Adaptive Fuzzy Observer Backstepping Control for a Class of Uncertain Nonlinear Systems with Unknown Time-delay

In this paper, a new adaptive fuzzy backstepping control approach is developed for a class of nonlinear systems with unknown time-delay and unmeasured states. Using fuzzy logic systems to approximate the unknown nonlinear functions, a fuzzy state observer is designed for estimating the unmeasured states. On the basis of the state observer and applying the backstepping technique, an adaptive fuzzy observer control approach is developed. The main features of the proposed adaptive fuzzy control approach not only guarantees that all the signals of the closed-loop system are semiglobally uniformly ultimately bounded, but also contain less adaptation parameters to be updated on-line. Finally, simulation results are provided to show the effectiveness of the proposed approach.

A Non-Singleton Type-3 Fuzzy Modeling: Optimized by Square-Root Cubature Kalman Filter

In many problems,to analyze the process/metabolism behavior,a mod-el of the system is identified.The main gap is the weakness of current methods vs.noisy environments.The primary objective of this study is to present a more robust method against uncertainties.This paper proposes a new deep learning scheme for modeling and identification applications.The suggested approach is based on non-singleton type-3 fuzzy logic systems(NT3-FLSs)that can support measurement errors and high-level uncertainties.Besides the rule optimization,the antecedent parameters and the level of secondary memberships are also adjusted by the suggested square root cubature Kalmanfilter(SCKF).In the learn-ing algorithm,the presented NT3-FLSs are deeply learned,and their nonlinear structure is preserved.The designed scheme is applied for modeling carbon cap-ture and sequestration problem using real-world data sets.Through various ana-lyses and comparisons,the better efficiency of the proposed fuzzy modeling scheme is verified.The main advantages of the suggested approach include better resistance against uncertainties,deep learning,and good convergence.

Fuzzy-Based Secure Clustering with Routing Technique for VANETs

Due to the advanced developments in communication technologies,Internet of vehicles and vehicular adhoc networks(VANET)offers numerous opportunities for effectively managing transportation problems.On the other,the cloud environment needs to disseminate the emergency message to the vehicles which are consistently distributed on the roadway so that every vehicle gets the messages from closer vehicles in a straightforward way.To resolve this issue,clustering and routing techniques can be designed using computational intelligence approaches.With this motivation,this paper presents a new type-2 fuzzy sets based clustering with metaheuristic optimization based routing(T2FSCMOR)technique for secure communication in VANET.The T2FSC-MOR technique aims to elect CHs and optimal routes for secure intercluster data transmission in VANET.The proposed model involves T2FSC technique for the selection of CHs and construction of clusters.The T2FSC technique uses different parameters namely traveling speed(TS),link quality(LQ),trust factor(TF),inter-vehicle distance(IVD),and neighboring node count(NCC).The inclusion of trust factor helps to select the proper cluster heads(CHs)for secure data dissemination process.Moreover,trust aware seagull optimization based routing(TASGOR)approach was derived for the optimal selection of routes in VANET.In order to validate the enhanced performance of proposed technique,the set of simulations take place and the outcomes are examined interms of different measures.The experimental outcomes highlighted the improved performance of the proposed model over the other state of art techniques with a higher throughput of 98%.

Stabilising control for a class of chaotic systems based on adaptive fuzzy logic systems

For a class of chaotic systems with unknown functions and disturbances,asymptotic stabilisation of the chaotic systems is achieved by designing a stability controller based on the adaptive fuzzy logic systems.At first,based on the universal approximation property of fuzzy logic systems,the Mamdani-type fuzzy logic systems with the parameter adaptive laws are designed utilising the data information sampled from the inputs and outputs of unknown functions in the chaotic systems,then the fuzzy logic systems are used to design the stability controller with three parameter adaptive laws,but the three parameters have no relationship with the number of fuzzy rules,so the stability controller is not only able to achieve asymptotic stabilisation for the chaotic system’s states,but also to reduce the number of fuzzy rules and the no-line computational burden significantly.Finally,simulations are used to show the validity of the stabilisation method.

Engineering DNA logic systems with non-canonical DNA-nanostructures:basic principles,recent developments and bio-applications

Engineering DNA logic systems is considered as one of the most promising strategies for next-generation molecular computers.Owing to the inherent features of DNA,such as low cost,easy synthesis,and controllable hybridization,various DNA logic devices with different functions have been developed in the recent decade.Besides,a variety of logic-programmed biological applications are also explored,which initiates a new chapter for DNA logic computing.Although this field has gained rapid developments,a systematical review that could not only elaborate the logic principles of diverse DNA logic devices but also outline recent representative works is urgently needed.In this review,we first elaborate the general classification and logical principle of diverse DNA logic devices,in which the operating strategy of these devices and representative examples are selectively presented.Then,we review state-of-the-art advancements in DNA computing based on different non-canonical DNA-nanostructures during the past decade,in which some classical works are summarized.After that,the innovative applications of DNA computing to logic-controlled bioanalysis,cell imaging,and drug load/delivery are selectively presented.Finally,we analyze current obstacles and suggest appropriate prospects for this area.

