Multi-person pose forecasting has garnered increasing attention owing to its critical role in applications such as human-robotic interaction and autonomous systems. Existing methods have achieved notable progress by integrating interaction-aware mechanisms to capture relational dynamics among individuals. However, these approaches often suffer from two key limitations: (1) the implicit assumption that all individuals within a scene are socially relevant, and (2) the uniform treatment of all body joints, regardless of their activity levels or importance in motion representation. To address these challenges, we propose a novel Transformer-based architecture, termed AsymFormer, which is designed to asymmetrically model both human-level interactions and joint-level dynamics. At the human level, we introduce a Selective Interaction-Aware Module (SIAM), which leverages a discriminator pre-trained on ground-truth interaction labels to evaluate the social relevance of each individual based on their historical motion trajectories. This design enables the model to explicitly focus on meaningful interactions while filtering out socially irrelevant agents. At the joint level, we develop a Targeted Joints Dynamic Encoder (TJDE) to emphasize high-activity joints, thereby avoiding the dilution of informative motion signals caused by the equal treatment of all joints. This selective focus enhances the discriminative power of motion representations. Extensive experiments conducted on multiple benchmark datasets demonstrate that our method significantly outperforms state-of-the-art approaches in multi-person 3D pose forecasting.

The partial domain adaptation (PDA) challenge is a prevalent issue in industrial fault diagnosis. Current PDA approaches primarily rely on adversarial learning for domain adaptation and use reweighting strategies to exclude source samples deemed outliers. However, the transferability of features diminishes from general feature extraction layers to higher task-specific layers in adversarial learning-based adaptation modules, leading to significant negative transfer in PDA settings. We term this issue the adaptation-discrimination paradox (ADP). Furthermore, reweighting strategies often suffer from unreliable pseudo-labels, compromising their effectiveness. In this work, we propose a novel PDA framework called Interactive Residual Domain Adaptation Networks (IRDAN), which introduces domain-wise models for each domain to provide a new perspective for the PDA challenge. Each domain-wise model is equipped with a residual domain adaptation (RDA) block to preserve the discriminative structure of each domain and mitigate the ADP. Additionally, we introduce a confident information flow via an interactive learning strategy, training the modules of IRDAN sequentially to avoid cross-interference. We also establish a reliable stopping criterion for selecting the best-performing model, ensuring practical usability in real-world applications. Experiments have demonstrated the superior performance of the proposed IRDAN.

This article investigates the differentially private leader-following consensus control (DPLFCC) problem for multiagent systems (MASs) operating on two-time scales. A new co-design framework with a private preserving scheme and a consensus controller is constructed by building a unique time-scale-dependent Lyapunov function. To achieve the ultimate mean-square leader-following consensus while maintaining differential privacy, the proposed strategy establishes a new distributed consensus controller with noise control for each follower. The initial state of the follower can be made more private by adjusting the noise control gain. It should be pointed out that controller-solving criteria and privacy level performances are designed depending on the time-scale parameter, thereby eliminating the numerical stiffness caused by the two-time-scale property. Furthermore, the results are extended to the leader's privacy-preserving situation. Finally, the effectiveness of the developed algorithm is illustrated by numerical simulation examples.

The inherent hysteresis nonlinearity in the magnetostrictive actuators (MAs) restricts their application in high-precision positioning systems. While direct inverse compensation has been widely used to mitigate hysteresis effects, it suffers from complexities in computing the inverse model, and addressing the residual modeling and compensation errors. In this respect, this paper introduces a composite control strategy to enhance the control performance of magnetostrictive actuated positioning system (MAPS) by presenting a new inverse-based compensator for the hysteresis. The key innovation lies in the derivation of an analytical expression for the inverse compensation error based on a generalized Prandtl-Ishlinskii (GPI) model, and the design of an unknown system dynamics estimator (USDE) to estimate this residual error. The USDE has a simple structure and easy parameter tuning, and can be trivially integrated into the closed-loop control system. Furthermore, a fast reaching law (FRL) is developed to design a composite sliding mode control (SMC), which uses both the inverse-based compensator and the USDE. This control with the FRL can achieve both the enhanced control response and the reduced chattering, compared with the standard SMC techniques. Finally, the effectiveness of the proposed approaches is confirmed by comparative simulations and experiments.

