This paper concerns the distributed state estimation-based security control issue for Internet-of-Things (IoT)-enabled wireless power transfer (WPT) system subject to interchange attacks. The WPT system is first modeled using a state-space framework, and then the IoT elements including Web-enabled smart sensors are considered to obtain measurements for state estimation and control purpose. Additionally, a Bernoulli distribution-based random variable with known probability is introduced to characterize randomly occurring interchange attacks in output measurements. Using Lyapunov theory and stochastic analysis technique, a set of sufficient conditions is established in terms of linear matrix inequalities for ensuring convergence of state estimation with reliable and consistent power transfer operations within the described WPT system applications. Furthermore, particle swarm optimization algorithm is used to determine the optimal gain of the state estimation scheme for minimizing mean-squared estimation errors. Finally, numerical simulation is given to illustrate the efficiency and usefulness of the presented control approach.

Recently, type-3 (T3) fuzzy logic systems (FLSs) have been widely used in various problems, such as modeling, control systems, image processing, forecasting problems, optimization algorithms, and many others. Most studies of T3-FLS focus on its different applications. However, the basic theory, the applications in real-time and online problems, learning schemes, and the robustness against non-Gaussian noises have been rarely studied. In this article, the simplification of T3-FLSs is taken into account, and the new membership functions (MFs), learning schemes, and type reduction are introduced. The concept of singleton MFs in adaptive fuzzy inference systems (ANFIS) is extended to T3-FLSs, and T3-ANFIS is proposed. The type reduction is simplified, and a noniterative learning scheme is developed. The corresponding computations for adaptation laws are derived, and all rules parameters and MF parameters are adjusted. To enhance the robustness versus impulsive noises, a T3-FLS-based correntropy Kalman filter (CKF) is designed. In the suggested algorithm, the kernel-size is not a constant value, but it is online updated by a T3-FLS. Also, to further improve robustness against noisy data, nonsingleton fuzzification for the suggested MF is formulated. By several simulations using real data sets, the feasibility of the suggested T3-FLS is shown, and its superiority is verified by comparisons. Also, the better robustness of suggested T3-FLS-based CKF versus impulsive noises is shown by comparison with traditional KFs.

This article addresses the problem of finite-time filtering for a class of discrete-time Takagi-Sugeno (T-S) fuzzy-model-based networked control systems (NCSs) subject to switching communication channels and cyber attacks. Specifically, the fluctuations of the state estimator gain parameters are taken into account in the design of the filter. Further, cyber attacks are supposed to take place in the communication channel and can modify the sensor signals by injecting false data if an attack is successful. Meanwhile, the transition probability of the Markov process is a switching phenomenon along with the minimum and maximum bounds of the multichannel communication scheme. By employing appropriate Lyapunov–Krasovskii functional along with reciprocally convex approach, a set of sufficient conditions guaranteeing the finite-time mixed$H_\infty$and passivity performance for NCSs are derived in terms of linear matrix inequalities. In the end, two illustrated examples quantify the effectiveness of the proposed analysis and filter design method.

This paper proposes a novel sampled-data platooning control design for a group of connected autonomous vehicles based on vehicle position sensor measurements, subject to unknown dynamics, leader vehicle control input and interchange cyber-attacks. Considering the limitations of velocity and acceleration sensors, the system approximates the finite difference values of current and recent previous vehicle position measurements. First, a unified model is presented to describe the coordinated platoon behavior of leader-follower vehicles, where inter-vehicle information is propagated via a wireless communication network. Second radial basis function neural network-based observer is constructed to estimate the effect unknown dynamics behavior. Third, multiple sampled-data control loops are considered, utilizing a shared communication network for data transmission. Fourth, the overall closed-loop platooning error system is formulated with the addressed stochastic interchange attack. The desired inter-vehicle stability criterion for the closed-loop platoon error system is obtained by employing Lyapunov-Krasovskii stability theory, and the maximum allowable sampling period with control gains for the addressed control problem are determined. Finally, simulation results are presented to substantiate the effectiveness and advantages of the proposed memory sampled-data platooning control algorithm against interchange attacks and leader vehicle input. Additionally, the effect of interchange attacks is compared with DoS attacks.

