design approach successfully breaks through the limitation of the neural network technique; that is, state variables must be located in some compact sets. The remarkable feature of the systems under investigation lies in the presence of measurement sensitivities and higher powers, which makes the nonlinear systems essentially different from the related works. By reductio and the introduction of a modified tuning function, an appropriate controller is constructed to render that all the state variables belong to a predetermined bounded set. Finally, an example is provided to illustrate the effectiveness of the proposed control strategy.

This paper focuses on the design of a reduced adaptive fuzzy tracking controller for a class of high-order stochastic nonstrict feedback nonlinear systems with full-state constraints. In the proposed approach, reduced fuzzy systems are used to approximate uncertain functions which involve all state variables and a high-order tan-type barrier Lyapunov function (BLF) is considered to deal with full-state constraints of the controlled system. With this BLF and a combination of the reduced fuzzy control and adding a power integrator, a novel control scheme is constructed to ensure that tracking error is within a very small range of the origin almost surely, meanwhile, the constraints on the system states are not breached almost surely during the operation. Two examples are proposed to show the effectiveness of the design scheme.

This article reports this article on the problem of adaptive fuzzy output tracking control for a category of high-order nonlinear systems with input saturation, output constraint, and serious uncertainties. A high-order barrier Lyapunov function and an auxiliary Hyperbolic Tangent function is employed to deal with output constraint and input saturation, respectively. By incorporating a backstepping design technique, adaptive fuzzy control, and adding a power integrator, a novel control scheme is designed to ensure that all states in the resulting closed-loop system are bounded and the tracking error converges to a bounded compact set. Moreover, two simulations are conducted to verify the effectiveness of the design method.

The issue of adaptive fuzzy prescribed performance tracking control is considered in this article for strict-feedback stochastic uncertain nonlinear systems. A novel adaptive tracking control design approach, in which the fuzzy systems are employed to approximate the totally unknown nonlinear terms, is proposed by incorporating the technology of prescribed performance control with the method of command filtered backstepping design. The proposed adaptive state feedback controller can ensure that the output tracking error converges to a predefined arbitrarily small residual set in probability and all the signals of the closed-loop system can be bounded in probability, meanwhile, the problem of “explosion of complexity” is solved. The effectiveness of the presented method is verified by two simulation examples.

This article proposes a novel approach to replace the fixed projection planes existed in the previous researches to reduce motion artifacts obtained from the human face by a normal webcam for monitoring heart rate in a real-time fashion. The novel projection plane is adaptively changed with the light intensity change to eliminate the color distortion induced by motion. In this article, the state-of-the-art semantic segmentation Deeplabv3+ is implemented to segment the skin pixels from the facial region that is detected by several trackers (Boosting, MIL, TLD, Median Flow, Mosse, and CSRT) to boost the computational time compared to the conventional face detection by Haar-Like features. Image and digital signal processing techniques are also applied to eliminate possible noise for obtaining a clean pulse signal. The proposed approach is compared with other existing approaches (Green, PCA, Chrom, and POS) in multiple challenges. From the experiments conducted, the Deeplabv3+ outperforms the conventional K-means for different kinds of skin segmentation. Moreover, the proposed approach is quite robust and stable in the stationary case (with the accuracy 96%), dim-lighting environment and the long-distance up to 4-m away without zooming in camera. Besides, multiple head-movement simulations and motions of fitness are conquered by the APP approach as shown in the experiments. Thus, it can be concluded that the proposed approach is applicable to surveillance or healthcare applications.

Verification of dispensed pharmaceutical packages is of paramount importance to prescription dispensing. Due to lack of identification peripherals like bar codes or RFID tags on blister packages, image-based solutions have been utilized. Earlier two-stage solutions require more resources for implementation and training, in addition to more computational time. In contrast, this paper presents an end-to-end trained solution, called Fast Rotated Occluded Rectangular (Fast ROR) pattern recognition architecture, composed of modules of: rotational rectangular detection, affine transformation, and image recognition. In particular, the features used to localize the package and the features used for identification are the same and extracted by a common feature extractor. As a result, the overall architecture is more compact with only one training set needed and more efficient computation time. Comparison experiments have been conducted on the proposed end-to-end FOR and on a representative two-stage HBIN [1] solution: Targeting a pool of 230 types of pharmaceutical packages, 30 paired front and back handheld images for each type were taken and randomly partitioned with 4:1 for training and testing, FOR (vs. HBIN) uses 41.79M (120.32M) network parameters, with a training time of 17 hours (119 hours), and a testing speed of 22.2 fps (10fps). The identification results in terms of F1-score by FOR (vs. HBIN) is 100% (98.67%) in familiar environment, whereas 94.30% (91.27%) when the identification is conducted in new environment.

This paper presents a new adaptive fuzzy control scheme for active suspension systems subject to control input time delay and unknown nonlinear dynamics. First, a predictor-based compensation scheme is constructed to address the effect of input delay in the closed-loop system. Then, a fuzzy logic system (FLS) is employed as the function approximator to address the unknown nonlinearities. Finally, to enhance the transient suspension response, a novel parameter estimation error-based finite-time (FT) adaptive algorithm is developed to online update the unknown FLS weights, which differs from traditional estimation methods, for example, gradient algorithm with e -modification or σ -modification. In this framework, both the suspension and estimation errors can achieve convergence in FT. A Lyapunov-Krasovskii functional is constructed to prove the closed-loop system stability. Comparative simulation results based on a dynamic simulator built in a professional vehicle simulation software, Carsim, are provided to demonstrate the validity of the proposed control approach, and show its effectiveness to operate active suspension systems safely and reliably in various road conditions.

This paper focuses on the practical output tracking control for a category of high-order uncertain nonlinear systems with full-state constraints. A high-order tan-type barrier Lyapunov function (BLF) is constructed to handle the full-state constraints of the control systems. By the BLF and combining a backstepping design technique, an adding a power integrator, and a fuzzy control, the proposed approach can control high-order uncertain nonlinear system with full-state constraints. A novel controller is designed to ensure that the tracking errors approach to an arbitrarily small neighborhood of zero, and the constraints on system states are not violated. The numerical example demonstrates effectiveness of the proposed control method.

This paper reports our study on adaptive tracking control for a mobile-wheeled inverted pendulum with periodic disturbances and parametric uncertainties. With an appropriate reduced dynamic model, incorporating repetitive learning strategies with dynamic decoupling and related adaptive control techniques, a novel controller is successfully constructed to ensure that the output tracking errors of the system will stay within a small neighborhood around zero and all of the other signals are semiglobal uniform bounded. Meanwhile, only one parameter estimation is used for adaptive controller design, which overcomes the problem of over-parametrization. Furthermore, a required condition of period identifier mechanisms is proposed. Finally, detailed simulation results are presented to demonstrate the effectiveness of the proposed control schemes.

The eighteen papers in this special section focus on emerging techniques and applications supported by fuzzy logic and artificial intelligence (AI). AI has become the focus of the day and attracted much attention from researchers, industries, and governments. This special issue serves as a forum to bring together all emerging techniques for fuzzy logic and fuzzy set-based AI and foster new advancements along this important direction. Actually, there have been a number of research pursuits that position themselves at the junction of AI and fuzzy logic. For example, natural language processing, viewed as the jewel in the crown of AI, has been one of the focal points in the domain of fuzzy logic and fuzzy sets. Fuzzy sets can offer an effective paradigm supporting accurate understanding of natural language and build efficient linkages to human intelligence through concepts and computing with membership functions, in particular type-2 fuzzy sets for explainable AI.

This paper aims to build a simple and low-cost system by using images to detect human breath in a real-time fashion to estimate the peak of the inspiratory phase of a breath so as to define a proper triggering timing for X-ray shooting. In fact, it is very difficult to detect very small breathing motion on images. In this paper, well-known techniques are employed to obtain useful features from the chest area for the Lucas-Kanade algorithm. Various levels of the Pyramidal Lucas-Kanade are then adapted to track possible small motions of those features. The proposed approach can successfully detect the inspiratory-expiratory motions and the peak time of inspiratory phase can be predicted within an acceptable interval of error time. From the experiments conducted, the breath motion can be successfully observed in two different environment situations (dim-lighting and lighting conditions). It can be found that the tracked features are quite robust and stable without losing the quantity over a long period of testing time. Thus, the proposed approach can effectively be used to define a proper triggering timing for X-ray shooting. Besides, in our experiments, even though the target is 6 m away, the breath detection is still successful. In other words, the proposed approach can also be used for surveillance or healthcare environments.