Finite-Time Observability of Probabilistic Logical Control Systems

This study investigates finite-time observability of probabilistic logical control systems(PLCSs)under three definitions(i.e.,finite-time observability with probability one,finite-time singleinput sequence observability with probability one,and finite-time arbitrary-input observability with probability one).The authors adopt a parallel extension technique to recast the finite-time observability problem of a PLCS as a finite-time set reachability problem.Then,the finite-time set reachability problem can be transferred to stabilization problem of a logic dynamical system by using the state transfer graph reconstruction method.Necessary and sufficient conditions for finite-time observability under the three definitions are derived respectively.Finally,the proposed methods are illustrated by numerical examples.

Practical fixed-time adaptive fuzzy control of uncertain nonlinear systems with time-varying asymmetric constraints: a unified barrier function based approach

A practical fixed-time adaptive fuzzy control strategy is investigated for uncertain nonlinear systems with time-varying asymmetric constraints and input quantization. To overcome the difficulties of designing controllers under the state constraints, a unified barrier function approach is employed to construct a coordinate transformation that maps the original constrained system to an equivalent unconstrained one, thus relaxing the time-varying asymmetric constraints upon system states and avoiding the feasibility check condition typically required in the traditional barrier Lyapunov function based control approach. Meanwhile, the “explosion of complexity” problem in the traditional backstepping approach arising from repeatedly derivatives of virtual controllers is solved by using the command filter method. It is verified via the fixed-time Lyapunov stability criterion that the system output can track a desired signal within a small error range in a predetermined time, and that all system states remain in the constraint range. Finally, two simulation examples are offered to demonstrate the effectiveness of the proposed strategy.

HiTL-based adaptive fuzzy tracking control of MASs:A distributed fixed-time strategy

Human-in-the-loop(HiTL)control is promising for the cooperative control problem of multi-agent systems(MASs)under the complicated environment.By considering the effect of human intelligence and decision making,the system robustness and security are notably enhanced.Hence,a distributed fixed-time tracking control problem is investigated in this paper for heterogeneous MASs based on the HiTL idea.First,a lemma of practically fixed-time stable is given where an explicit relationship of settling time and convergence domain is clearly shown.Then,under the framework of the adaptive backstepping approach,a series of modified intermediate control signals is designed to avoid the singularity problem by taking advantage of power transformation,fuzzy logic systems,and inequality schemes.Finally,the numerical example and comparison results are utilized to testify the effectiveness of the proposed method.

A matrix-based static approach to analysis of finite state machines

Traditional matrix-based approaches in the field of finite state machines construct state transition matrices,and then use the powers of the state transition matrices to represent corresponding dynamic transition processes,which are cornerstones of system analysis.In this study,we propose a static matrix-based approach that revisits a finite state machine from its structure rather than its dynamic transition process,thus avoiding the“explosion of complexity”problem inherent in the existing approaches.Based on the static approach,we reexamine the issues of closed-loop detection and controllability for deterministic finite state machines.In addition,we propose controllable equivalent form and minimal controllable equivalent form concepts and give corresponding algorithms.

Global FLS-based Consensus of Stochastic Uncertain Nonlinear Multi-agent Systems

Using graph theory, matrix theory, adaptive control, fuzzy logic systems and other tools, this paper studies the leader-follower global consensus of two kinds of stochastic uncertain nonlinear multi-agent systems(MAS). Firstly, the fuzzy logic systems replaces the feedback compensator as the feedforward compensator to describe the uncertain nonlinear dynamics. Secondly, based on the network topology, all followers are divided into two categories: One is the followers who can obtain the leader signal, and the other is the follower who cannot obtain the leader signal. Thirdly, based on the adaptive control method, distributed control protocols are designed for the two types of followers. Fourthly, based on matrix theory and stochastic Lyapunov stability theory, the stability of the closed-loop systems is analyzed. Finally, three simulation examples are given to verify the effectiveness of the proposed control algorithms.

State space optimization of finite state machines from the viewpoint of control theory

Motivated by the inconvenience or even inability to explain the mathematics of the state space optimization of finite state machines(FSMs)in most existing results,we consider the problem by viewing FSMs as logical dynamic systems.Borrowing ideas from the concept of equilibrium points of dynamic systems in control theory,the concepts of t-equivalent states and t-source equivalent states are introduced.Based on the state transition dynamic equations of FSMs proposed in recent years,several mathematical formulations of t-equivalent states and t-source equivalent states are proposed.These can be analogized to the necessary and sufficient conditions of equilibrium points of dynamic systems in control theory and thus give a mathematical explanation of the optimization problem.Using these mathematical formulations,two methods are designed to find all the t-equivalent states and t-source equivalent states of FSMs.Further,two ways of reducing the state space of FSMs are found.These can be implemented without computers but with only pen and paper in a mathematical manner.In addition,an open question is raised which can further improve these methods into unattended ones.Finally,the correctness and effectiveness of the proposed methods are verified by a practical language model.

Hybridity in Embedded Computing Systems

An embedded system is a system that computer is used as a componelit ina larger device. In this paper, we study hybridity in embedded systems andpresent an interval based temporal logic to express and reason about hybridproperties of such ldnd of systems.