In this article, the interval type-3 fuzzy-based state feedback control is proposed for the stabilization problem of interval type-3 fuzzy systems (IT3FSs) subject to time-varying delay. Specifically, to improve the model accuracy and control capabilities, we proposed a nonparallel distributed compensation-based controller approach, be precise, the system and controller adopts the distinct membership functions. Moreover, the stability analysis of IT3FSs in the state space form is studied newly in this article. Besides the primary membership function that exists for interval type-2 fuzzy systems (IT2FSs), the IT3FSs possess the secondary membership function to handle the overall uncertainty which enacts the superiority of the proposed work. Notably, the adequate stability criteria are derived in the form of linear matrix inequality (LMI) using the Lyapunov stability theorem. Additionally, the convex relaxation technique is used to strengthen the design flexibility and achieve the faster convergence rate, which converts an optimization problem with a vector variable to a convex program with a matrix variable, via a lifting technique. A noteworthy aspect is that the application of the convex relaxation technique in the context of IT3FSs is proposed for the first time in literature. Finally, the comparative results between IT2FSs and IT3FSs are shown via two illustrative examples, including the inverted-pendulum model, to underscore the efficacy and practical implementations of the developed control methodology.

In this article, the state estimation problem for wind turbine systems (WTSs) is studied by considering the Internet of Things (IoT) framework. Specifically, the WTS is described in the form of a state-space model along with sensors to obtain required state information. Precisely, to discuss the operating condition of the WTSs, multiobjective PSO-based event-triggered state estimation algorithm is developed. Under the event-triggered framework, the state estimator updates sensor measurements in response to specific triggering events determined by time-varying threshold parameter. This approach enhances communication efficiency by reducing the number of transmitted data packets and minimizing battery usage. The simulation results reveal that developed approach can accurately estimate state which converges to the actual state. Precisely, the conditions are developed based on the Lyapunov technique to ensure that the error system to be asymptotically stable. The developed algorithm can be useful in IoT-based WTSs with optimized communication frequency and reduced network traffic. Moreover, the control design is developed for the addressed systems by employing pole-placement technique. Finally, the effectiveness of the proposed estimation algorithm is demonstrated through numerical simulations by achieving optimal utilization of sensors, communication frequency, networks transmission and battery usage.

In this study, combined problem of robust output tracking and disturbance rejection for nonlinear semi-Markovian jump systems with time-varying delays and uncertainties is investigated in through improved-equivalent-input-disturbance-estimator (IEIDE)-based modified repetitive control (MRC) law. More precisely, IEIDE is introduced into the channel of control input to estimate and compensate the disturbance signals with high precision with an eye to attain the robust tracking performance. Subsequently, MRC block is internally embedded in the control loop that makes the system output signals to track the given reference signals inside the selected neighborhood of the equilibrium via repetitive learning. Furthermore, to obtain the controller and observer gain matrices , the linear matrix inequality conditions are derived based on the technique of Lyapunov stability . Finally, a switching boost converter circuit and single-link robot arm model are used to demonstrate the control scheme's efficiency and superiority. In addition to that the comparative study with the existing works are carried out. In this way, the potential benefits from practical perspectives of the developed results are validated.

This article investigates the asymptotic stabilization of periodic piecewise time-varying systems with time-varying delay under various cyber attacks, particularly deception and acrlong DoS attacks. The addressed system is reformed into a number of time-varying subsystems based on the time interval for each period. Following that, a state-feedback controller with periodic time-varying gain parameters is developed to solve the stabilization problem. The control design depicts the possibility of the aforementioned cyber attacks with two mutually exclusive stochastic Bernoulli distributed parameters. Then, an augmented Lyapunov-Krasovskii functional with periodically varying matrices is used to determine the conditions for designing the proposed controller that ensures the mean-square asymptotic stability of the addressed system. The results of numerical examples support the conclusion that the proposed method is effective and superior, regardless of the cyber attacks involved.

This paper proposes an approach for the solvability of multi-agent systems with Markov jumps subject to time-varying delay and uncertainties. The primary concern of this paper is to construct a state feedback control design with a guaranteed cost function which not only ensures consensus but also assures certain amount of energy consumption. This cost function is designed with the aid of control inputs of all agents and state error among neighbouring agents. A new set of sufficient conditions for the guaranteed cost consensus of the considered Markov jumping multi-agent system (MAS) is derived in terms of linear matrix inequalities (LMIs) by constructing suitable Lyapunov-Krasovskii functional (LKF) and with the aid of Kronecker product properties. Finally, a numerical example is given to illustrate the effectiveness of the developed theoretical results.

