This paper proposes a decentralized H∞sampled-data fuzzy filter design for nonlinear interconnected systems composed of multiple subsystems with uncertain interconnections and variable sampling intervals. The interconnected system and the decentralized filter are modeled using Takagi-Sugeno fuzzy systems. To derive the filter-design conditions, the estimation error dynamics are modeled, and the H∞filter performance inequality is defined. The filter performance inequality is aimed at minimizing the ratio of the estimation error to the sum of the norm of the disturbance and oscillating system. The sufficient conditions for the filter design are derived using the Wirtinger-based integral inequality and expressed in the form of linear matrix inequalities. Finally, the performance of the proposed filter-design techniques is demonstrated through two simulation examples.

Heterogeneous cellular networks (HCNs) where conventional macro cells are being overlaid with low-powered small cells and the millimeter wave (mmW) frequencies are considered as a way to efficiently enhance the spectral efficiency per area with the availability of huge bandwidths. However, how to efficiently manage small cells and mmW frequencies is still an open question. In this paper, a load balancing problem for 5G HCNs is discussed with different band operation modes and a joint user association (UA) scheme using a hybrid self-organizing network (SON) is suggested for a practical clustered HCN.

This paper presents an output-feedback exponential stabilization condition of sampled-data polynomial fuzzy control systems under variable sampling rates. Compared with previous work, the proposed method is less conservative because of the newly developed time-dependent fuzzy Lyapunov-Krasovskii functional that is based on the conventional fuzzy Lyapunov function. Moreover, the controller is allowed to contain polynomial gain matrices, thereby improving the control performance and design flexibility. This is realized by assuming the difference between the continuous- and discrete-time state vectors as time-varying norm-bounded uncertainties, which are manipulated using a robust control technique. A new sufficient condition is introduced to cast the stability condition containing the integral term as the sum-of-square conditions. Finally, the effectiveness of the proposed method is validated by simulations.

This paper considers a regulation problem of non-linear large-scale systems. To do this, a Takagi-Sugeno (T-S) fuzzy model is adopted for fuzzy modeling of the nonlinear large-scale systems, which has unknown interconnection terms. An output-feedback decentralized fuzzy controller with integral action is employed to drive the system outputs to reach a reference value and minimize the steady-state error. Sufficient conditions for the output regulation are derived from Lyapunov stability and these are formulated in terms of linear matrix inequalities (LMI). Finally, a numerical simulation example is given to demonstrate the effectiveness of the proposed design procedures and regulation conditions.

This paper presents the decentralized sampled-data fuzzy observer design techniques for nonlinear interconnected systems, which are assumed to be composed of fuzzy subsystems and unknown interconnections. To design the decentralized fuzzy observer, the estimation error dynamics is obtained, and the performance function is defined. Based on the estimation error dynamics and the performance function, the continuous-time decentralized fuzzy observer is first proposed to minimize the interconnection bound to attenuation degree ratio. In addition, the decentralized sampled-data fuzzy observers are designed by using the approximate discretization and the exact discrete-time design approaches, respectively. Each proposed observer design technique is formulated into the optimal problems with the linear matrix inequalities. Finally, several simulation examples show the validity and superiority of the proposed observer design techniques by comparing with the conventional techniques.

This paper focuses on a class of reinforcement learning (RL) algorithms, named integral RL (I-RL), that solve continuous-time (CT) nonlinear optimal control problems with input-affine system dynamics. First, we extend the concepts of exploration, integral temporal difference, and invariant admissibility to the target CT nonlinear system that is governed by a control policy plus a probing signal called an exploration. Then, we show input-to-state stability (ISS) and invariant admissibility of the closed-loop systems with the policies generated by integral policy iteration (I-PI) or invariantly admissible PI (IA-PI) method. Based on these, three online I-RL algorithms named explorized I-PI and integral Q -learning I, II are proposed, all of which generate the same convergent sequences as I-PI and IA-PI under the required excitation condition on the exploration. All the proposed methods are partially or completely model free, and can simultaneously explore the state space in a stable manner during the online learning processes. ISS, invariant admissibility, and convergence properties of the proposed methods are also investigated, and related with these, we show the design principles of the exploration for safe learning. Neural-network-based implementation methods for the proposed schemes are also presented in this paper. Finally, several numerical simulations are carried out to verify the effectiveness of the proposed methods.