Heart rate estimation from fitness plays an important role in the evaluation of fitness exercises. Conventional approaches use the photoplethysmography (PPG) sensor to consider the change of light absorption on the wrist skin for heart rate estimation. However, users are required to buy smartwatches for using this function. Various approaches based on video analysis are recently implemented for surveillance purpose. However, it is unstable for motion scenario such as fitness exercises due to the color distortion induced by movement. POS and CHROM are introduced to address this issue. Since the fixed projection planes from POS and CHROM are given in several sources of light, it is not widely applied for surveillance applications. Therefore, a novel projection plane that is adaptively changed with the lighting environment is proposed to estimate the heart rate from fitness videos in ambient light. Moreover, image and digital signal processing techniques are also applied to extract the clean pulse signal from a novel projection plane. From the experiments conducted, the proposed approach outperformed the existing approaches to be the best model for heart rate estimation from fitness videos with the accuracy up to 91.08%.

This paper discusses the problem of adaptive tracking control for full-state constrained nonlinear system with unknown control directions. In view of Nussbaum technology and Barrier Lyapunov function design, an adaptive control scheme is designed to ensure that the tracking error asymptotically converges to zero and the states of the system are always within the given bounds. A numerical simulation example is given to confirm the effectiveness of the designed method.

Nursing shift handover involves conversations between nurses during duty handover. Handover contents have been recorded manually, and it is desirable to have an automatic speech recognition solution that transcribes audio speeches into texts. Even though there are excellent commercial Speech recognition solutions available, they are mainly designed for general contexts, which may not be most efficient for special contexts. This pilot study is to investigate speech recognition for the special contexts of nursing handover. To be more precise, we are to transcribe audios of 500 Chinese sentences conversed during nursing handover at the Taipei Hospital. A deep learning based network, Deep Speech 2 [1] working in conjunction with Beam Search Decoder with Language Model [2], is adopted for the intended automatic speech transcription. Special adaptation is considered for the audios is in Chinese: the recognition unit in DS2 is Chinese characters instead of letters as in English; the N-gram language model is also on Chinese characters rather than words as in English. Performance of the trained model has been conducted on two experiments: One is to transcribe audios made by people other than the people made the training audios. The other is to transcribe audios whose contents deviated from the recorded 500 sentences. For the former, the character error rate (CER) is 2.33%, as compared to that of 7.94% by Google Speech API [3]. For the latter, when the testing sentences are deviated from the training ones by 21% in word counts, the CER by the trained system is 13%. The results show promising foundation for more complicate sentences patterns.

In this paper, suffering from both complex uncertainties and underactuations, accurate trajectory tracking control problem of an asymmetric underactuated surface vehicle (AUSV) is first addressed by guiding yaw dynamics which are free of persistent excitation (PE). Using nested coordinate transformations, the AUSV is formulated in a cascade structure consisting of translation and rotation subsystems with complex uncertainties. Finite-time uncertainty observers (FUOs) are devised to exactly estimate transformed uncertainties, and enable separation principle in controller and observer syntheses. By virtue of creating yaw-guided dynamics, rotation tracking is shaped to stabilize yaw and sway tracking discrepancies, simultaneously, in collaboration with yaw controller. Nominal dynamics of translation tracking errors are globally asymptotically stabilized by surge-control synthesis using cascade analysis and Lyapunov approach, and thereby contributing to global asymptotic stability of the entire translation-rotation tracking system. Eventually, an FUO-based yaw-guided tracking control (FUO-YTC) scheme of an AUSV with complex uncertainties is established. Simulation studies demonstrate remarkable performance.

This paper reports our study on adaptive fuzzy tracking control for flexible-joint robots with full state constraints. In the control design, fuzzy systems are adopted to identify the totally unknown nonlinear functions and can properly avoid burdensome computations. The tan-type barrier Lyapunov functions are used to deal with state constraints so that even without state constraints, the controller is still valid. By combining the method of backstepping design with adaptive fuzzy control approaches, a novel simpler controller is successfully constructed to ensure that the output tracking errors converge to a sufficiently small neighborhood of the origin, while the constraints on the system states will not be violated during operation. Finally, comparison simulations are presented to demonstrate the effectiveness of the proposed control schemes.

In this paper, a backpropagating constraints-based trajectory tracking control (BCTTC) scheme is addressed for trajectory tracking of a quadrotor with complex unknowns and cascade constraints arising from constrained actuator dynamics, including saturations and dead zones. The entire quadrotor system including actuator dynamics is decomposed into five cascade subsystems connected by intermediate saturated nonlinearities. By virtue of the cascade structure, backpropagating constraints (BCs) on intermediate signals are derived from constrained actuator dynamics suffering from nonreversible rotations and nonnegative squares of rotors, and decouple subsystems with saturated connections. Combining with sliding-mode errors, BC-based virtual controls are individually designed by addressing underactuation and cascade constraints. In order to remove smoothness requirements on intermediate controls, first-order filters are employed, and thereby contributing to backsteppinglike subcontrollers synthesizing in a recursive manner. Moreover, universal adaptive compensators are exclusively devised to dominate intermediate tracking residuals and complex unknowns. Eventually, the closed-loop BCTTC system stability can be ensured by the Lyapunov synthesis, and trajectory tracking errors can be made arbitrarily small. Simulation studies demonstrate the effectiveness and superiority of the proposed BCTTC scheme for a quadrotor with complex constrains and unknowns.

Human action recognition from video sequences is one of the most challenging computer vision applications, primarily owing to intrinsic variations in lighting, pose, occlusions, and other factors. The human skeleton joints extracted by the depth camera Kinect have the advantages of simplified structures and rich contents, and are therefore widely used for capturing human actions. However, at present, most of the skeletal joint and Deep learning based action recognition methods treat all skeletal joints equally in both spatial and temporal dimensions. Logically, this is not in accordance with the fact that for different human actions the contributions from skeletal joints could significantly vary spatially and temporally. Incorporating information pertaining to such natural variations will certainly aid in designing a robust human action recognitions system. Hence, in this work, we endeavor to propose a global spatial attention (GSA) model to suitably express the different skeletal joints with different weights so as to provide precise spatial information for human action recognition. Further, we will introduce the notion of accumulative learning curve (ALC) model that can highlight which frames contribute most to the final decision by giving varying temporal weights to each intermediate accumulated learning results provided by an LSTM upon input frames. The proposed GSA (for spatial information) and ALC (for temporal processing) models are integrated into the LSTM framework to construct a robust action recognition framework that takes the human skeletal joints as input and predicts the human action using the enhanced spatial-temporal attention model. Rigorous experiments on NTU datasets (by-far the largest benchmark RGB-D dataset) show that the proposed framework offers the best performance accuracy, least algorithmic complexity and training overheads, when compared with other state-of-the-art human action recognition models.

Good action recognition relies on correct interpretation of two critical attributes related to action: the spatial attribute on the detected person's posture, and the temporal attribute on the detected person's body movement. Whereas deep learning has greatly improved image recognition, we have not found a similar progress for action recognition. One of the main reasons is due to the complexity caused by the additional temporal dimension; another, to the fact that there are less annotated training data samples for action recognition than that for image recognition. In this regard, this paper proposes a handcrafted cued LSTM model for human action recognition based on RGB-D data, as a collection of 25 skeleton joints in 3D coordinates, found in NTU-RGB-D, currently the most comprehensive dataset for action recognition. As opposed to the raw data of skeleton joints, handcrafted cues, pre-processed results geared to facilitate focused learning, are proposed as input to the LSTM structure. In particular, pertaining to the spatial cue, the SVIT cue derived by Skeleton View-invariant Transformation is adopted; pertaining to the temporal cue, the Diff cue computed by taking the displacements of all joint across down-sampled raw data is utilized. Based on the train/test protocol, the experiment we conducted on NTU-RGB-D shows that the recognition result based on either of the proposed handcrafted cues is better than that based on the raw data. In addition, by our proposed techniques of feature fusion and/or decision fusion of these two handcrafted cues, the recognition performance is better than that of the state-of-the-art approaches conducting on the same dataset by same train/test protocol.

