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Summary: This article investigates the devise issue of a practical finite‐time output‐tracking control for uncertain nonlinear nonstrict‐feedback systems subject simultaneously to time‐varying output constraints (TVOC), actuator saturation, and bounded unmatched disturbances. An adaptive fuzzy approximator‐based control system is devised using the dynamic surface control (DSC) concept. System output constraints are addressed by employing an appropriate barrier Lyapunov function. Adaptive fuzzy disturbance observers are constructed to practically deal with time‐varying unmatched but bounded disturbances. It is verified via Lyapunov stability analysis and simulation experiments that all closed‐loop signals remain bounded, the output tracking error practically converges in finite time to a tiny but adjustable neighborhood around the origin, and the system output stays within its required time‐varying bounds. [ABSTRACT FROM AUTHOR]

This paper introduces an analog integrated fractional order type-2 fuzzy PID control system. Current approaches frequently depend on energy-intensive embedded digital systems, consuming substantial energy levels ranging from a few μW to mW. To address this limitation we propose a fully analog design offering insights into the potential of analog circuits for powerefficient robust control in complex and uncertain environments. It consists of Gaussian function, min/max, Operational transcoductance amplifier circuits and Resistor-Capacitor networks for the implementation of the fractional-order components. Crafted for operation under a reduced voltage supply (0.6 V), the controller attains minimal power usage (861.8 nW), facilitating uninterrupted, extended-term functioning. Post-layout simulation results confirm the proper operation of the proposed design. The proposed system is designed and simulated using the Cadence IC Suite in a TSMC 90 nm CMOS process. [ABSTRACT FROM AUTHOR]

This paper addresses the finite-time control problem of a class of uncertain nonlinear systems subject to input saturation and actuator faults. To approximate the unknown system states, a fuzzy state observer is established where fuzzy logic systems are utilized to estimate the unknown nonlinearities. A nonlinear disturbance observer to estimate the external disturbance of the system. A new finite-time adaptive fuzzy controller is constructed together with the proposed disturbance and state observers, which can guarantee the tracking error into a small neighborhood around zero within finite time. To avoid the tedious and arithmetic problems brought by the traditional backstepping control, the dynamic surface technique is applied, which can effectively reduce the computation burden. It can be proved that not only the boundedness of the closed-loop system states can be guaranteed but also the tracking error is regulated into a small range near the equilibrium in finite time, despite unknown actuator faults and input saturation. Finally, the simulations of two-stage chemical reactor nonlinear system are conducted to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This article is concerned with the adaptive event‐triggered (AET) efficient output feedback model predictive control (EOFMPC) for a set of nonlinear networked interval type‐2 Takagi–Sugeno (IT2 T–S) fuzzy control systems with data loss. For the reduction in data transmission burden and required control performance, the AET scheme and a novel dynamic triggering law are introduced jointly in this article. The efficient model predictive control approach as in Kouvaritakis et al. (IEEE Trans Automat Contr. 2000;45(8):1545–1549.), which transfers the main part of online optimization work to offline stage by incorporating an extra perturbation into the fixed feedback law, is extended to networked control systems to reduce their online computational burden. This article proposes the following: (1) an unified closed‐loop model representation of data loss process and IT2 T–S fuzzy nonlinearities is established under the framework of EOFMPC, which explicitly considers the AET condition; (2) a new design method for the state observer gain is presented based on an ellipsoidal invariant set, so that a smaller bound of estimation error can be obtained through an extra optimization problem; (3) some optimization problems are formulated to design the proposed controllers, and the corresponding EOFMPC algorithm is summarized; (4) by means of the extra matrix partition technique, the online optimization problem subject to one ellipsoidal constraint is transformed into a novel convex form to solve the uncertainty of the estimation error. A simulation example and a few comparative simulation experiments illustrate the effectiveness and availability of the aforementioned approach. [ABSTRACT FROM AUTHOR]

As the penetration rate of wind power systems is rising, which causes the overall system's inertia to decline, the power system's capacity to regulate frequency will be negatively affected. Therefore, this paper investigates the inertia control of doubly fed induction generation, and an energy storage system is installed in the wind farm to respond to the frequency deviation. First, a fuzzy control-based virtual inertia adaptive control strategy is presented. The goal of dynamic adjustment of the virtual inertia coefficient is realized by taking into account the uncertain factors of wind speed and frequency change rate. A recovery strategy based on the energy storage system's level of charge is employed to prevent overcharging and over-discharging of the battery. Then, a weight factor based on frequency deviation is introduced to combine the droop output of the energy storage system with the virtual inertia output of the doubly fed induction generation, and the joint output mode of the wind storage system is determined in each stage of primary frequency regulation. Finally, the simulation verification is performed using the wind storage system simulation model created by MATLAB. The comparison results with other control methods prove that the proposed method is effective. [ABSTRACT FROM AUTHOR]