This article proposes a secure recursive estimator-based command filtered event-triggered control (EBCFETC) scheme for cyber-physical islanded AC microgrids (MGs) with unknown nonlinear loads under deception attacks. A discrete-time dynamic model of the islanded AC MG is given, and the voltage regulation problem is transformed into an output feedback tracking control issue. First, a secure recursive extended state estimator is designed to handle unknown nonlinear loads and estimate the immeasurable MG states under deception attacks. In particular, an upper bound on the estimation error covariance of the recursive estimator is obtained, and the real-time gain matrix is derived from the upper bound. Then, an EBCFETC strategy is developed by utilizing the backstepping technique, and an event-triggered mechanism is introduced to reduce the transmission frequency of control signals. The scheme guarantees that both the estimation error and the signals of the closed-loop system are bounded, and the tracking error converges to a small neighbourhood near zero. Finally, the simulation results verify the validity of the proposed EBCFETC method.

In the application of multimotor driving systems, achieving high control performance while reducing the occupation of communication networks holds practical significance. This article proposes an event-triggered fixed-time command filtered control strategy that not only facilitates load tracking and motor synchronization but also conserves communication resources. In tracking control design, the fixed-time control is incorporated into the backstepping procedure to accelerate convergence rate and improve tracking precision. Command filters are utilized to reduce computational complexity, and a compensation mechanism with double powers is constructed to eliminate filtering errors. In synchronization control design, a practical grouping control approach tailored for four motors is proposed, with control inputs superimposed on current commands to quickly achieve speed synchronization and reduce mechanical abrasion. Subsequently, an event-triggered mechanism is designed for the final composite control signal, which greatly conserves communication resources while maintaining desirable performance. The closed-loop system is proven to be practical, fixed-time stable, and free of Zeno behavior. Experiments conducted on a four-motor driving turntable demonstrate the effectiveness of the proposed strategy.

Taking into consideration the issue of the quadrotor unmanned aircraft robots (UARs) actuated by motors with hysteresis input, this research presents an adaptive dynamic implicit inverse control technique based on neural networks to achieve the desired trajectories. The following summarizes the primary technologies: 1) the hysteresis effect in UARs has been considered and eliminated by the proposed implicit inverse algorithms, which means a searching method for acquiring the real control signals is designed resulting in selecting to avoid constructing the hysteresis direct inverse model; 2) precise tracking is accomplished by designing an adaptive dynamic surface control (DSC) technology with enhanced state observer under the constraint that only the position data is available. In the meanwhile, the$L_{\infty }$performance can be obtained by selecting the suitable parameters; and 3) the underactuated Drone platform has been constructed as well as the control results have implemented to confirm that the successful application of the proposed implicit inverse control algorithms.

This article studies the bumplessH∞control issue for a class of continuous-time switched linear systems with mode-dependent average dwell time (MDADT) switching. A new two-step approach is proposed to suppress the control input bumps. First, the predesigned stabilizing controller is acquired via imposing the common-matrix-based bump limitation constraints. Second, the transition bumpless transfer (BT) controller is designed to be activated at the subsystem switching instants and combined with the stabilizing controller to constitute the transition-dependent piecewise BT controller. By applying the new transition-dependent piecewise Lyapunov function and the control amplitude limitation strategy, sufficient conditions are derived for the existence of a piecewise BT controller under MDADT switching. Finally, a tunnel diode circuit system is provided to highlight the feasibility and superiority of the developed BT control method.