Motivated by the challenging difficulty in tracking an uncertain marine vehicle (MV) with unknown dynamics and disturbances to any unmeasurable/unknown trajectory, which is unresolved, an adaptive universe-based fuzzy control (AUFC) scheme with retractable fuzzy partitioning (RFP) in global universe of discourse (UoD) is created to achieve global asymptotic model-free trajectory-independent tracking. By defining an error surface and intensively exploring the MV structure, tracking error dynamics are sufficiently trimmed via separating external unknowns including trajectory dynamics and disturbances from internal nonlinearities dependent on tracking errors. An innovative retractable fuzzy approximator (RFA) using the RFP is developed to estimate internal nonlinearities and does not require a priori knowledge on the UoD, thereby contributing to a globally adaptive approximation based control approach in conjunction with Lyapunov synthesis. Together with RFA residuals, external unknowns are globally dominated by adaptive universal compensators driven by tracking error surface. Eventually, tracking errors and their derivatives globally asymptotically converge to the origin and all other signals of the closed-loop system are bounded. Simulation studies demonstrate superior performance of the proposed AUFC scheme in terms of both tracking and approximation.

One main difficulty in applying action recognition to practical applications is the need to segment beginnings and ends of actions in a continuous online monitoring process. This paper proposed a finite state machine (FSM) model for automatic action segmentation and recognition solution, based on pose streams in the form of skeleton joint data provided by Kinect. With the action recognition problem reframed as a state identification problem, the key solution to state identification hinges on detection of changing events, which signify the start of new action and the recognition of the underlying action. In that regard, a decision tree is constructed to detect these events based on the spatial positions and the changes of the skeleton data. In addition, to identify the current state or equivalently the action of a detected person, a fault observer is derived from the modeled action FSM. The fault observer does not only identify the initial state of action when the recognition process starts, but also serve the purpose of error recovery when the system loses track of ongoing events at times of intermittent sensor faults. Additionally, an AutoCorrect mechanism is presented to further enhance the accuracy of action recognition. To evaluate the proposed approach, an experiment with 300 participating subjects has been conducted for a total of 900 test sequences. The 98.63% correct identification result ensures the proposed approach a promising solution to constant action monitoring solution.

This paper presents an efficient approach for the use of recursive least square (RLS) learning algorithm in Takagi-Sugeno-Kang neural fuzzy systems. In the use of RLS, reduced covariance matrix, of which the off-diagonal blocks defining the correlation between rules are set to zeros, may be employed to reduce computational burden. However, as reported in the literature, the performance of such an approach is slightly worse than that of using the full covariance matrix. In this paper, we proposed a so-called enhanced local learning concept in which a threshold is considered to stop learning for those less fired rules. It can be found from our experiments that the proposed approach can have better performances than that of using the full covariance matrix. Enhanced local learning method can be more active on the structure learning phase. Thus, the method not only can stop the update for insufficiently fired rules to reduce disturbances in self-constructing neural fuzzy inference network but also raises the learning speed on structure learning phase by using a large backpropagation learning constant.

Decomposed fuzzy system (DFS) is a fuzzy system with a novel structure. Due to its excellent learning performance, DFS is originally proposed for an online learning control scheme and is shown to have effective learning performance. This paper is about the use of DFS for modeling dynamic systems. Since the learning mechanism used in online learning control is not suitable for modeling tasks, a commonly used back propagation learning algorithm is adapted for the use of DFS in modeling dynamic systems. The structure of DFS is to decompose each fuzzy variable into fuzzy subsystems that are called component fuzzy systems. Owing to the independency among component fuzzy systems, the learning for those parameters is also independent among different component fuzzy systems and thus, the learning can become more efficient. From the simulation results, it is evident that the proposed DFS can have much faster convergent speed. In addition, the DFS has a smaller testing error than those of other fuzzy systems.

The paper presents an improved radial basis function network to degrade the influence of the heteroscedasticity noises in the training data. A general purpose learning algorithm is regarded as the statistical nonlinear regression model which is assumed the constant noise level. However, the heteroscedasticity noises always exist in the real data. The transformation based least trimmed squares-support vector regression radial basis function network (LTS-SVR RBFN) employs the Box-Cox transformation and the LTS-SVR to address this problem. From the experiment results, it is evident that our proposed method can be used in a more realistic data.

Granular/symbolic data processing is an emerging conceptual and computing paradigm of information processing. In the era of big data, the emergence of granular/symbolic processing has been motivated by the urgent need for intelligent transformation of empirical data that are now commonly available in vast quantities, into a human-manageable knowledge. In such an aggregation process, we hope to retain as much information as possible while making the findings easily understood and well-supported by the existing experimental evidence. Those aggregated entities are often referred to as symbolic or granular data. Research areas referred to as symbolic data analysis in statistics and multivariate data analysis address some of the fundamental or applied facets of granular computing. The theoretical fundamentals of granular/symbolic data processing are well-established. They involve set theory (interval mathematics), fuzzy sets, rough sets, and random sets linked together in a highly comprehensive treatment of this emerging paradigm. In addition to interval-based formalism of information granules, we also encounter histograms, distributions, lists of values, etc. Hence, granular/symbolic data processing hinges on a general computation theory that effectively uses granules such as classes, clusters, subsets, groups, and intervals to build an efficient computational model for complex applications realized in the presence of huge amounts of data, information, and knowledge. This research arises as a substantial shift from the current machine-centric to human-centric approach to information and knowledge.

This paper investigates how to precisely transform images displaying color on organic light-emitting diodes (OLEDs) in real time for the purpose of energy-saving that meets the personal viewing quality requirements based on the structural similarity (SSIM) assessment. These methods could be applied to real-time energy-saving transformation of any displaying media, including video playback on OLEDs, and it will save up to 20% of the displaying power consumption based on the predicted SSIM = 0.9, which has very good image quality after the transformation.

A vision-based hand gesture recognition system is considered in this paper. Unlike other hand gesture recognition studies, our study considers a complicated background and possible dynamic motion of hands. Thus, instead of using simple background subtraction, in this study, many problems are considered, such as detection of skin color image, detection of when a hand reaches in the field of the camera view and detection of a full palm. The proposed system consists of four stages, detection of the appearance of hands, segmentation of hand regions, detection of full palm, and hand gesture recognition. Detection of the appearance of hands is to find out when a hand appears in the front of the camera. Moreover, some morphological techniques, along with two stage skin color detection, are employed to alleviate the effect of noise. The proposed two-stage skin color detection approach is adopted from the idea of handling outliers to extract the palm from a complicated background. Following that, detection of full palm is conducted to know whether the hand reaches beyond the field of the camera view. The concept of ergonomics is employed to determine whether the hand is beyond the field of the camera view. Finally, experimental results show that the proposed system is quite promising.

This paper presents a novel direct adaptive controller design via decomposed fuzzy Petri net to solve the control tracking problem. The controller combines decomposed fuzzy system (DFS) and Petri net to achieve good performance with less computation time. In the DFS structure, fuzzy variables are decomposed into several layers. DFS has been shown to have fast learning capability but with a complicated system structure. In this study, Petri net is employed to form a mechanism in constructing meaningful component fuzzy systems in the DFS so that the number of fuzzy components can be dramatically reduced without significantly degrading the modeling performance. Finally, the effectiveness of the proposed controller scheme is verified by simulation results.

In this paper, a novel fuzzy structure termed as the decomposed fuzzy system (DFS) is proposed to act as the fuzzy approximator for adaptive fuzzy control systems. The proposed structure is to decompose each fuzzy variable into layers of fuzzy systems, and each layer is to characterize one traditional fuzzy set. Similar to forming fuzzy rules in traditional fuzzy systems, layers from different variables form the so-called component fuzzy systems. DFS is proposed to provide more adjustable parameters to facilitate possible adaptation in fuzzy rules, but without introducing a learning burden. It is because those component fuzzy systems are independent so that it can facilitate minimum distribution learning effects among component fuzzy systems. It can be seen from our experiments that even when the rule number increases, the learning time in terms of cycles is still almost constant. It can also be found that the function approximation capability and learning efficiency of the DFS are much better than that of the traditional fuzzy systems when employed in adaptive fuzzy control systems. Besides, in order to further reduce the computational burden, a simplified DFS is proposed in this paper to satisfy possible real time constraints required in many applications. From our simulation results, it can be seen that the simplified DFS can perform fairly with a more concise decomposition structure.