In this paper, we deal with the global practical exponential stabilization of a class of perturbed Takagi-Sugeno fuzzy control systems. The terms of perturbations are supposed uniformly bounded by some known functions and in certain cases not necessarily smooth. We prove that the solution of the closed-loop system with a linear fuzzy controller converge to a neighborhood of the origin. We use common quadratic Lyapunov function and parallel distributed compensation controller techniques to study the asymptotic behavior of the solutions of fuzzy system. Numerical simulations are given to validate the proposed approach. [ABSTRACT FROM AUTHOR]

The observer design and dynamic output feedback control for a class of nonlinear networked systems are studied in this paper. The model of the networked systems is established by using T-S fuzzy method, and the state observer of the systems is designed when the states of the systems are unknown. On this basis, the sufficient conditions for the exponential stability of the system are explored by using the linear matrix inequality (LMI) method and Lyapunov stability theory. Then, the dynamic output feedback control of the systems is designed by using the observer states, which ensures that the states of the closed-loop systems and the error systems exponentially converge to the origin at the same time. Finally, a simulation example is given to illustrate the feasibility and effectiveness of the design method. [ABSTRACT FROM AUTHOR]

One of the most important issues for FLC systems is the problem of finding the right balance between interpretability and accuracy. For this delicate balance, several methods which can be integrated into fuzzy logic, and tune the fuzzy logic parameters adaptively, have been proposed. One of these popular approaches is the heuristic optimization method. However, in terms of optimization, designing fuzzy logic control is a complex optimization problem that is discrete in terms of rule optimization and numerical in terms of optimization of membership degrees parameters. In this context, heuristic-based adaptive fuzzy control systems focus on either fuzzy rule optimization, weighting fuzzy rules, or parameter optimization. In this paper, unlike the others, an adaptive weighted fuzzy logic control (awFLC) method, which weights the inputs instead of the rules, is proposed. First, the membership degree of each input is calculated. Then, the resultant weight is determined by combining the weighted input membership degrees. For a crisp result, the average of the membership degrees of the resultant weight to the output membership functions is calculated. In awFLC, the interaction between membership functions is achieved by average membership degree, communication between inputs is achieved by the weighting of inputs, and mapping between inputs-outputs is achieved by the resultant weight value. Thus, the approach, which turns into a purely numerical optimization problem, provides convenience for heuristic search. In awFLC, optimal values for input weights and variable parameters are searched by the genetic algorithm. The performance of the method was tested on traction power control, and the results were compared with the ANFIS results. With awFLC, an 8.13% average error was obtained, while ANFIS produced solutions with an average error rate of 8.97%. [ABSTRACT FROM AUTHOR]

Summary: This article copes with the adaptive fixed‐time funnel control problem for a class of nonlinear systems with actuator faults. To deal with the difficulties due to actuator faults, which include both lock‐in‐place and loss‐of‐effectiveness models, an adaptive fault‐tolerant control scheme is proposed. By introducing the fuzzy logic system and backstepping technique, an adaptive fixed‐time controller is developed to ensure that the tracking error converges into a pre‐specified performance funnel within a fixed time, which is independent on the initial state, and in the meantime, the controlled closed‐loop system remains semi‐global fixed‐time bounded stable and all the signals are bounded. Simulation results verify the proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, we present two ILC schemes. The first one is a PD-type iterative learning control with an initial state algorithm to solve the trajectory tracking problem for nonlinear systems with uncertainties and without satisfying the classical resetting condition. λ -norm method is used to prove the asymptotic stability of the closed loop system and the simulation results on perturbed nonlinear system have been given. The second approach is a simple P-type iterative learning fuzzy control scheme to solve the trajectory tracking problem for MIMO nonlinear systems. The control design is applicable to deal with a class of nonlinear systems without satisfying the global Lipschitz continuity condition, for which a fuzzy logic term is added to cope with unknown parameters. In addition, the swarm optimization algorithm is used to design the optimum iterative learning fuzzy control (ILFC). Using Lyapunov theory, the asymptotic stability of the closed loop system is guaranteed over the whole finite time. Finally, an illustrative example on two-link manipulator is provided to illustrate the effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]