This paper concerns with the fault estimation problem for a class of unmanned marine vehicles with sensor faults and non-differentiable actuator faults. Firstly, the presented unmanned marine vehicles are modeled as a class of continuous-time switched systems. In order to resolve the non-differentiable actuator faults, two kinds of learning observer methods are proposed to allow the augmented systems independent of the time-derivative of actuator faults. The first one is a kind of timeline-based learning observer, which produces the satisfactory estimation results by using the estimated information from the previous moment and the measured output estimation error information. The second one is an iterative learning observer, where the actuator faults are accurately estimated by the iterative process. The monotonic convergence of the iterative process is guaranteed based on the derived linear matrix inequality conditions. Under arbitrary switching signals, the simultaneous estimations of states, faults and disturbances can be achieved via the proposed observer design approaches. Finally, simulation results are provided to illustrate the effectiveness of the developed two kinds of learning observers.Note to Practitioners—The mass of unmanned marine vehicles (MVs) is switched back and forth as the unmanned MV performs the tasks, such as loading, dispatching and retrieving detectors. Hence, in order to better describe the dynamic behaviour, the unmanned MVs are described as the mass-switched systems. In general, any actual systems with switching properties can be characterised as the switched systems. At present, the fault estimation problem of the mass-switched unmanned MVs has not yet been investigated to date. This paper presents a theoretical study. Existing approaches to fault estimation of switched systems generally require differentiable actuator faults, which may be difficult to guarantee. In order to relieve this restriction, this paper proposes the new learning observer-based estimation method for switched linear systems. Based on the physical data of the unmanned MV, simulation results show that the proposed method is feasible.

This article proposes fuzzy-logic systems (FLSs)-based decentralized adaptive implicit inverse control scheme for a class of large-scale nonlinear systems with time delays and multihysteretic loops. Our novel algorithms feature hysteretic implicit inverse compensators designed to effectively mitigate multihysteretic loops in large-scale systems. In this article, hysteretic implicit inverse compensators can replace the traditional hysteretic inverse models, which are exceedingly difficult to construct, and no longer necessary. The authors provide three contributions: 1) a searching mechanism to obtain the approximate value of the practical input signal from the so-called hysteretic temporary control law; 2) the arbitrarily small$\mathit{L}_{\infty}$norm of the tracking error attained by utilizing the proposed initializing technique, which applies the combination of FLSs and a finite covering lemma to deal with time delays; and 3) the construction of a triple-axis giant magnetostrictive motion control platform, which validates the effectiveness of the proposed control scheme and algorithms.

This article focuses on the finite-time formation control problem for multiple quadrotor unmanned aerial vehicles (QUAVs) with malicious attacks, and presents a finite-time fuzzy adaptive output-feedback control scheme. First, the positional and angular velocities are estimated by developing the fuzzy state observer to replace actual values for controller design. Second, the problem of “computational complexity” is avoided and the effect of filtered error is eliminated by introducing the finite-time command filtered technique and constructing the error compensation mechanism, respectively. Meanwhile, the adaptive parameters are used to estimate the boundaries of malicious attack signals, overcoming the challenge of requiring bounds for attack signals in the backstepping design process. Based on the finite-time stability theory, it is proven that all signals are bounded in the multiple QUAVs system, and the formation tracking errors can converge to a sufficiently small neighborhood near the origin in a finite time. Finally, the validity of the algorithm is verified by a simulation example.

In this article, based upon reinforcement learning (RL) and reduced control techniques, an${H}_{\infty }$output tracking control method is represented for nonlinear two-time-scale industrial systems with external disturbances and unknown dynamics. First, the original${H}_{\infty }$output tracking problem is transformed into a reduced problem of the augmented error system. Based on zero-sum game idea, the Nash equilibrium solution is given and the tracking Hamilton–Jacobi–Isaacs (HJI) equation is established. Then, to handle the issue of unmeasurable states of the virtual reduced system, full-order system state data are collected to reconstruct the reduced system states, and the model-free RL algorithm is proposed to solve the tracking HJI equation. Next, the algorithm implementation is given under the actor–critic–disturbance framework. It is proved that the control policy obtained from reconstructed state data can make the augmented error system asymptotically stable and satisfy theL$_{\mathbf{2}}$gain condition. Finally, the effectiveness of the proposed method is illustrated by the permanent-magnet synchronous motor experiment.