In this paper, a novel control scheme adopted from moment control is proposed. In the proposed approach, an adaptive fuzzy system is employed to learn the effective moment. It is easy to see that such an approach can avoid wild guessing for the effective moment, and as shown in our simulation, can have nice control performance. In traditional adaptive fuzzy control approaches, bounds of system functions are required to facilitate supervisory control so as to have the robust control property. It can be expected that when those bounds used in the supervisory controller are not proper, the output may not be able to follow the reference trajectory satisfactorily. With the proposed moment adaptive fuzzy control, the bound needed is only the supremum of the control variance between two consecutive steps. It is much easier to predict. In our study, in order to further relax this requirement, another adaptive system is employed to estimate the residue of the moment adaptive fuzzy control system. It is called residue compensation in this paper. It can be found that with residue compensation, the approach does not need a supervisory controller, but still can quickly track the reference in a satisfactory fashion. Various simulations are conducted to demonstrate the effectiveness of the proposed approaches.

In the paper, based on the finiteL2-gain property, a way of estimating learning errors for direct adaptive fuzzy control systems is proposed, and then, a novel adaptive law with this estimated learning error is further proposed to improve the learning performance of the system in the sense of Lyapunov. In the literature, there is no direct way of estimating learning errors for direct adaptive fuzzy control systems. Based on the robust control approach proposed in our previous study, a way of estimating learning errors for direct adaptive fuzzy control can be derived, and as shown in our simulation, it can be found that such estimation is effective, as expected. It can be found that by incorporating this estimated learning error into the adaptive law, the proposed approach indeed can have much better learning performance. Besides, owing to good learning capability, the proposed control scheme can also have better tracking control performance. Simulation results clearly demonstrated the effectiveness of the proposed design.

This paper introduces a new structure of radial basis function networks (RBFNs) that can successfully model symbolic interval-valued data. In the proposed structure, to handle symbolic interval data, the Gaussian functions required in the RBFNs are modified to consider interval distance measure, and the synaptic weights of the RBFNs are replaced by linear interval regression weights. In the linear interval regression weights, the lower and upper bounds of the interval-valued data as well as the center and range of the interval-valued data are considered. In addition, in the proposed approach, two stages of learning mechanisms are proposed. In stage 1, an initial structure (i.e., the number of hidden nodes and the adjustable parameters of radial basis functions) of the proposed structure is obtained by the interval competitive agglomeration clustering algorithm. In stage 2, a gradient-descent kind of learning algorithm is applied to fine-tune the parameters of the radial basis function and the coefficients of the linear interval regression weights. Various experiments are conducted, and the average behavior of the root mean square error and the square of the correlation coefficient in the framework of a Monte Carlo experiment are considered as the performance index. The results clearly show the effectiveness of the proposed structure.

Support vector regression (SVR) employs the support vector machine (SVM) to tackle problems of function approximation and regression estimation. SVR has been shown to have good robust properties against noise. Besides, in SVR, outliers may also possibly be taken as support vectors. Such an inclusion of outliers in support vectors may lead to seriously overfitting phenomena. The rough set theory is successes to deal with imprecise, incomplete or uncertain for information system. In this paper, a novel regression approach, termed as the Rough Margin Support Vector Regression (RMSVR) network, is proposed to enhance the robust capability of SVR. The basic idea of the approach is to adopt the concept of rough sets to construct the model obtained by SVR and fine tune it with a robust learning algorithm. Simulation results of the proposed approach have shown the effectiveness of the approximated function in discriminating against outliers.

In the paper, an adaptive Fourier compensator is proposed to compensate the sliding decoupling control law for a class of uncertain coupling systems. Generally, on the hierarchical sliding mode, the sliding decoupling control law, which consists of an equivalent control law and a hitting control law, can be derived to stabilize the coupling system. When the system functions are subject to uncertainties, the control gain of the hitting control law can be adjusted to be a larger value to ensure the system stability. However, the control performance can not be guaranteed by the simple manner. So, in order to improve the control performance in this situation, an adaptive Fourier compensator is proposed to further compensate the sliding decoupling control law; the obtained control performance can be similar to that of the ideal sliding decoupling control law. The simulation results clearly demonstrate the effectiveness of the proposed adaptive Fourier compensator.

Statechart has been utilized as a visual formalism for the modeling of complex systems. It illuminates the features on describing properties of causality and concurrency. This paper focuses on the use of statecharts to model the preemption of emergency vehicles system. The advantage of the proposed approach is the clear presentation of traffic lights' behaviors in terms of conditions and events that cause the preemption phase. Moreover, the paper also proposes a new emergency vehicle preemption policy that provides the emergency vehicles can pass through the intersections with minimal delay. The analysis of the control models is performed to demonstrate how the models enforce the lights' transitions by reachability tree method. And then the reachability and reversibility properties of the control statecharts will be obtained.

In this study, we continue our analysis on the use of RLS in neural fuzzy systems. The recursive least square (RLS) algorithms can have great learning performance for neural fuzzy networks. From our previous work, it can be observed that the advantages of using RLS instead of using BP are not so obvious. For the use of forgetting factor in RLS, the idea is to account for the effects of the change in the premise part. In this study, we have observed that the use of a forgetting factor can still have some advantages when the premise part is fixed. The idea is similar to the used of Widrow-Hoff learning concept in the backpropagation learning algorithm. From our experiments, a strong forgetting factor (smaller value) can let the consequent part trace the error in the learning phase. But the testing error becomes very large. When the system capacity is sufficient, a forgetting factor will improve both in the learning phase and in the testing phase. Finally, the initial value of the covariance matrix is considered. The learning capacity will rise when the initial value increases. But it will increase the error tracing phenomenon in the consequent part too. But it is opposite in a system with less learning capacity.

Self-constructing neural fuzzy inference network (SONFIN) is a neural fuzzy system and owing to its structure learning capability, SONFIN has been demonstrated to have excellent learning performance. However, various parameters must be selected in the implementation of SONFIN. In this paper, the learning behavior of SONFIN is studied. First, the SONFIN system with different thresholds and variances are considered. Different selections will result in different rule numbers and membership function widths. Our experiment results indicate that when there are possibilities of overfitting, more rules may not always come up with better performance. Secondly, two different learning algorithms are considered; the backpropagation (BP) learning algorithm and the recursive least square (RLS) algorithm. It can found that the learning of using RLS is much faster than that of using BP as expected. However, it can be found that when overfitting may occur, BP can have better learning performance in terms of testing errors. Finally, the use of reset for the covariance matrix in the RLS algorithm is investigated. From this primitive study, it can be found that the learning algorithms or some parameter selections may have both good effects in the testing performance and the training performance before the learning does not have significant overfitting. However, when the learning crosses this point, any selection is good for learning may bring bad effects on the testing performance.

In the paper, the Lyapunov stable theory and L2-gain control performance are employed to interpret a sliding controller design with the proportional-integral (PI) control form and the integral sliding surface. First, a theoretical analysis of the considered sliding PI controller is provided. Our analysis reveals that the proportional control term of the sliding PI controller, i.e., sliding proportional controller, can lead the energy of system state error to be bounded and thus, the system fundamental robustness can be established. The integral control term of the sliding PI controller, i.e., sliding integral controller, can guarantee the system stability further. Secondly, a direct adaptive design is further proposed to estimate the optimal proportional gain of the sliding proportional controller, in which the sliding integral controller is employed to handle the total uncertainty with unknown upper bound in the adaptive control design. Therefore, the selection problem of the proportional gain is resolved and the valuable robust control property of the sliding integral controller is illustrated again. Finally, three simulations are provided to demonstrate these results.

One disadvantage of hardware controller is that it cannot learn from experiential information. Many software neural networks and fuzzy algorithms can resolve this problem, but their processes are complicated and waste time. Therefore, a simple firmware controller with learning ability programmed on FPGA is proposed in this paper. The proposed firmware controller is a CMAC based supervisory controller. The firmware memory includes two parts: (1) the software algorithm, which is designed by the CMAC-Based supervisory controllers; (2) the hardware structure, which is implemented by FPGA. The VHDL is utilized to program FPGA as the firmware controller. In our implementation, CMAC is used as the learning mechanism because of its quick learning capability, and it can learn the perfect control law of the supervisory control system. Thus, the CMAC-based supervisory controller can be realized easily. Finally, the proposed controller will be implemented in the FPGA to demonstrate its control ability.