This paper addresses the reliable finite-time stabilisation problem for a class of fractional-order memristor neural networks under sampled-data controller influenced by the quantisation signal and actuator failures. Precisely, the framework of observer has been initiated for estimating unmeasured state and remunerate the actuator faults with nonlinearities in the controller. Precisely, quantiser is incorporated in the network can reduce the process of transmitting data. Subsequently, activation function approach bringing together with traditional indirect Lyapunov theory endows some sufficient conditions in the frame of linear matrix inequalities to assure the finite-time stabilisation criterion for the addressed neural networks under the proposed reliable sampled-data control. Explicitly, the state feedback control and observer gain matrices are attained by solving the developed linear matrix inequalities. Convincingly, two numerical simulations are explored to substantiate the excellence and potentiality of the developed control law.

The prime purpose of this study is to design an output tracking control law in the perspective of compensating the effects induced by time-varying input delays and disturbances. In particular, the control protocol is developed for nonlinear systems, where the nonlinearities are effectively represented as a convex summation of linear subsystems using membership functions, which is precisely referred by fuzzy model approach. Accordingly, with the aid of parallel distributed compensation strategy and extended Smith predictor approach, the fuzzy-dependent dynamic control law is formulated. Moreover, the improved-equivalent-input-disturbance estimator approach is implemented to estimate the disturbance signals, whose output is tied to the resultant dynamic output feedback controller. On such circumstances, the controller gain matrices that assure the asymptotic tracking performance of the addressed system are calculated by solving the derived set of sufficient conditions. Conclusively, the effectiveness of the developed theoretical outcomes is validated by virtue of numerical simulations.

Quantized antidisturbance control design problem is presented for a class of interval type-2 (IT2) fuzzy stochastic systems subject to time delays and multiple disturbances. The inherent uncertain nonlinear and hybrid characteristics of the concerned system make it difficult to design a stable antidisturbance controller. In order to properly reflect the characteristics of IT2 fuzzy stochastic models with multiple disturbances, a new fuzzy disturbance observer is proposed to estimate the disturbances generated by a fuzzy exogenous system. Furthermore, a quantized fuzzy antidisturbance control scheme is synthesized by fusing the estimation of the multiple disturbances. With the support of Lyapunov functional method,$It{\hat{o}}$’s formula and auxiliary function-based integral inequality, the resulting IT2 fuzzy stochastic systems are proved to be robustly stochastically stable with mixed$H_\infty$/passivity performance index. The design methods of the fuzzy disturbance observer and quantized antidisturbance controller are formulated in the form of linear matrix inequalities so that the corresponding gain matrices can be easily obtained. Finally, simulation studies on three examples are provided to justify the efficiency of the designed control strategy.

This article addresses the issue of input–output finite-time stabilization for interval type-2 fuzzy systems in the presence of deception attack effects. Our main goal is to make efficient use of network resources by developing an event-triggered controller for interval type-2 fuzzy systems. For the stabilization process, an event-triggered controller that does not share the same membership functions as the system is designed by using affine transformation parameters. Following that, by using an asymmetric Lyapunov–Krasovskii functional and some advanced integral inequalities to establish the sufficient conditions for the existence of the proposed controller. Furthermore, an asymptotic stabilization result is presented and discussed in a comparative analysis as a special case. Due to the asymmetric Lyapunov–Krasovskii functional structure, the transmission delay interval is large compared with some recent studies. Finally, two simulation examples are carried out and the efficiency of designed controller is verified.

This article aims to resolve the event-based finite-time dissipative filtering issue for interval type-2 fuzzy complex dynamical networks with coupling delays and cyber attacks. An event-triggered scheme introducing the estimation error is designed, which is a tradeoff between state estimator performance and network communication bandwidth in accordance with the practical requirements. Moreover, the cyber attack model is established, which consists of randomly occurring deception attacks. The stochastic variable obeying the Bernoulli distribution is utilized to describe the attack condition. Through handling the linear matrix inequalities (LMIs) with some slack matrices, the event-triggered finite-time filter is designed to guarantee that the resulting systems are finite-time bounded and strictly dissipative. The proposed filter design parameters are obtained by solving LMIs. Finally, a numerical example and a practical example of a tunnel diode circuit system are used to show the effectiveness and applicability of the proposed event-based dissipative filtering scheme.