In this study, the authors propose a blind spot reduction method in wavelet transform‐based time–frequency domain reflectometry (WTFDR) by using the Gaussian chirp as the mother wavelet. The blind spot is one of the intrinsic weak points in reflectometry and it means the overlapping ranges of the reference and the reflected signals when the fault is generated at a close distance. Owing to the blind spot, it is difficult to localise the close range fault. Thus, many researchers study the blind spot which is generated in various cables such as electric cable in flight, network cable and power cable. In this study, two methods are used to reduce the blind spot. Firstly, by using the linearity of a complex wavelet transform, the overlapped reference signal at the measured signal is separated and the blind spot is reduced by obtaining the difference of the moduli of the wavelet coefficients for the reference and the reflected signals. Secondly, by using the Gaussian chirp as the mother wavelet, which is designed by considering the characteristics of the cable, the wavelet analysis and the resolution of the WTFDR are improved. Finally, the computer simulations and the real experiments are performed to confirm the effectiveness and the accuracy of the proposed method.

This paper presents a decentralized fuzzy control problem for asymptotic stabilization of a class of nonlinear large-scale systems that use an observer-based output-feedback scheme. A Takagi-Sugeno (T-S) fuzzy model is adopted for the nonlinear large-scale system, which has unknown interconnection terms, and a fuzzy controller is separately considered for measurable and nonmeasurable premise variable cases. Sufficient conditions are derived for both asymptotic stabilization and optimization of a maximum bound of interconnection and are formulated in terms of linear matrix inequalities. Finally, numerical examples are provided to verify the effectiveness of the proposed techniques.

We consider two-way relay (TWR) networks consisting of two communicating nodes and multiple relays built upon orthogonal frequency-division multiplexing signaling. For low-cost implementation of relays, two pragmatic TWR protocols are proposed based on analog network coding (ANC). The proposed two-phase (2P)- and three-phase (3P)-ANC schemes employ time-domain instantaneous power scaling and relay selection to improve the outage performance without equipping discrete Fourier transform (DFT) and inverse DFT functions at the relay. In particular, we devise a relay power allocation factor and a resultant relay selection metric for the proposed 3P-ANC scheme, which can be implemented at low complexity. For the proposed schemes, the outage probability and achievable diversity order are also analyzed, which reveals a larger diversity order of the 3P-ANC scheme than the 2P-ANC scheme. The results from analysis and simulation show that the proposed schemes outperform the conventional 2P-ANC schemes employing time-domain average power scaling and distributed space-time block coding or frequency-domain power scaling and relay selection.

This paper presents the relaxed stability condition for the affine fuzzy system using the fuzzy Lyapunov function which is the Lyapunov function with the membership function. Based on Lyapunov-stability theory, the relaxed stability condition for the affine fuzzy system is derived and represented to linear matrix inequalities(LMIs) with added null product term. Slack matrix variables are considered to analyze further relaxed stability condition. Finally, a simulation example is given to illustrate the merits of the proposed methods.

This paper presents the novel and efficient warhead tracking filter based on probabilistic data association (PDA) scheme for FMCW radar seeker with anti-ballistic capability. In frequency domain, the warhead tracking problem can be cast into a difficult data-association problem because multiple and closely located frequency measurements are generated by scatterers of a ballistic missile and multi-path clutters. For enhancing the warhead detection and tracking performance, the proposed warhead tracking filter adopts the feature information about scatterers' appearance. The feature information indicates the possibility that the measured frequency measurement is originated from the scatterer in warhead section of ballistic missile. Utilizing the feature information, the proposed tracking filter is able to accurately separate the warhead frequency measurement from others. Moreover, for its simple structure, it is suitable for real-time implementation. Simulation results demonstrate the effectiveness and the reliability of the proposed solution over the conventional PDA based filter.