In this study, an effective and systematical robust approach for adaptive fuzzy-control-system design is proposed. In the design, a compensative-control law is proposed to provide the finite$L_2$-gain property for direct-adaptive fuzzy-control systems to cope with possible external disturbances and approximation errors of the system. An integral term is further introduced into the compensative-control law to provide a more stable edge in order to have better control performance. With such a compensative-control law, the control performance can be anticipated in the$L_2$-gain robust-control sense. As a consequence, the dead-zone modification approach can then be employed to resolve the parameter-drift problem occurring in traditional learning of adaptive fuzzy-control systems. Finally, various simulations are conducted to demonstrate the effectiveness of the proposed approach.

In this paper, an adaptive fuzzy switched swing-up and sliding controller (AFSSSC) is proposed for the swing-up and position controls of a double-pendulum-and-cart system. The proposed AFSSSC consists of a fuzzy switching controller (FSC), an adaptive fuzzy swing-up controller (FSUC), and an adaptive hybrid fuzzy sliding controller (HFSC). To simplify the design of the adaptive HFSC, the double-pendulum-and-cart system is reformulated as a double-pendulum and a cart subsystem with matched time-varying uncertainties. In addition, an adaptive mechanism is provided to learn the parameters of the output fuzzy sets for the adaptive HFSC. The FSC is designed to smoothly switch between the adaptive FSUC and the adaptive HFSC. Moreover, the sliding mode and the stability of the fuzzy sliding control systems are guaranteed. Simulation results are included to illustrate the effectiveness of the proposed AFSSSC.

The research about sliding based approaches is a widely studied topic to the adaptive fuzzy control system designs. In this paper, a novel state error feedback sliding controller is proposed. An optimal feedback gain is required and in this study it is assumed to be unknown. Usually, a rudimentary feedback gain is used. Besides, in order to approximate the state error feedback sliding controller with the optimal feedback gain, an adaptive fuzzy system is employed. Thus, the proposed control scheme consists of an adaptive fuzzy system and a state error feedback sliding controller with a rudimentary feedback gain. In the system framework, the rudimentary state error feedback sliding controller can be viewed as the approximate error estimator of the adaptive fuzzy system. Therefore, such an estimated error can be fed back to the learning of the fuzzy system through a modified adaptive law. With such an approximate error feedback, it is clearly evident from our simulation that the learning speed of the proposed learning scheme is faster than that of the original scheme. Also, with the proposed controller, the system stability not only is guaranteed, but also becomes more stable.

In our earlier study, the property of L2-gain robust control is incorporated into the direct adaptive fuzzy control design with the use of the compensative controller. The tracking control performance is guaranteed. In the study, we further employ the L2-gain property to improve the adaptive law design. Due to the L2-gain control property, the approximate error of the fuzzy system is estimable, and thus, an adaptive law with the approximate error feedback is proposed in the study. The novel adaptive law improves the learning speed so that the learning performances can be better. Since the L2-gain based compensative controller can guarantee the tracking control performance and remove the effect of the gain function in the adaptive law. The direct adaptive fuzzy control system design of the paper not only has the satisfactory tracking control performance but also has the better learning performance. Various simulations are conducted to demonstrate the effectiveness of the proposed design.

In order to solve the parameter drift problem of learning fuzzy control systems, a compensate controller is proposed based on the H-infinite control theory. The proposed compensate controller has the H-infinite tracking capability and thus is named as the H-infinite tracking compensator (HTC). The characteristic of the HTC design is that the concept of Lyapunov energy convergence is employed to solve the H-infinite controller design problem. Besides, for a leaning fuzzy control system, the HTC ensures the tracking error of the learning fuzzy control system is bounded. Therefore, the dead-zone modification can be used to stabilize the final learning stage of the adaptive fuzzy control system. In other words, the parameter drift problem of a learning fuzzy control system can be resolved by the use of dead-zone modification with HTC so that the stability problem does not appear.

A serious problem limiting the applicability of the fuzzy neural networks is the “curse of dimensionality”, especially for general continuous functions. A way to deal with this problem is to construct a dynamic hierarchical fuzzy neural network. In this paper, we propose a two-stage genetic algorithm to intelligently construct the dynamic hierarchical fuzzy neural network (IfFNN) based on the merged-FNN for general continuous functions. First, we use a genetic algorithm which is popular for flowshop scheduling problems (GA_FSP) to construct the INN. Then, a reduced-form genetic algorithm (RGA) optimizes the INN constructed by GA _FSP. For a real-world application, the presented method is used to approximate the Taiwanese stock market.

In this study, an idea to the self-tuning sliding mode controller design is proposed. The proposed controller not only reduces the chatting phenomenon, but also needs less the system bounds than traditional sliding mode controllers do. The proposed self-tuning sliding mode controller can be viewed as an improved hitting controller. Besides, in order to resolve the parameter selection problem, the Lyapunov stability theory is considered and a self-tuning machine is employed to reduce the conjecture of parameter selection. Finally, the proposed controller is employed to control a two-link robot arm, which is a class of MIMO nonlinear control system. The result is promising.

The CMAC tuning effects to improve the Hinfin control performance is proposed in this paper. The Hinfin norm is utilized for evaluating the supremum of the robust control variable such that it ensures the Lyapunov stability of the proposed control schemes. Under the Lyapunov stable criterion, we apply the novel pre-step CMAC online tuning scheme for tuning the control variable in order to improve the system output performance. Two kinds control schemes are employed to simulate and discuss in this paper: the supervisory robust control and the pre-step CMAC robust control schemes. Finally, simulation results demonstrate that the pre-step CMAC robust tuning control schemes can efficiently save control power and decrease control error, which make the system output follow the control command quickly and accurately.

This paper addresses the problem of designing robust static output-feedback controllers for nonlinear discrete-time interval systems with time delays both in states and in control input based on the Takagi-Sugeno fuzzy model. In the approach, we do not directly employ the Lyapunov approach, as do in most of traditional fuzzy control design approaches. Instead, sufficient conditions for guaranteeing the robust stability for the considered systems are derived in terms of the matrix spectral norm of the closed-loop fuzzy system. The sufficient conditions are further formulated into linear matrix inequalities so that the desired controller can be easily obtained by using the Matlab LMI toolbox.

The algorithm of online predictor from input-output data pairs will be proposed. In this paper, it proposed an approach to generate fuzzy rules of predictor from real-time input-output data by means of ARMA model concept for unknown system. It includes two phase: (1). generating fuzzy rules phase, (2). online learning phase; If the error between the real output and the predictor's output is larger than the desired error, it means that the lack of the fuzzy rules. Thus, it will generate some new fuzzy rules for the fuzzy predictor or adding an output term in the premise part of fuzzy rules. From the generating fuzzy rules phase, it can online generate the fuzzy predictor. In another word, some redundant rules may be generated from bad information after learning. They may be incoming data include outliers, noises or uncertainties. Such bad rules will be discarded by a usage degree constant. To achieve good performance for this fuzzy predictor, the parameters of each fuzzy rule may be adjusted by on-line learning, when the prediction error into a pre-defined bound. In the simulation example, a nonlinear time-varying process operating in open loop is illustrated. Simulations and real-time results show the advantages of the proposed method.

This paper proposes on-line modeling via Takagi-Sugeno (T-S) fuzzy models and robust adaptive control for a class of generalized multiple input multiple output (MIMO) nonlinear dynamic systems with external disturbances. The T-S fuzzy model is established to approximate the nonaffine nonlinear dynamic system in a linearized way and is used to be an error compensator for external disturbances and system uncertainly, i.e. the unmodeled dynamics, modeling errors and external disturbances. In second type adaptive laws, fuzzy B-spline membership functions (BMFs) are developed for on-line tuning. In this paper, we can prove that the closed-loop system which is controlled by the proposed controller is stable and the tracking error will converge to zero.