This article investigates the issue of security control for stochastic Markov jump cyber-physical systems (SMJCPS) against actuator failures (AF), randomly occurring injection attacks (ROIA), and inaccessible states by virtue of state estimator-based adaptive sliding mode control (SMC) strategy. The knowledge of the states is generated with an estimator not requesting any input information from which a novel switching surface of linear type (SSL) is established. Then, an adaptive SMC input is developed to ensure the attainability of the SSL in limited steps, almost surely under stochastic noise, unknown ROIA, and potential AF. In the light of the arrival of the SSL and stochastic stability theory, a new stochastically stable criterion for the target SMJCPS operating on the defined SSL is deducted in the occurrence of AF, ROIA, and more generally uncertain transition rates. At last, a simulation study is performed, in which the raised control scheme is realized and certified by a tunnel diode circuit model.

This article proposes an adaptive fuzzy neural network (NN) command filtered impedance control for constrained robotic manipulators with disturbance observers. First, barrier Lyapunov functions are introduced to handle the full-state constraints. Second, the adaptive fuzzy NN is introduced to handle the unknown system dynamics and a disturbance observer is designed to eliminate the effect of unknown bound disturbance. Then, a modified auxiliary system is designed to suppress the input saturation effect. In addition, the command filtered technique and error compensation mechanism are used to directly obtain the derivative of the virtual control law and improve the control accuracy. The barrier Lyapunov theory is used to prove that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded. Finally, simulation studies are performed to illustrate the effectiveness of the proposed control method.

This brief is concerned with neural network (NN)-based adaptive finite-time output feedback attitude tracking control for rigid spacecraft in the presence of actuator saturation, inertial uncertainty, and external disturbance. First, a neural state observer is designed to estimate the unknown state. Then, based on the estimated state, the adaptive neural finite-time command filtered backstepping (CFB) is applied to construct virtual control signal and controller with updating law. The finite-time command filter is given to avoid the computation complexity problem in traditional backstepping, and the compensation signals based on fractional power are constructed to remove filtering errors. Using Lyapunov stability theory, we show that the attitude tracking error (TE) can converge into the desired neighborhood of the origin in finite time and all the signals in the closed-loop system are bounded in finite time although input saturation exists. The numerical simulations are used to show the effectiveness of the given algorithm.

In this article, an adaptive neural piecewise implicit inverse control strategy is proposed to effectively compensate for butterfly hysteresis effectively. First, a new butterfly Krasnoselskii–Pokrovskii (BKP) model is developed for the double-loop butterfly hysteresis characteristics. Second, an adaptive neural piecewise implicit inverse control strategy is designed to mitigate the butterfly-like hysteresis without constructing its analytical inverse model. Finally, experimental results on the dielectric elastomer actuator (DEA) motion control platform demonstrate the effectiveness of the adaptive neural piecewise implicit inverse control strategy.

Hysteresis is a complex nonlinear effect in smart materials-based actuators, which degrades the positioning performance of the actuator, especially when the hysteresis shows asymmetric characteristics. In order to mitigate the asymmetric hysteresis effect, an adaptive neural digital dynamic surface control (DSC) scheme with the implicit inverse compensator is developed in this article. The implicit inverse compensator for the purpose of compensating for the hysteresis effect is applied to find the compensation signal by searching the optimal control laws from the hysteresis output, which avoids the construction of the inverse hysteresis model. The adaptive neural digital controller is achieved by using a discrete-time neural network controller to realize the discretization of time and quantizing the control signal to realize the discretization of the amplitude. The adaptive neural digital controller ensures the semiglobally uniformly ultimately bounded (SUUB) of all signals in the closed-loop control system. The effectiveness of the proposed approach is validated via the magnetostrictive-actuated system.