The issues of input–output finite-time stability and stabilization of Takagi–Sugeno (T–S) fuzzy systems with time-varying state delay and exogenous input signal are studied in this article. For the stabilization process, a controller that does not share the same membership functions as the system is considered and the state feedback is quantized by using a logarithmic quantizer. The desired stability criterion for the addressed fuzzy system is first derived without the control term. It is then extended to the case in which the proposed controller is present. The stability criteria are given in the form of matrix inequalities, which are supported by the augmented Lyapunov–Krasovskii functional, generalized free-weighting-matrix approach and Finsler’s lemma. Moreover, as a special case, an asymptotic stability criterion is obtained and used in a comparative study. As a result, the time-delay interval is much larger than some of the works published very recently, which is due to the augmented Lyapunov–Krasovskii functional construction. In addition, the effectiveness and practicability of the theoretical findings are validated with simulation examples.

In this article, the robust asynchronous filtering problem is investigated for a class of discrete-time T–S fuzzy complex dynamical networks subjected to random coupling delays and deception attacks. Specifically, the deception attacks are considered in the filter, where the adversary attempts to inject some false information data in the measurement output to modify the transmitted signal in the communication networks. We introduce, respectively, two sets of stochastic variables satisfying the Bernoulli distribution to depict the probability of the data transmitted by the network being subjected to time-varying coupling delays and deception attacks. By using Lyapunov–Krasovskii stability theory and Abel lemma-based finite-sum inequality, a new set of sufficient conditions is established, which ensures the stochastic stability of the resulting error system with a prescribed mixed$H_\infty$and passivity performance index. The proposed filter parameters are obtained by solving linear matrix inequalities. Ultimately, both the effectiveness and advantages of the proposed asynchronous filter with deception attacks are verified by two numerical examples including a tunnel diode circuit model.

In this work, the problem of fault and state estimation is studied for Takagi–Sugeno fuzzy systems with time-varying delay and external disturbance over a finite horizon. A fuzzy rule-based fault estimator with two tuning parameters, including the current system output information, is designed. Based on which, the Luenberger state observer is then considered to estimate the immeasurable states of the system addressed. The aim of taking into account the tuning parameters in the estimator design is to improve the accuracy of the fault estimation. More precisely, by defining error variables in accordance with actual and estimated system states and faults, an augmented system is formulated to achieve the desired results. By using the existing stability theory and integral inequality, a delay-dependent criterion for ensuring that the states of the augmented system are bounded over a finite horizon is derived. Subsequently, conditions for determining the observer gain matrices are presented. Two simulation examples, including an application example of the truck-trailer model, are provided to demonstrate the advantages of the theoretical results that have been established. In particular, the importance of the use of tuning parameters is discussed.

The aim of this article is to analyze the problem of fault estimation for mode-dependent interval type-2 fuzzy systems with quantized output measurements. Different from the existing fault estimation methods requiring the observer matching condition, a new fault estimation technique is proposed, wherein a stochastically intermediate variable subject to the information on operating modes is considered, under which a robust observer is constructed to simultaneously estimate the state and faults. Based on the strategy of linear matrix inequality, sufficient conditions are established to ensure that the states of resulting systems are bounded in probability sense. By offering three illustrative examples, in which two of them are practical models, namely, tunnel diode circuit system and Rössler system, the availability and feasibility of the proposed design method are explained.

This article intends to provide an integrated robust synchronization and anti-disturbance control design for a nonlinear multiweighted complex network with the use of fuzzy modeling approach. The network takes the effect of both matched and mismatched disturbances into account, wherein the matched one is unknown and caused by some exogenous systems. In order to estimate the unknown disturbance, a disturbance observer is precisely considered. By exploiting the parallel distributed compensation strategy and the output of disturbance observer, the desired fuzzy rule-based control law is formulated. Based on these settings, the required control gain matrices that affirm asymptotic synchronization of the addressed network are determined with the support of the Lyapunov functional method and extended Wirtinger's integral inequality. The method of this article can render better system performance than the existing H∞control method, which is demonstrated via simulations. In addition, a three-dimensional Lorenz chaotic model is employed to certify the applicability of the theoretical findings.