This paper proposes an antilock braking system (ABS), in which unknown road characteristics are resolved by a road estimator. This estimator is based on the LuGre friction model with a road condition parameter, and can transmit a reference slip ratio to a slip ratio controller through a mapping function considering the effect of wheel angular velocity. In the controller design, a direct adaptive fuzzy-neural controller (DAFC) for an ABS is developed. Finally, this paper gives simulation results of an ABS with the road estimator and the DAFC, and shows good effectiveness under varying road conditions.

The design target of a supervisory controller (SC) is to stabilize the system states within a designate region. In this paper, we propose two improved SCs designed for SISO nonlinear system based on the Lyapunov stable theory. These improved SCs have the original stabilize function and moreover they can have acceptable control ability even if the system does not have a main controller. Since these SCs design is independent to the main controller, they can be applied to many different main controllers with the constraint that the bounds information of plant model must be known. In addition, we design a bounded lay to improve the chattering problem based on the improved SC form. Finally, we present numerical simulations to illustrate the correctness and practicability of the improved SCs.

This paper proposes on-line modeling via Takagi-Sugeno (T-S) fuzzy models and robust adaptive control for a class of unknown nonlinear dynamic systems with external disturbances. The T-S fuzzy model is established to approximate an unknown nonlinear dynamic system in a linearized way. Fuzzy B-spline membership functions (BMFs) which possesses a fixed number of control points are developed for on-line tuning. In this paper, the closed-loop system which is controlled by the proposed controller can be stabilized and the tracking error will converge to zero. An example is simulated in order to confirm the effectiveness and applicability of the proposed methods in this paper.

Fuzzy controller designing approaches are represented by TSK fuzzy models. Traditional structure learning algorithms are to adjust the parameters in the fuzzy rules based on modeling error. Such an approach will result an improper clustering structure, especially, when the training data are corrupted with outliers. Such a controller is called improper clustering structure fuzzy controllers (IPFC). The paper proposes a way of designing controllers with proper clustering structure (PFC) for affine TSK fuzzy models directly from training data, which may contain noise and outliers. Based on the Lyapunov theorem, an instability sufficient condition for modeling-error bound is derived. Furthermore, the adaptive law to tune the parameters of consequent parts is also obtained. Various simulations are conducted and the results verify that the PFC indeed showed superior performance over other IPFCs.

In this paper, a new method, applied the annealing robust Walsh function networks, is proposed to discretize the continuous-time controller in computer-controlled systems. That is, the annealing robust Walsh function networks are used to add nonlinearly and to approximate smooth controller with digital neural networks. Hence, the proposed controller is a new smooth controller that can replace original controller and independent of the sampling time under the Sample Theorem. Besides, the input-output stability is proposed for this discretizating continuous-time controller with the annealing robust Walsh function networks. Consequently, the proposed annealing robust Walsh function networks controller cannot only discretize the continuous-time controllers, but can also tolerate a wider range of sampling time uncertainty.

In this paper, we reported our study on solving 0/1 knapsack problem effectively by using ant colony optimization. The 0/1 knapsack problem is to maximize the total profit under the constraint that the total weight of all chosen objects is at the most weight limit. In our study, we viewed the search in ant colonies as a mechanism providing a better performance and it has the ability to escape from local optima. In this paper, several examples are tested to demonstrate the superiority of the proposed algorithm. From simulation results, the proposed algorithm indeed has remarkable performance.

This paper proposes an observer-based adaptive fuzzy-neural controller, structured by a merged fuzzy-neural network (merged-FNN) to reduce the number of adjustable parameters. In this paper, the merged-FNN is proved to take the place of the traditional fuzzy-neural networks under some assumptions. Moreover, the overall adaptive schemes using the proposed merged-FNN guarantees that all signals involved are bounded and the output of the closed-loop system asymptotically tracks the desired output trajectory. From experimental examples, the proposed merged-FNN has far fewer parameters than the traditional FNN, and the computation time is significantly reduced. To demonstrate the effectiveness of the proposed methods, simulation results are illustrated in this paper.

In this paper we present some performance comparisons of several unsupervised clustering techniques include: Self-Organizing Map (SOM), Fuzzy C-means (FCM) and hierarchical clustering, and they are employed to analyze the ovarian cancer microarray data. The data includes 15 samples with 9,600 genes and these samples include 5 benign ovarian tumors (OVT), 1 borderline ovarian malignancy (OVTT), 4 ovarian cancers at stage I (OVCAI), and 5 ovarian cancers at stage III (OVCAIII). A regression analysis is used to reduce the dimension and get 9600 residuals of genes. The genes with 100 largest and 100 smallest residual are picked to analyze using analysis of variance (ANOVA). After the ANOVA, 12 gene markers are got and can be used to distinguish OVT, OVTT, OVCAI and OVCAIII samples. The 12 gene markers are performed clustering by the SOM, FCM and hierarchical clustering techniques and to compare the results between these clustering techniques. Our experimental results show that the hierarchical clustering can get best performance of clustering and users do not need to define the number of clusters.

In this paper, the online learning capability and the robust property for the learning algorithms of cerebellar model articulation controllers (CMAC) are discussed. Both the traditional CMAC and fuzzy CMAC are considered. In the study, we find a way of embeding the idea of M-estimators into the CMAC learning algorithms to provide the robust property against outliers existing in training data. An annealing schedule is also adopted for the learning constant to fulfill robust learning. In the study, we also extend our previous work of adopting the credit assignment idea into CMAC learning to provide fast learning for fuzzy CMAC. From demonstrated examples, it is clearly evident that the proposed algorithm indeed has faster and more robust learning. In our study, we then employ the proposed CMAC for an online learning control scheme used in the literature. In the implementation, we also propose to use a tuning parameter instead of a fixed constant to achieve both online learning and fine-tuning effects. The simulation results indeed show the effectiveness of the proposed approaches.

Support vector regression (SVR) based on statistical learning is a useful tool for nonlinear regression problems. The SVR method deals with data in a high dimension space by using linear quadratic programming techniques. As a consequence, the regression result has optimal properties. However, if parameters were not properly selected, overfitting and/or underfilling phenomena might occur in SVR. Two parameters /spl sigma/, the width of Gaussian kernels and /spl epsi/, the tolerance zone in the cost function are considered in this research. We adopted the concept of the sampling theory into Gaussian filter to deal with parameter /spl sigma/. The idea is to analyze the frequency spectrum of training data and to select a cut-off frequency by including 90% of power in spectrum. The corresponding /spl sigma/ can then be obtained through the sampling theory. In our simulations, it can be found that good performances are observed when the selected frequency is near the cut-off frequency. For another parameter /spl epsi/, it is a tradeoff between the number of support vectors and the RMSE. By introducing the confidence interval concept, a suitable selection of /spl epsi/ can be obtained. The idea is to use the L/sub 1/-norm (i.e., when /spl epsi/ = 0 ) to estimate the noise distribution of training data. When /spl epsi/ is obtained by selecting the 90% confidence interval, simulations demonstrated superior performance in our illustrative example. By our systematical design, proper values of /spl sigma/ and /spl epsi/ can be obtained and the resultant system performances are nice in all aspects.

In general, the support vector regression (SVR) is very suitable to approximate a high dimensionality space and ill-posed problem in modeling. That is, the SVR consists of a quadratic programming problem that can be solved efficiently and guaranteed to find a global extremism. Therefore, for the complex data, the SVR is easy to reconstruct an approximated model based on the linear programming technique. On the other hand, a typical microarray data consists of expression levels for a large number of genes on a relatively small number of samples. In order to avoid higher computational complexity and larger prediction errors on high-dimensional problem, we proposed the dimension reduction with SVR for the ovarian cancer microarray data. That is. The SVR can reduce dimension on each sample from 9600 genes to about three hundreds genes. Besides, we can choose the εvalue εvalue εvalue εvalue εvalue εvalue in the loss-function of SVR to obtain the variable number of gene and the proposed method can also overcome the block effect of microarray data. Finally, these results can provide for gene class discovery and gene class prediction.

This paper provides an intelligent system, a fuzzy-genetic algorithm (FGA) with local search, for multiple sequences alignment. The general multiple sequence alignment, known as NP-hard problem, refers to search for maximal similarity in three or more sequences. The proposed algorithm is to enhance the performance of genetic algorithm by incorporating local search andfuzzy set theory for multiple sequence alignment. In the proposed algorithm, genetic algorithms perform a multiple directional search by maintaining a set ofsolutions. Local search operators are performed to explore the neighborhood in an attempt to enhance the fitness of the solution in a local manner. Moreover, fuzzy set theory is designed to dynamically adjust the probability ofcrossover, mutation and local search during evolutionary process. Results from our experiments indicate that our approach can obtain good performance in the majority of data sets with both low similarity and high diversity.