This article aims to realize a precise position and attitude tracking control for the quadrotor using a proposed fuzzy approximator-based compound adaptive fuzzy quantized control scheme. In the control scheme, a quantized output-feedback control for position tracking and a state-feedback quantized control for attitude trajectory tracking are combined to deal with the underactuated and strong coupling problems of the quadrotor. The main contributions are: 1) the adaptive fuzzy quantized control is realized, then the strong nonlinearities caused by the quantizer are effectively mitigated, which implies that the control precision can be improved when a low communication rate is required in the real-time control system of quadrotor; 2) by applying the adaptive fuzzy dynamic surface control (DSC) technique to the underactuated quadrotor control system, the “explosion of complexity” problem in the backstepping method is overcome and the L∞tracking performance is achieved with the proposed initializing technique inspired by Zhang et al. This guarantees that the attitude signals promptly converge to the desired trajectories, then the underactuated problem of the quadrotor is overcome by solving the designed adaptive fuzzy-quantized control equations; and 3) the experiments on the platform of the Quanser Qball-X4 quadrotor are conducted and the effectiveness of the proposed control scheme is validated.

This article presents a new switching control scheme for controlling a class of nonlinear discrete-time dynamical systems. The key idea behind the proposed control techniques lies in the decomposition of unmodeled dynamics, that is, the unmodelled dynamics are decomposed as a sum of a known function depending on the data from the posterior unmodeled dynamics measurement and an unknown increment. The control algorithm is based on a novel estimation algorithm for the increment of unmodeled dynamics, which contributes two nonlinear controllers. The theoretical results on both convergence and stability of the closed-loop system are given. The system performance is evaluated by some simulation results.

A plant is controlled remotely on a network for a networked control system. Disturbances from the network and surroundings may deteriorate the control performance of such a system. To solve this problem, this paper presents a new method of suppressing an exogenous disturbance for those control systems. The control system has a state observer to estimate the state of the plant and an equivalent-input-disturbance (EID) estimator to produce an estimate of the disturbance on the control input channel in a real-time fashion. The system is divided into two subsystems for the analysis of system stability. A stability condition of the control system with a time-varying delay is presented in terms of a linear matrix inequality. Simulations demonstrate the validity of the method. And a comparative study shows the superior of our method to a conventional Smith-EID control method.

Focusing on the piezoelectric positioning stage, this paper proposes an adaptive estimated inverse output-feedback quantized control scheme. First, the quantized issue due to the use of computer is addressed by introducing a linear time-varying quantizer model where the quantizer parameters can be estimated on-line. Second, by using the fuzzy approximator, the developed controller can avoid the identification of the parameters in the piezoelectric positioning stage. Third, by constructing the estimated inverse compensator of the hysteresis, the hysteresis nonlinearities in the piezoelectric actuator are mitigated; Fourth, the states observer is designed to avoid the measurements of the velocity and acceleration signals. The analysis of stability shows all the signals in the piezoelectric positioning stage are uniformly ultimately bounded and the prespecified tracking performance of the quantized control system is achieved by employing the error transformed function. Finally, a computer controlled experiments for the piezoelectric positioning stage is conducted to show the effectiveness of the proposed quantized controller.

This paper proposes a decentralized neural adaptive dynamic surface approximated inverse control (DNADSAIC) scheme for a class of large-scale time-delay systems with hysteresis nonlinearities as input. The decentralized control problem under the case only the outputs are measurable is solved by utilizing the radial basis function neural networks approximator and the hysteresis approximated inverse compensator. Also, with the help of finite covering lemma, the traditional Krasovskii functionals are dropped when coping with the delays, leading to the removal of the assumptions on the functions with time-delay states and the acquisition of the arbitrarily small L∞tracking performance of each hysteretic subsystem with time delays. The analysis of stabilities guarantees all the signals of the closed-loop systems are semiglobally uniformly ultimately bounded. Simulation results illustrate the efficiency of the proposed DNADSAIC scheme.