A target localisation estimator based on time difference of arrival (TDOA) measurement is proposed. The localisation estimator is designed in the framework of the recently developed robust least‐squares (RoLS) estimator, which provides an unbiased estimation result and can be implemented with a recursive filtering structure. However, when the RoLS estimator is applied to the localisation problem, its localisation performance depends on the knowledge of the stochastic information of the TDOA measurement. This dependency means that incorrectly given information causes localisation error. Therefore to complement the dependency of the given stochastic information of the TDOA measurement, we design a compensation procedure based on the constraints on the state variables of the estimator. The performance of the proposition under several cases of incorrectly given stochastic information is verified through computer simulation, and its filtering structure is compared with other existing localisation algorithms mathematically. In addition, the entire process of the proposed localisation estimator is derived as a recursive form for real‐time applications.

In this paper, we propose a 6-step scheme for efficient CoMP scheme selection and radio resource management. Simulations show that the proposed 6-step scheme can greatly improve user average throughput compared to a conventional 4-step scheme.

This paper concerns a new stability condition for polynomial fuzzy system by introducing polynomial fuzzy Lyapunov function and slack matrix. Stability condition for polynomial fuzzy system is usually derived by common quadratic polynomial Lyapunov function. However this method results the conservative problem. To relax the conservative result, various relaxing methods which are introduced in T-S fuzzy system case are converted to polynomial fuzzy system. With this line, this paper adapts fuzzy Lyapunov function and extends it to polynomial fuzzy Lyapunov function. It is the extended version of existing fuzzy Lyapunov function which is replaced constant matrices with polynomial one. Furthermore, by adding the slack matrix to the proposed stability condition, this condition is further relaxed. Finally, these results will be proved by numerical example.

This paper presents a decentralized fuzzy controller design for the stochastic stabilization of the large-scale fuzzy systems with packet losses. Sufficient conditions are derived for the stochastic stabilization of the closed-loop system. Using the cost function, the fuzzy guaranteed cost control is defined and minimization of a given cost function is formulated in terms of linear matrix inequalities. Finally, a numerical example is provided to verify the effectiveness of the proposed technique.

This paper presents an intelligent tracking method for nonlinear maneuvering target by compartmentalizing external noises of 3D maneuvering target. Proposed method makes the filter recognize the maneuvering target as linear one by separating acceleration properly from the overhaul noises. For achieving that, we use the particle swam optimization-fuzzy c-means (PSO-FCM) clustering as the criteria of methodology. The positional difference between measured point and predicted one is separated into acceleration and noise. Compartmentalized external noises plays the role of acceleration in accordance with assigned position and its quantity. Proposed algorithm makes approximated acceleration to be compensated and approximated noise is filtered by Kalman filter (KF). To depict the real maneuvering target and track the target with unlimited condition, we handle 3D dynamic model. Finally, some examples are provided to show the effectiveness of the proposed algorithm.

This paper focuses on the integral reinforcement learning (I-RL) for input-affine continuous-time (CT) nonlinear systems where a known time-varying signal called an exploration is injected through the control input. First, we propose a modified I-RL method which effectively eliminates the effects of the explorations on the algorithm. Next, based on the result, an actor-critic I-RL technique is presented for the same nonlinear systems with completely unknown dynamics. Finally, the least-squares implementation method with the exact parameterizations is presented for each proposed one which can be solved under the given persistently exciting (PE) conditions. A simulation example is given to verify the effectiveness of the proposed methods.

In this paper, we propose a control method for stable grasp of dual-fingered robots with rigid hemispherical fingertips. The main contribution of the proposed approach is to lower the probability of the object slipping out of the fingertips as optimizing the angle of the grasping force. In addition, since the controller is allowed to use the tactile sensor, the shape of an object is not limited, as long as the contact point is positioned on the vicinity of smooth curvatures. We prove the stability analysis of the proposed controller via the Lyapunov direct method. Finally, we present simulation results to validate the effectiveness of the proposed approach.