This paper presents a method of discrete time nonlinear system identification using a HopfieId neural network (HNN) as a coefficient learning mechanism to obtain optimized coefficients over a set of Gaussian basis functions. A linear combination of Gaussian basis functions is used to replace the nonlinear function of the equivalent discrete time nonlinear system. The outputs of the HNN, which are coefficients over a set of Gaussian basis functions, are discretized to be a discrete Hopfield learning model. Using the outputs of the HNN, one can obtain the optimized coefficients of the linear combination of Gaussian basis functions conditional on properly choosing an activation function scaling factor of the HNN. The main contributions of this paper is that the convergence of learning of the HNN can be guaranteed if the activation function scaling factor is properly chosen. Finally, to demonstrate the effectiveness of the proposed methods, simulation results are illustrated in this paper.

This paper addresses the problem of designing robust static output-feedback controllers for nonlinear discrete-time interval systems with time delays both in states and in control input. In the approach, we do not directly employ the Lyapunov approach, as do in most of traditional fuzzy control design approaches. Instead, sufficient conditions for guaranteeing the robust stability for the considered systems are derived in terms of the matrix spectral norm of the closed-loop fuzzy system. The stability conditions are further formulated into linear matrix inequalities so that the desired controller can be easily obtained by using the Matlab linear matrix inequality (LMI) toolbox. Finally, an example is provided to illustrate the effectiveness of the proposed approach.

The paper proposes a way of designing state feedback controllers for affine Takagi-Sugeno-Kang (TSK) fuzzy models. In the approach, by combining two different control design methodologies, the proposed controller is designed to compensate all rules so that the desired control performance can appear in the overall system. Our approach treats all fuzzy rules as variations of a nominal rule and such variations are individually dealt with in a Lyapunov sense. Previous approaches have proposed a similar idea but the variations are dealt with as a whole in a robust control sense. As a consequence, when fuzzy rules are distributed in a wide range, the stability conditions may not be satisfied. In addition, the control performance of the closed-loop system cannot be anticipated in those approaches. Various examples were conducted in our study to demonstrate the effectiveness of the proposed control design approach. All results illustrate good control performances as desired.

The terrain location identification problem represents a very complicated learning task. Beside of learning from noisy and nondeterministic training data, the training task must learn from a very large size of training data, which may lead to lots of learning problems. A phenomenon called the fake convergence is observed in our implementation. In that case, the training process seemed to converge to a fixed error level, but the actual error is much higher than the converged one. In our study, a fuzzy hierarchical network is proposed to cope with the problem of large training data sets. With this fuzzy hierarchical structure, the learning process can become fast and errors are significantly reduced. Another issue is regarding about embedding domain knowledge into the learning structure of neural fuzzy networks. The idea is simple but effective. The proposed structure is called the fuzzy hierarchical data fusion network and its learning performance is significantly better than that of original fuzzy hierarchical networks. With the use of fuzzy hierarchical data fusion networks, errors indeed can converge and the system becomes practically applicable.

In the literature, an approach, Markov Fourier Grey model (MFGM) has been proposed to incorporate global information based on local prediction schemes. In traditional forecasting, people may want to predict the next data and this kind of prediction is called one-step prediction. Nevertheless, we may also need to make multi-step prediction. From our simulation, it can be found that local prediction schemes of MFGM can have nice performance in one-step prediction. However, they usually have awful performance for multi-step prediction. In this study, we study approaches in combining local and the global prediction results. Neural networks are widely used to predict time series. In our study, neural networks are employed as global prediction schemes and Fourier Grey Model (FGM) is employed as local prediction schemes. In the paper, we proposed a neural network based approach for the fusion of global and local information in predicting time series.

In this paper, the online learning capability and the robust property for the learning algorithms of cerebellar model articulation controllers (CMAC) are discussed. Both the traditional CMAC and fuzzy CMAC are considered. A credit assignment idea is adopted to provide fast learning for CMAC. The idea is to distribute errors proportional to the inverse of learning times, which are viewed as the credibility of addressed cells. In the paper, we also embed the M-estimator into the CMAC learning algorithms to provide the robust property against noise or outliers existing in training data. An annealing schedule is also adopted to suitably define a scale estimate required in the M-estimator. From example simulations, it is clearly evident that the proposed algorithm indeed has faster and more robust learning than traditional CMAC does. Besides, we also employ the proposed CMAC for an online learning control scheme used in the literature. The simulation results indeed show the effectiveness of the proposed approaches.

A general weapon-target assignment (WTA) problem is to find a proper assignment of weapons to targets with the objective of minimizing the expected damage of own-force asset. Genetic algorithms (GAs) are widely used for solving complicated optimization problems, such as WTA problems. In this paper, a novel GA with greedy eugenics is proposed. Eugenics is a process of improving the quality of offspring. The proposed algorithm is to enhance the performance of GAs by introducing a greedy reformation scheme so as to have locally optimal offspring. This algorithm is successfully applied to general WTA problems. From our simulations for those tested problems, the proposed algorithm has the best performance when compared to other existing search algorithms.

In this paper, a novel learning scheme is proposed to speed up the learning process in cerebellar model articulation controllers (CMAC). In the conventional CMAC learning scheme, the correct numbers of errors are equally distributed into all addressed hypercubes, regardless of the credibility of the hypercubes. The proposed learning approach uses the inverse of learned times of the addressed hypercubes as the credibility (confidence) of the learned values, resulting in learning speed becoming very fast. To further demonstrate online learning capability of the proposed credit assigned CMAC learning scheme, this paper also presents a learning robust controller that can actually learn online. Based on robust controllers presented in the literature, the proposed online learning robust controller uses previous control input, current output acceleration, and current desired output as the state to define the nominal effective moment of the system from the CMAC table. An initial trial mechanism for the early learning stage is also proposed. With our proposed credit-assigned CMAC, the robust learning controller can accurately trace various trajectories online.

Support vector regression (SVR) employs the support vector machine (SVM) to tackle problems of function approximation and regression estimation. SVR has been shown to have good robust properties against noise. When the parameters used in SVR are improperly selected, overfitting phenomena may still occur. However, the selection of various parameters is not straightforward. Besides, in SVR, outliers may also possibly be taken as support vectors. Such an inclusion of outliers in support vectors may lead to seriously overfitting phenomena. In this paper, a novel regression approach, termed as the robust support vector regression (RSVR) network, is proposed to enhance the robust capability of SVR. In the approach, traditional robust learning approaches are employed to improve the learning performance for any selected parameters. From the simulation results, our RSVR can always improve the performance of the learned systems for all cases. Besides, it can be found that even the training lasted for a long period, the testing errors would not go up. In other words, the overfitting phenomenon is indeed suppressed.

In the literature, researchers have introduced delay feedback (or recurrent) networks and claimed that those networks could accurately model dynamical systems without knowing their system orders. In this paper, we have studied those delay feedback networks and also proposed a better version of delay feedback neural-fuzzy networks, called additive delay feedback neural-fuzzy networks (ADFNFN). From our simulations for various examples, it is clearly evident that ADFNFN can have the best modeling accuracy among those existing delay feedback networks. Nevertheless, we also showed by examples that those delay feedback networks can only reach the accuracy of nonlinear autoregressive with exogenous inputs (NARX) models with order two, and that the number of delays in delay feedback networks plays the same role as the order in NARX models.

In this paper, we report our study on embedding fuzzy mechanisms and knowledge into box-type reinforcement learning controllers. One previous approach for incorporating fuzzy mechanisms can only achieve one successful run out of nine tests compared to eight successful runs in a nonfuzzy learning control scheme. After analysis, the credit assignment problem and the weighting domination problem are identified. Furthermore, the use of fuzzy mechanisms in temporal difference seems to play a negative factor. Modifications to overcome those problems are proposed. Furthermore, several remedies are employed in that approach. The effects of those remedies applied to our learning scheme are presented and possible variations are also studied. Finally, the issue of incorporating knowledge into reinforcement learning systems is studied. From our simulations, it is concluded that the use of knowledge for the control network can provide good learning results, but the use of knowledge for the evaluation network alone seems unable to provide any significant advantages. Furthermore, we also employ Makarovic's (1988) rules as the knowledge for the initial setting of the control network. In our study, the rules are separated into four groups to avoid the ordering problem.