Discrete-time adaptive control for ionic polymer-metal composite (IPMC) actuator is studied in this paper. First, a new mathematical model in discrete-time domain is proposed for IPMC actuator. Then, based on the obtained model, a discrete adaptive control law is synthesized for IPMC actuators. The proposed discrete adaptive controller can guarantee the global stability of the closed-loop system, and the position tracking error of the IPMC actuator can be controlled by the design parameters. Finally, the proposed model and control law are verified by IPMC actuator experiments.

Complex industrial processes are multivariable and generally exhibit strong coupling among their control loops with heavy nonlinear nature. These make it very difficult to obtain an accurate model. As a result, the conventional and data-driven control methods are difficult to apply. Using a twin-tank level control system as an example, a novel multivariable decoupling control algorithm with adaptive neural-fuzzy inference system (ANFIS)-based unmodeled dynamics (UD) compensation is proposed in this paper for a class of complex industrial processes. At first, a nonlinear multivariable decoupling controller with UD compensation is introduced. Different from the existing methods, the decomposition estimation algorithm using ANFIS is employed to estimate the UD, and the desired estimating and decoupling control effects are achieved. Second, the proposed method does not require the complicated switching mechanism which has been commonly used in the literature. This significantly simplifies the obtained decoupling algorithm and its realization. Third, based on some new lemmas and theorems, the conditions on the stability and convergence of the closed-loop system are analyzed to show the uniform boundedness of all the variables. This is then followed by the summary on experimental tests on a heavily coupled nonlinear twin-tank system that demonstrates the effectiveness and the practicability of the proposed method.

For a class of complex industrial processes with nonlinear, strongly coupled multivariable properties, a new multivariable decoupling design framework which based on the concepts of virtual unmodeled dynamics (VUD) and lower order linear models is proposed in this paper. First, a self-tuning multivariable decoupling controller is constructed based on a lower order model. Then based on the compensator of the VUD, a nonlinear multivariable decoupling controller is designed, where a decomposition estimation algorithm is employed for modeling the VUD. In our proposed scheme, it solves the problem that the current input signal is embedded in the VUD and the true input data vector used by the learner model is difficult to be obtained in time. The linear and nonlinear decoupling controllers are integrated by an adaptive switching control algorithm to take advantage of their complementary features. Finally, the stability and convergence of the proposed algorithm is analyzed. Experimental tests on a heavily coupled nonlinear twin-tank system are carried out to demonstrate the effectiveness and the practicability of the proposed method.

In this paper, high precision motion control of piezo-actuated stage is discussed. In order to cope with the nonlinear characteristic of the piezoelectric actuator (PEA) and get high tracking performance, this paper proposes a new approach to design the discrete time sliding mode control (DSMC) with an ability to maintain the tracking error in a known region described by a performance function. The effectiveness of the proposed method is verified by experiments. The results show that the system not only performs well with complicated desired trajectories but also robust against external disturbances.

Magnetostrictive actuators featuring high energy densities, large strokes, and fast responses are playing an increasingly important role in micro/nano-positioning applications. However, such actuators with different input frequencies and mechanical loads exhibit complex dynamics and hysteretic behaviors, posing a great challenge on applications of the actuators. Therefore, it is important to develop a dynamic model that can characterize dynamic behaviors of the actuators, including current-magnetic flux nonlinear hysteresis, frequency responses, and loading effects, simultaneously. To this end, a comprehensive model, which thoroughly considers the electric, magnetic, and mechanical domain, as well as the interactions among them, is developed in this paper. To validate the developed model, the parameters of the model are identified where the hysteresis of the magnetostrictive actuator is described, as an illustration, by the asymmetric shifted Prandtl-Ishlinskii model. The experimental results demonstrate that the comprehensive model presents an excellent agreement with dynamic behaviors of the magnetostrictive actuator.