In this paper, a novel digital controller is proposed for the nonlinear systems with packet loss using the intelligent digital redesign (IDR). For the fuzzy controller, the nonlinear system is represented by a Takagi-Sugeno (T-S) fuzzy model. The IDR technique is to convert a pre-designed analog controller into an equivalent digital controller. For this technique, the discretized models of the analog and digital closed-loop system are presented, respectively. The digital control gain is obtained to minimize the norm error between the state of the analog and digital closed-loop systems and stabilize the digital closed-loop system. Its sufficient conditions are derived in terms of linear matrix inequalities (LMIs). Finally, a numerical example is provided to verify the effectiveness of the proposed technique.

This paper is concerned with the mixed-time fuzzy optimal estimator for the target tracking. The proposed method takes account of behaviour for target motion in continuous time and system measurement in discrete time. The overall dynamic of system in continuous-time is discretised by using Takagi-Sugeno (T-S) fuzzy models. Based on the fuzzy model the fuzzy estimation is studied. Then the error state of the filtering process is means square bounded. A basis dependent Lyapunov function approach is developed to design the fuzzy filter and a filter gain is minimized by using some linear matrix inequalities (LMIs) constraints. A numerical example is provided to demonstrate various aspects of theoretical results.

This paper is concerned with exploring an extended approach for the stability analysis and synthesis for Markovian jump nonlinear systems (MJNLSs) with incomplete transition descriptions via fuzzy control. In this paper, not all the elements of the rate transition matrices (RTMs) are assumed to be known. By fully considering the properties of the RTMs and the convexity of the uncertain domains, sufficient criteria of stability and stabilization are obtained. The proposed stability conditions are much less conservative than most of the existing results and stabilization conditions with a mode-dependent fuzzy controller are derived for Markovian jump fuzzy systems (MJFSs) in terms of linear matrix inequalities (LMIs). Finally, illustrative numerical examples are provided to demonstrate the effectiveness of the proposed approach.

This paper analyzes a two-way relaying (TWR) system where two sources with multiple antennas exchange data via an amplify-and-forward relay with a single antenna. In the system considered herein, the link quality between the sources and relay can generally be asymmetric due to the nonidentical antenna configuration and relay location. In such a general setup, an accurate upper-bound on the average sum rate (ASR) is derived when beamforming is employed at the sources. We show that the proposed bound is much closer to the exact ASR than the conventional bound under various system configurations. It is also observed that relay location is more important than relay power allocation when the numbers of source antennas are unequal.

This paper provides a low-complexity scheme for peak-to-average power ratio (PAPR) reduction without side information for frequency switched transmit diversity orthogonal frequency division multiplexing (FSTD-OFDM). The proposed scheme generates multiple signal candidates (SCs) by dividing parallel data symbols into subparts and mapping the subparts onto the antennas under a criterion reducing the correlation between the SCs to select one SC with minimum PAPR. The proposed scheme is shown to provide reasonably good PAPR performance at a lower complexity than the conventional schemes. In addition, the proposed scheme can detect the transmitted SC via energy detection without side information.

The recently developedk-samples variation approach is known as a powerful way to reduce the conservativeness of existing stability and stabilization conditions for discrete-time Takagi-Sugeno (T-S) fuzzy systems. In this approach, the Lyapunov functions under consideration are not necessarily decreasing at every sample but are allowed to decrease everyksamples, which is evidently less restrictive than classical approaches. Consequently, less-conservative linear-matrix-inequality (LMI) conditions were derived. In addition, it was proved that, for two positive integersk1andk2, if the condition fork=k1is fulfilled, then those corresponding tok=k2are also satisfied whenk2is the divisor ofk1. In this letter, we prove that, if the condition fork=k2admits a solution, then those corresponding to anyk>;k2are also solvable.

This paper presents a new fuzzy Lyapunov function (FLF) for the stability analysis of continuous-time Takagi-Sugeno (T-S) fuzzy systems. Unlike conventional FLFs, the proposed one depends not only on the fuzzy weighting functions of the T-S fuzzy systems but on their first-order time derivatives as well. Based on the proposed FLF, a sufficient stability condition is derived in the form of linear matrix inequalities, depending on the upper bounds on the second-order time derivative of the fuzzy weighting functions, as well as the first-order ones. Finally, some examples demonstrate that the proposed condition can provide less conservative results than the previous ones in the literature.