This paper develops a novel maximum power point tracking (MPPT) control strategy for nonlinear photovoltaic (PV) systems. The MPPT control problem of the considered PV systems is first formulated in the framework of fuzzy descriptor systems. Then, based on the new formulation, a fuzzy sliding mode control (FSMC) law is constructed to drive the state trajectories into a desired sliding surface within the finite-time T* with T* <; T. Moreover, sufficient conditions are derived to ensure the reachable set boundings of the closed-loop PV control systems in the finite-time intervals [0,T*] and [T*,T]. A simulation example is given to show the effectiveness of the proposed design method.

In this paper, based on the Takagi-Sugeno fuzzy model approach, a novel output feedback control strategy is developed for a class of continuous-time nonlinear time delay systems via modified repetitive control scheme. The main objective of this paper is to design a fuzzy rule-based modified repetitive controller such that the system output can track any given periodic reference signal even in the presence of periodic disturbances. For this purpose, by employing fuzzy-dependent Lyapunov-Krasovskii functional together with the free-weighting matrix technique, a new set of sufficient conditions is presented in terms of linear matrix inequalities to guarantee the robust stability of the resulting closed-loop system. Based on these conditions and using internal model principle, it is shown that the system output tracks the given reference input within the desired error. Finally, two application-oriented examples are presented to validate the obtained theoretical results in which the proposed control design scheme gives better performance over the existing control scheme.

This paper investigates the problem of resilient fault-tolerant control for a class of Takagi-Sugeno fuzzy model which is described by fractional order differential equations. In particular, the system input under consideration is subject to actuator saturations, actuator faults, and nonlinearities. The main purpose of this paper is to put forward a generalized nonlinear resilient fault-tolerant controller that can make the considered system robustly asymptotically stable. Utilizing the concept of continuous frequency distribution, a new set of linear matrix inequalities, which are the sufficient conditions for robust asymptotic stability of the closed-loop system, are derived by using the indirect Lyapunov approach. Two numerical examples are included to illustrate the feasibility and effectiveness of the proposed method.

This paper investigates the observer-based synchronization problem for a family of complex dynamical networks subject to time delay, external disturbance, randomly occurring actuator faults, and input saturation. A realistic actuator fault model is considered in which the actuator faults are represented by stochastic variables that are assumed to obey a certain probabilistic distribution. An H∞ performance-related criterion is obtained via the Lyapunov-Krasovskii functional approach and stochastic analysis technique to asymptotically minimize the synchronization error and observer error simultaneously. Moreover, to meet the requirement of actuator saturation, the conditions for the domain of the attraction region are determined by employing the linear matrix inequality (LMI) approach and an optimization technique. Specifically, the proposed controller for the network synchronization is very simple and easy to implement in practical systems. Furthermore, the gain values of controller and observer gains are calculated by solving a set of LMIs. Eventually, the proposed theoretical results are verified through numerical simulations.

A nondeterministic actuator fault model-based reliable control design method is presented for the leader-following consensus of a multiagent system (MAS) subject to input time-varying delay and the nonlinear phenomenon through the observer framework. Notably, a more generalized practical actuator fault model is proposed where the faults of each actuator are assumed to occur randomly and the fault rates are characterized by stochastic variables that satisfy certain probability conditions. To trace the leader dynamics, a reliable observer-based state feedback consensus algorithm is developed for the pursuing agents. After that, in light of graph theory and the Lyapunov-Krasovskii stability theorem, the required conditions are achieved to guarantee the leader-following consensus of the considered MAS, and then the corresponding reliable controller design method is proposed. Furthermore, simulations are eventually conducted to exhibit the efficiency of the newly developed control scheme, where it is shown that the obtained conditions guarantee the consensus of the considered MAS in the case of possible actuator faults.

This paper addresses the exponential H∞ filtering problem for a class of discrete-time switched neural networks with random time-varying delays. The involved delays are assumed to be randomly time-varying which are characterized by introducing a Bernoulli stochastic variable. Effects of both variation range and distribution probability of the time delays are considered. The nonlinear activation functions are assumed to satisfy the sector conditions. Our aim is to estimate the state by designing a full order filter such that the filter error system is globally exponentially stable with an expected decay rate and a H∞ performance attenuation level. The filter is designed by using a piecewise Lyapunov-Krasovskii functional together with linear matrix inequality (LMI) approach and average dwell time method. First, a set of sufficient LMI conditions are established to guarantee the exponential mean-square stability of the augmented system and then the parameters of full-order filter are expressed in terms of solutions to a set of LMI conditions. The proposed LMI conditions can be easily solved by using standard software packages. Finally, numerical examples by means of practical problems are provided to illustrate the effectiveness of the proposed filter design.