This paper discusses the modeling and control of the ionic polymer-metal composite (IPMC) actuators which have many promising applications in biomechatronics. A novel mathematical model in continuous-time domain of the IPMC actuator, being a stable second-order dynamical system preceded by a nonlinear hysteresis representation, is proposed. An adaptive controller is formulated for the IPMC actuator based on the proposed model. The proposed adaptive control law ensures the global stability of the controlled IPMC system, and the position error of the IPMC actuator can be theoretically guaranteed to converge to zero. The effectiveness of the proposed model and the superiority of the proposed control to the traditional proportional-integral-derivative control are verified by experimental results.

In this paper, the challenging issue of estimating virtual unmodeled dynamics is addressed. A novel estimation algorithm based on historical data and the output of low-order approximation models for virtual un-modeled dynamics is presented. In particular, the virtual un-modeled dynamics are decomposed into known and unknown parts, where only the unknown part is to be estimated. The method effectively avoids the need to use the unknown control input directly, and enables the estimation of the un-modeled dynamics with a relatively simple algorithm. Moreover, it is shown that the proposed algorithm overcomes the difficulty in obtaining the control solutions caused by the fact that the controller input is embedded in un-modeled dynamics. Finally, simulation studies are presented to demonstrate the effectiveness of the proposed method.

By representing nonlinear systems as a combination of linear part and unmodeled dynamics, in this paper, an improved estimation algorithm using an adaptive neuro-fuzzy inference system (ANFIS) for unmodeled dynamics is presented. At first, the unmodeled dynamics is divided into two parts using the differential expansion of the control input at the last time instant; then, the two parts are estimated by the ANFIS. It has been shown that the proposed algorithm overcomes the problem that the unknown control input is embedded in unmodeled dynamics, which makes the true value of unmodeled dynamics difficult obtain. Moreover, the method improves the precision of the estimation of unmodeled dynamics. Second, under the assumption that the growth rate of unmodeled dynamics does not exceed its input vector, the “one-to-one mapping” and “regularization technique” are adopted to deal with the input and output data and the unmodeled dynamics, respectively. As a result, the data vector can be guaranteed to lie inside a compact set, which ensures the use of the universal approximation property of the ANFIS. On the other hand, it has been shown that datum of a system can be fully used to obtain the parameters (centers, widths) in membership functions and the network connection weights in the ANFIS by offline training. These parameters are tuned online to improve the estimation convergence rate of the unmodeled dynamics. The effectiveness of the proposed estimation method is illustrated by comparing it with the simulation results that are obtained from the other existing methods. Finally, the proposed estimation method is applied to the nonlinear switching control design. Both simulation and theoretical analysis have confirmed that the nonlinear switching control which adopts the proposed estimation method cannot only guarantee the stability and convergence of the system but can exhibit a desired dynamic performance for the closed-loop system as well.

In this paper, a novel knowledge-based global operation approach is proposed to minimize the effect on the production performance caused by unexpected variations in the operation of a mineral processing plant subjected to uncertainties. For this purpose, a feedback compensation and adaptation signal discovered from process operational data is employed to construct a closed-loop dynamic operation strategy. It uses the signal to regulate the outputs of the existing open-loop and steady-state based system so as to compensate the uncertainty in the steady-state operation at the plant-wide level. The utilization mechanism of operational data through constructing increment association rules is firstly described. Then, a rough set based rule extraction approach is developed to generate the compensation rules. This includes two steps, namely the determination of the variables to be compensated based on the significance of attributes in the rough set theory and the extraction of the compensation rules from process data. Based upon the operational data of the mineral processing plant, relevant rules are obtained. Both simulation and industrial experiments are carried out for the proposed global operation, where the effectiveness of the proposed approach has been clearly justified.