We present a leader-follower-based adaptive formation control method for electrically driven nonholonomic mobile robots with limited information. First, an adaptive observer is developed under the condition that the velocity measurement is not available. With the proposed adaptive observer, the formation control part is designed to achieve the desired formation and guarantee the collision avoidance. In addition, neural network is employed to compensate the actuator saturation, and the projection algorithm is used to estimate the velocity information of the leader. It is shown, by using the Lyapunov theory, that all errors of the closed-loop system are uniformly ultimately bounded. Simulation results are presented to illustrate the performance of the proposed control system.

In this paper, stability of continuous-time Takagi-Sugeno (T-S) fuzzy systems is investigated by means of a new fuzzy Lyapunov function which depends on the first-order time-derivative of the fuzzy weighting functions (TDFWF). As a result, both the first and second-order TDFWF appear in the Lyapunov inequality and the stability condition. The additional dependence on the second-order TDFWF makes it possible to reduce the conservativeness of the previous result. The stability condition is formulated in terms of linear matrix inequalities (LMIs). The effectiveness of the proposed analysis approach is demonstrated by numerical examples.

This paper presents the relaxed nonquadratic stabilization conditions of discrete-time Takagi-Sugeno (T-S) fuzzy systems. To do this, we propose a new fuzzy controller and Lyapunov function by generalizing the nonparallel distributed compensation (non-PDC) control law and nonquadratic Lyapunov function, respectively. By exploiting Po¿lya's theorem and algebraic properties of a homogeneous polynomials of normalized fuzzy weighting functions, an infinite family of sufficient conditions for the asymptotic stabilizability is derived. These conditions are formulated in the format of linear matrix inequalities (LMIs) and, hence, are numerically tractable via convex programming techniques. Finally, an example is given to illustrate advantages of the proposed method.

This paper discusses the decentralized fuzzy control technique for the stochastic stability of nonlinear interconnected systems with missing measurements. We develop the fuzzy subsystem of the interconnected system by using the Takagi-Sugeno (T-S) fuzzy model and design the decentralized fuzzy controller for stochastic stability. To guarantee the sufficient condition of stability, we use Lyapunov stability theorem. Its constructive condition is expressed as the linear matrix inequality (LMI). An example is given to show the verification discussed throughout the paper.

This paper is an attempt to pursue the relaxed stabilization conditions for discrete-time Takagi-Sugeno (T-S) fuzzy systems. The main results given here concern their stabilization using extended non-quadratic Lyapunov functions, a non-parallel distributed compensations (non-PDC). New sufficient conditions in the terms of linear matrix inequalities (LMIs) have been proposed. The conditions are shown to be less conservative than some previous results in the literature. Two examples are given to illustrate the advantage of the proposed methods.

In this paper, the stability analysis and stabilization problem for a discrete-time Markovian jump nonlinear systems (MJLNS) with time-varying delays are investigated. The time-delay is considered to be time-varying and has a upper bound. The transition probabilities of the mode jumps are considered to be completely known. Sufficient conditions for stochastic stability of the Markovian jump fuzzy systems (MJFS) are derived via the linear matrix inequality (LMI) formulation, and the design of the stabilizing controller is further given. A numerical example is used to illustrate the developed theory.

In this paper, we concern an intelligent digital re-design (IDR) method for a fuzzy observer-based output-feedback control system which includes parametric uncertainties. The term IDR is to convert an existing analog control into an equivalent digital counterpart viastate-matching. The considered IDR problem is viewed as convex minimization problem of the norm distances between linear operators to be matched and its constructive condition is formulated in terms of linear matrix inequalities (LMIs). The main features of the proposed method are that the state estimation error in the plant dynamics is considered in the IDR condition that plays a crucial role in the performance improvement; the uncertainties in the plant dynamics is shown in the IDR condition by virtue of the bilinear and inverse-bilinear approximation method; finally, the stability property is preserved by the proposed IDR method.