This paper is concerned with the problem of reliable mixed H ∞ and passivity-based control for a class of stochastic Takagi-Sugeno (TS) fuzzy systems with Markovian switching and probabilistic time varying delays. Different from the existing works, the H∞ and passivity control problem with probabilistic occurrence of time-varying delays and actuator failures is considered in a unified framework, which is more general in some practical situations. The main aim of this paper is to design a reliable mixed H∞ and passivity-based controller such that the stochastic TS fuzzy system with Markovian switching is stochastically stable with a prescribed mixed H∞ and passivity performance level γ > 0 . Based on the Lyapunov-Krasovskii functional (LKF) involving lower and upper bound of probabilistic time delay and convex combination technique, a new set of delay-dependent sufficient condition in terms of linear matrix inequalities (LMIs) is established for obtaining the required result. Finally, a numerical example based on the modified truck-trailer model is given to demonstrate the effectiveness and applicability of the proposed design techniques.

This study is concerned with the problem of robust reliable dissipative filter design for networked control systems (NCSs) with sensor failures and random packet dropouts. The considered NCS model is subject to the sources of uncertainty in the system parameters. The sensor signals are modelled by sequences of a Bernoulli distributed white sequence and the packet dropouts may occur randomly during transmission. The main objective is to design a suitable reliable dissipative filter such that, for all network‐induced imperfections, a resulting error system is robustly stochastically stable and strictly (𝒬, 𝒮, ℛ) dissipative. The results are obtained for known as well as unknown sensor failure rates, so the results are more general one because it can guarantee the dissipativity of system whether or not the sensor encounter failures. The sufficient conditions for existence of filters are derived in terms of linear matrix inequality (LMI) approach and the corresponding filter parameters can be obtained by solution to a set of LMIs, which can be easily solved by using some standard numerical packages. Finally, two numerical examples are given to illustrate the applicability and effectiveness of the proposed filter design.

The Rao--Blackwellized particle filter (RBPF) and FastSLAM have two important limitations, which are the derivation of the Jacobian matrices and the linear approximations of nonlinear functions. These can make the filter inconsistent. Another challenge is to reduce the number of particles while maintaining the estimation accuracy. This paper provides a robust new algorithm based on the scaled unscented transformation called unscented FastSLAM (UFastSLAM). It overcomes the important drawbacks of the previous frameworks by directly using nonlinear relations. This approach improves the filter consistency and state estimation accuracy, and requires smaller number of particles than the FastSLAM approach. Simulation results in large-scale environments and experimental results with a benchmark dataset are presented, demonstrating the superiority of the UFastSLAM algorithm.

This paper addresses the problem of Simultaneous Localization and Map Building (SLAM) using a Neural Network aided Extended Kalman Filter (NNEKF) algorithm. Since the EKF is based on the white noise assumption, if there are colored noise or systematic bias error in the system, EKF inevitably diverges. The neural network in this algorithm is used to approximate the uncertainty of the system model due to mismodeling and extreme nonlinearities. Simulation results are presented to illustrate the proposed algorithm NNEKF is very effective compared with the standard EKF algorithm under the practical condition where the mobile robot has bias error in its modeling and environment has strong uncertainties. In this paper, we propose an algorithm which enables a biased control input in vehicle model using neural network.

Rao-Blackwellized Particle Filter and FastSLAM have become popular tools to solve the Simultaneous Localization and Mapping (SLAM) problem. However, the above techniques have two important potential limitations, which are the derivation of the Jacobian matrices and the linear approximations to the nonlinear functions. Also, one of the major challenges of both Rao-Blackwellized particle filter and FastSLAM is to reduce the number of particles while maintaining the estimation accuracy. This paper proposes a new algorithm based on unscented transformation called Unscented FastSLAM (UFastSLAM) that overcomes important drawbacks of the Rao-Blackwellized particle filter frameworks by directly using nonlinear relations. Experimental results in large scale environments are presented that demonstrate the effectiveness of the UFastSLAM algorithm over the previous approaches.