This paper presents a single chip VLSI architecture of wireless image sensor node, which is constituted by an enhanced embedded 8051 microcontroller, a CMOS camera interface and hardware accelerators. The algorithms and control flows of the IEEE 802.15.4 MAC layer are accelerated by hardware, results in 45% less code size compared with the conventional software stack. An innovated CFA preprocessing algorithm and JPEG-LS compressing method is adopted and implemented by hardware, which has a minimal 46.3dB PSNR, an average compression ratio of about 3.0bit/pixel and an approximately 5fps at 16MHz system clock. Furthermore, low power design and techniques are employed to extend battery life, resulting in 60mW max system power consumption when the SoC is in full working mode (i.e. processor, image processing and wireless communication are active simultaneously) in 0.18μm CMOS process.

This paper presents an energy-efficient implementation of a real-time on-demand MAC protocol for medical wireless body sensor network (WBSN). Medical WBSN is focused on pervasive healthcare and medical applications, such as monitoring vital signs, making basic drug delivery, etc. The sensor nodes in the heterogeneous WBSN generally require different data rates, due to their differing functions. Additionally, because of the strict resource constraints, the sensor nodes must be ultra-low-power. Thirdly, low-rate treatment-function nodes must also ldquowork-on-demandrdquo to prove proper activities in the slave nodes such as stimulus and drug delivery. These three requirements cannot currently be satisfied simultaneously in commonly-used single channel implementations because the channel monitoring consumes too much power for long-term use. In the proposed implementation, a secondary channel is introduced in, which is used for channel listening only. Benefiting from the secondary channel, the node can achieve both real-time ldquowork-on-demandrdquo and zero idle power, by means of recovering energy from the ldquodemand tokenrdquo. An elaborated energy-harvesting RF module achieves monitoring the secondary channel. The prototype system of sensor nodes is expected for the zero-idle-power and the response time of less than 2 ms.

By analyzing the features unfit for parallel computation and low power implementation, a VLSI architecture of JPEG-LS encoder for lossless image compression is proposed in this paper. It functionally consists of four parts: Mode decision module, clock controller, three linear parallel pipelines, and a two-tier data packer. Computations are organized in a fully pipelined style in these modules, so that real time data processing can be achieved. The clock management scheme with four interlaced clock domains and a dedicated clock controller is applied to ensure the bottleneck calculation, reduce the clock frequency on non-critical paths, and shut off the working clocks of idle modules, which reduces 15.7% of overall power consumption. The proposed JPEG-LS encoder with the features of low power and high processing speed, has been applied in a wireless endoscopy system.

A design of low power digital baseband for wireless endoscope capsule is presented. The key design issues involved in this IC are discussed, including implementation of communication protocol, real time image filter and real time JPEG-LS encoder. Power dissipation is lowered through the architectural exploration. The baseband has been implemented in 0.18 μm CMOS technology. Measurement results show that 50% power reduction is achieved when image compression is enabled @ 30 fps for VGA image at 1.2 V supply voltage compared with the situation when image compression is disabled, 11% power reduction is achieved compared with the previous research.

This paper proposes a new low complexity near-lossless image compression method and its VLSI design for low power and high frame rate in the wireless endoscopy capsule system. Assuring outstanding compression performance and high image quality, the proposed method with the features of low complexity, low storage overhead and real time data processing, makes it ideal for hardware implementation. The VLSI architecture consists of two pipelined parts: The preprocessor and the JPEG-LS engine. A fully pipelined VLSI structure with a dedicated clock management scheme is proposed for the JPEG-LS engine, which ensures a low power application, and real time data processing as well. The hardware implementation has been verified on FPGA and implemented in 0.18μm CMOS technology.

This paper presents a new method for near-lossless image compression for digital colorful image sensors with Bayer color filter arrays (CFA). In this method, the captured CFA raw data is first smoothed by low-pass filters followed by down-sampling and then compressed directly before full color interpolation which introduces redundancy. Assuring high image quality, the method can provide higher compression ratio and lower complexity than conventional image compression methods and other existing similar methods. The composite peak signal to noise ratio (CPSNR) of the decompressed image is larger than 51 dB.