This brief proposes a simple control approach for a class of uncertain nonlinear systems with unknown time delays in strict-feedback form. That is, the dynamic surface control technique, which can solve the "explosion of complexity" problem in the backstepping design procedure, is extended to nonlinear systems with unknown time delays. The unknown time-delay effects are removed by using appropriate Lyapunov-Krasovskii functionals, and the uncertain nonlinear terms generated by this procedure as well as model uncertainties are approximated by the function approximation technique using neural networks. In addition, the bounds of external disturbances are estimated by the adaptive technique. From the Lyapunov stability theorem, we prove that all signals in the closed-loop system are semiglobally uniformly bounded. Finally, we present simulation results to validate the effectiveness of the proposed approach.

A decentralized adaptive methodology is presented for large-scale nonlinear systems with model uncertainties and time-delayed interconnections unmatched in control inputs. The interaction terms with unknown time-varying delays are bounded by unknown nonlinear bounding functions related to all states and are compensated by choosing appropriate Lyapunov-Krasovskii functionals and using the function approximation technique based on neural networks. The proposed memoryless local controller for each subsystem can simply be designed by extending the dynamic surface design technique to nonlinear systems with time-varying delayed interconnections. In addition, we prove that all the signals in the closed-loop system are semiglobally uniformly bounded, and the control errors converge to an adjustable neighborhood of the origin. Finally, an example is provided to illustrate the effectiveness of the proposed control system.

In this comment, we point out an error in [1], which will show that the main result of the paper cannot be generalized for nonlinearly parametric time-delay systems considered in [1]. In [1], the authors considered the problem of the adaptive control for a class of nonlinearly parametric time-delay systems and applied successfully the proposed approach to second-order nonlinear time-delay systems with the term x1(t - tau)x2(t - tau) used as simulation examples in Section VI. However, we show in this comment that the control approach presented in [1] cannot be generalized although it is realizable for nonlinear second-order time-delay systems with the term x1(t - tau ) x2(t - tau). In addition, we illustrate this error by one detail example. For simplicity, all the symbols in this comment are the same as those in [1].

In this paper, we propose a new robust output feedback control approach for flexible-joint electrically driven (FJED) robots via the observer dynamic surface design technique. The proposed method only requires position measurements of the FJED robots. To estimate the link and actuator velocity information of the FJED robots with model uncertainties, we develop an adaptive observer using self-recurrent wavelet neural networks (SRWNNs). The SRWNNs are used to approximate model uncertainties in both robot (link) dynamics and actuator dynamics, and all their weights are trained online. Based on the designed observer, the link position tracking controller using the estimated states is induced from the dynamic surface design procedure. Therefore, the proposed controller can be designed more simply than the observer backstepping controller. From the Lyapunov stability analysis, it is shown that all signals in a closed-loop adaptive system are uniformly ultimately bounded. Finally, the simulation results on a three-link FJED robot are presented to validate the good position tracking performance and robustness of the proposed control system against payload uncertainties and external disturbances.

A new method for the robust control of flexible-joint (FJ) robots with model uncertainties in both robot dynamics and actuator dynamics is proposed. The proposed control system is a combination of the adaptive dynamic surface control (DSC) technique and the self-recurrent wavelet neural network (SRWNN). The adaptive DSC technique provides the ability to overcome the "explosion of complexity" problem in backstepping controllers. The SRWNNs are used to observe the arbitrary model uncertainties of FJ robots, and all their weights are trained online. From the Lyapunov stability analysis, their adaptation laws are induced, and the uniformly ultimately boundedness of all signals in a closed-loop adaptive system is proved. Finally, simulation results for a three-link FJ robot are utilized to validate the good position tracking performance and robustness against payload uncertainties and external disturbances of the proposed control system.

This paper concerns an intelligent digital redesign (IDR) technique for a Takagi–Sugeno fuzzy observer-based output-feedback control (FOBOFC) system. The term IDR involves converting an existing analog control into an equivalent digital counterpart in the sense of state-matching. The IDR problem is herein viewed as a minimization problem of the norm distances between nonlinearly interpolated linear operators to be matched. Its constructive condition with global rather than local state-matching is formulated in terms of bilinear matrix inequalities. The main features of the proposed method are that the state estimation error in the plant dynamics is considered in the IDR condition that plays a crucial role in the performance improvement; the stability property is preserved by the proposed IDR algorithm; the separation principle is shown when the premise variables are measurable; finally, the IDR condition for a more general FOBOFC—an estimated premise variable case is conducted. A numerical example is demonstrated to visualize the feasibility of the developed methodology.