Traditional model-free prediction approaches, such as neural networks or fuzzy models use all training data without preference in building their prediction models. Alternately, one may make predictions based only on a set of the most recent data without using other data. Usually, such local prediction schemes may have better performance in predicting time series than global prediction schemes do. However, local prediction schemes only use the most recent information and ignore information bearing on far away data. As a result, the accuracy of local prediction schemes may be limited. In this paper a novel prediction approach, termed the Markov-Fourier gray model (MFGM), is proposed. The approach builds a gray model from a set of the most recent data and a Fourier series is used to fit the residuals produced by this gray model. Then, the Markov matrices are employed to encode possible global information generated also by the residuals. It is evident that MFGM can provide the best performance among existing prediction schemes. Besides, we also implemented a short-term MFGM approach, in which the Markov matrices only recorded information for a period of time instead of all data. The predictions using MFGM again are more accurate than those using short-term MFGM. Thus, it is concluded that the global information encoded in the Markov matrices indeed can provide useful information for predictions.

The Takagi-Sugeno-Kang (TSK) type of fuzzy models has attracted a great attention of the fuzzy modeling community due to their good performance in various applications. Most approaches for modeling TSK fuzzy rules define their fuzzy subspaces based on the idea of training data being close enough instead of having similar functions. Besides, training data sets algorithms often contain outliers, which seriously affect least-square error minimization clustering and learning algorithms. A robust TSK fuzzy modeling approach is presented. In the approach, a clustering algorithm termed as robust fuzzy regression agglomeration (RFRA) is proposed to define fuzzy subspaces in a fuzzy regression manner with robust capability against outliers. To obtain a more precision model, a robust fine-tuning algorithm is then employed. Various examples are used to verify the effectiveness of the proposed approach. From the simulation results, the proposed robust TSK fuzzy modeling indeed showed superior performance over other approaches.

In this research, the problems of how to teach a robot to execute skilled operations are studied. Human workers usually accumulate his experience after executing the same task repetitively. In the process of training, the worker must find ways of adjusting his/her execution. In our system, the parameters for the impedance control scheme are used as the targets for adjustment. After mass amount of training, the worker is supposed to be able to execute deburring tasks successfully. This is because the worker might have gotten some knowledge about tuning the parameters required in the impedance control scheme. Thus, the rules for adjusting the parameters in impedance control are the operational skills to be identified. In this research, a training scheme, called the evolution-based virtual training scheme, is proposed in extracting knowledge for robotic deburring tasks. In this approach, an evolution strategy is employed for searching for the best set of fuzzy rules. This learning scheme has been successfully applied in adjusting the parameters of impedance controllers required in deburring operations. In general, the results of deburring are much more satisfactory when compared with those in previous research. When executing a deburring task, the robot simulator can find its optimal adjusting rules for parameters after several generations of evolution.

Multilayer feedforward neural networks are often referred to as universal approximators. Nevertheless, if the used training data are corrupted by large noise, such as outliers, traditional backpropagation learning schemes may not always come up with acceptable performance. Even though various robust learning algorithms have been proposed in the literature, those approaches still suffer from the initialization problem. In those robust learning algorithms, the so-called M-estimator is employed. For the M-estimation type of learning algorithms, the loss function is used to play the role in discriminating against outliers from the majority by degrading the effects of those outliers in learning. However, the loss function used in those algorithms may not correctly discriminate against those outliers. In this paper, the annealing robust backpropagation learning algorithm (ARBP) that adopts the annealing concept into the robust learning algorithms is proposed to deal with the problem of modeling under the existence of outliers. The proposed algorithm has been employed in various examples. Those results all demonstrated the superiority over other robust learning algorithms independent of outliers. In the paper, not only is the annealing concept adopted into the robust learning algorithms but also the annealing schedule k/t was found experimentally to achieve the best performance among other annealing schedules, where k is a constant and is the epoch number.

This paper presents a systematic procedure for generating the goal region of a mating action from a model of assemblies. The goal region of a mating action is defined as the acceptable destination of the moving object and is used to identify the successful situation of the mating action. In this paper, goal regions are constructed by using the mating features and the constraint types residing in the model of assemblies. The mating features identify the important variables between which the interdependencies are considered, and the constraint types identify the necessary constraints that mating features cannot provide. An analytical solution for C-space interior is developed to construct the goal region from 2D features. The effects of interference and fine-motion planning on success probabilities are also taken into consideration to define theoretical goal regions; goal regions are also subject to uncertainties. An approach that shrinks the nominal boundary of a goal region is also proposed to compute the expectation of success probabilities.

An assembly planning system that takes the CAD model description of a product as input and automatically generates executable plans not only can automate assembly processes, but also can provide assembly properties of the product through the simulation of all possible assembly plans. Yet, to achieve such automation, the necessary information required in describing the executable action must be automatically generated from the model of a product so that the effective simulation of the assembly process is possible. In this paper, the generation of the destination of a mating action in the presence of uncertainty is presented. When a mating action in an assembly process is required to be executed, the action description developed from the model of a product usually only specifies the mating properties of the action, like "peg in hole". Thus, with the purpose of effective simulation, a set of locations must be derived from the mating property to identify the acceptable region of the goal of the action such that the uncertainties occurring in the representation of object dimensions, action motions, and sensory information can be incorporated in the assembly process. In the authors' previous work, a systematic procedure has been proposed to automatically generate such a goal region. However, goal regions can only provide the information of the acceptable goals of the corresponding moving objects. In specifying a goal in a planning process or in the description of an executable action, a point is required to define the destination of a mating action in order that the system can know how to perform the action. In this paper, with the concept of reliability indices, the destination of a goal region, which is assumed to be a convex polygon, is considered to be the point from which the minimal distance to the edges of the polygon is the maximum, and then the destination can yield the maximal estimated success probability on executing the mating action. An algorithm with the time complexity of O(NlogN) is proposed to find the point from which the minimal distance to the edges of the polygon is the maximum.>

The authors examine issues involved in integrating the design level and the assembly planning level with a feedback loop. The integration is performed in two stages. The first stage focuses on the evaluation of an assembly plan. Evaluation criteria that can pinpoint areas which need redesign are defined. The second stage is to use the evaluation results to come up with the actual redesign. Algorithms are developed for performing evaluation of assembly plans. From the evaluation results, means of generating hints for redesign are discussed. The hints are then processed and calls are made to the redesign operators to perform the actual redesign of components. The integrated design-planning system has the ability to identify parts that need redesign and the ability to come up with feasible redesign options in polynomial time.>

A systematic methodology of manipulating and propagating spatial uncertainties in the form of homogeneous transforms and in a probabilistic sense is presented. Uncertainties are represented by covariance matrices and the manipulation of uncertainties focuses on the spatial uncertainty propagation and the uncertainty fusion. To integrate the uncertainty information into a task plan that consists of a sequence of primitive actions, the propagation of uncertainty before and after a primitive action such as moving action, perception action, and contact action is developed. A simple and optimal solution for maintaining the consistency in the world state is proposed for perception actions. To determine the applicability of an action, forward propagation and backward propagation are proposed to verify the success of an action in the presence of uncertainties. It is shown that the backward propagation method can be used to determine an admissible set of an action with a specified acceptable success confidence and/or to efficiently apply perception actions to reduce the uncertainty.>

Uncertainties are represented in the form of homogeneous transforms, and the manipulation of uncertainties is focused on the spatial uncertainty propagation and the uncertainty fusion. In order to integrate the uncertainty information into a task plan which consists of a sequence of primitive actions, the propagation of uncertainty before and after a primitive action, such as moving, perception, or contact action, has been developed. A simple and optimal solution for the consistency problem is proposed for perception actions. Using conditional probability and the evidence collecting technique, the correlation between motion parameters, when a contact occurs, can also be taken into account. To determine the applicability of an action, forward propagation and backward propagation are proposed to verify the success of the action in the presence of uncertainties. This backward uncertainty propagation method can be used to determine the admissible set of an action according to an acceptable success confidence.>