This paper proposes a novel and efficient global intelligent digital redesign technique for a Takagi-Sugeno (T-S) fuzzy system. The term of intelligent digital redesign involves converting an existing analog fuzzy-model-based controller into an equivalent digital counterpart in the sense of state-matching. The proposed method should be notably discriminated from the previous works in that it allows us to globally match the states of the overall closed-loop T-S fuzzy system with the predesigned analog fuzzy-model-based controller and those with the digitally redesigned fuzzy-model-based controller, and further to examine the stabilizability by the redesigned controller in the sense of Lyapunov. The key idea is that the global intelligent digital redesign problem is viewed as a convex minimization problem of the norm distance between nonlinearly interpolated linear operators to be matched. Sufficient conditions for the global state-matching and the stability of the digitally controlled system are formulated in terms of linear matrix inequalities (LMIs). A complex nonlinear system, Duffing-like chaotic oscillator is simulated and demonstrated to validate the feasibility and effectiveness of the proposed digital redesign technique, which implies the safe applicability to the digital control system.

A novel approach for navigation of cleaning robots in an unknown workspace is presented which includes a new map representation method and a complete coverage navigation mechanism. First, we discuss a triangular cell map representation which makes the cleaning robot navigate with a shorter path and increased flexibility than a rectangular cell map representation. Then, we propose the complete coverage navigation and map construction methods which enable the cleaning robot to navigate the complete workspace with no complete information about the environment. Finally, we evaluate the performance of our proposed triangular cell map via the existing distance-transform-based path planning method comparing to that of the rectangular cell map.

Intelligent digital redesign involves converting a continuous-time fuzzy-model-based controller into an equivalent discrete-time counterpart for the digital control of continuous-time nonlinear systems by using the Takagi-Sugeno (TS) fuzzy models. In this paper, the authors present a new global state-matching intelligent digital redesign method for nonlinear systems by using genetic algorithms (GAs). More precisely, the intelligent digital redesign problem is converted to an equivalent optimization problem, and then GAs are adopted to find a solution. The search space, in which each problem variable is defined for GAs, are systematically obtained by the interval arithmetic operations. The proposed method results in global matching of the states of the analogously controlled system with those of the digitally controlled system while the conventional intelligent digital redesign method does not. The Chen's chaotic system is used as an illustrative example to show the effectiveness and the feasibility of the developed method. The proposed method provides a new approach for the digital redesign of a class of fuzzy-model-based controllers.

In the above paper, a Takagi-Sugeno (T-S) fuzzy-model-based output regulation methodology is proposed. However, since the problem formulation is incorrect, the derived design techniques may not accomplish the control objectives. We illustrate the reasons and show counterexamples.

We propose a new robust Kalman filter for discrete-time system with norm-bounded parametric uncertainty. The uncertainty is described by energy bound constraints, i.e., sum quadratic constraints (SQC). It is shown that the SQC can be converted into indefinite quadratic cost function to be minimized in Krein space, and it is also found that the Krein space Kalman filter is a solution of this minimization problem. Since the resulting robust Kalman filter has the same recursive structure as a nominal Kalman filter has, a robust filtering scheme can be readily designed via the proposed method. Simulation results show the improved performance and the robustness of the proposed filter.

Addresses the robust fuzzy control problem for nonlinear systems in the presence of parametric uncertainties. The Takagi-Sugeno (T-S) fuzzy model is adopted for fuzzy modeling of the nonlinear system. Two cases of the T-S fuzzy system with parametric uncertainties, both continuous-time and discrete-time cases are considered. In both continuous-time and discrete-time cases, sufficient conditions are derived for robust stabilization in the sense of Lyapunov asymptotic stability, for the T-S fuzzy system with parametric uncertainties. The sufficient conditions are formulated in the format of linear matrix inequalities. The T-S fuzzy model of the chaotic Lorenz system, which has complex nonlinearity, is developed as a test bed. The effectiveness of the proposed controller design methodology is finally demonstrated through numerical simulations on the chaotic Lorenz system.