This article presents a fault-tolerant tracking control strategy for Takagi–Sugeno fuzzy model-based nonlinear systems which combines integral sliding mode control with adaptive control technique. Two common actuator faults: 1) loss of effectiveness and 2) increased bias input, are considered simultaneously. The fuzzy tracking control system is first established by incorporating the integral term of the output tracking error. Then, an appropriate fuzzy integral switching surface is designed such that the corresponding sliding motion only suffers from the unamplified unmatched disturbance. The solution of the nominal tracking controller can be transformed into a to convex optimization problem. In particular, an adaptive fuzzy sliding mode tracking controller is synthesized to ensure the accessibility of the sliding motion despite the effect of actuator faults and unknown disturbances. Finally, the proposed tracking strategy is verified by applying it to the dynamic positioning control of unmanned marine vehicles. [ABSTRACT FROM AUTHOR]

We introduce a new configuration of a robust and adaptive autopilot system for a model-scale flapping-wing aircraft. The system is specifically designed to achieve high performance flapping angle tracking in the face of large uncertainties. To describe the dynamics of the system, we leverage the benefits of both first principle modeling and data-driven approach (system identification technique). We introduce a high-performance robust and adaptive nonlinear control system by means of a feedback linearization (FL) technique, supported with an interval Type-2 fuzzy system due to its ability to accommodate the footprint-of-uncertainties (FoUs). While the first stage of our nonlinear control system is to cancel some predictable nonlinearities using an FL technique, the second phase of control is to accommodate the existing uncertainties in the system by way of an interval Type-2 fuzzy control technique, e.g., due to imperfect cancelation and modeling errors. This way, the stability and the robustness of the closed-loop control system can be guaranteed. We quantify the relative merit of our hybrid control system with respect to an FL technique, supported with a fixed gain state feedback controller and a Type-1 fuzzy system. Lastly, we also conduct stability analysis of the overall closed-loop control system. [ABSTRACT FROM AUTHOR]

This paper presents the novel design of an ambidextrous robot arm that offers double range of motion as compared to dexterous arms. The arm is unique in terms of design (ambidextrous feature), actuation (use of two different actuators simultaneously: Pneumatic Artificial Muscle (PAM) and Electric Motors)) and control (combined use of Proportional Integral Derivative (PID) with Neural Network (NN) and Multiple Adaptive Neuro‐fuzzy Inference System (MANFIS) controller with selector block). In terms of ambidextrous robot arm control, a solution based on forward kinematic and inverse kinematic approach is presented, and results are verified using the derived equation in MATLAB. Since solving inverse kinematics analytically is difficult, Adaptive Neuro Fuzzy Inference system (ANFIS) is developed using ANFIS MATLAB toolbox. When generic ANFIS failed to produce satisfactory results due to ambidextrous feature of the arm, MANFIS with a selector block is proposed. The efficiency of the ambidextrous arm has been tested by comparing its performance with a conventional robot arm. The results obtained from experiments proved the efficiency of the ambidextrous arm when compared with conventional arm in terms of power consumption and stability. [ABSTRACT FROM AUTHOR]

This paper is concerned with the adaptive fault-tolerant compensation control problem for a class of Takagi–Sugeno (T–S) fuzzy systems with mismatched parameter uncertainties and actuator faults. A matrix transformation technique is introduced to relax the restriction regarding the input matrices of T–S fuzzy systems considered in the existing results. Moreover, by combining backstepping-like technique and adaptive control method, a new adaptive fault-tolerant control scheme with compensation mechanism is proposed to achieve the desirable control performance. In addition, it is proved that the states of the resulting closed-loop fuzzy system converge to zero asymptotically even in the presence of actuator faults and mismatched parameter uncertainties. Finally, two simulation examples are given to verify the effectiveness of the presented control scheme. [ABSTRACT FROM AUTHOR]

This paper investigates an observer based adaptive double-layer fuzzy control design problem for strict feedback nonlinear systems with prescribed performance and unknown control direction. To this end, a class of T-S fuzzy systems with unknown fuzzy rules are considered, and an adaptive interval type-2 (IT2) fuzzy controller is proposed for these systems. By using the presented control scheme, the unknown functions of the system can be ultimately approximated by the double-layer fuzzy logic systems (FLSs) which contain the first-layer type-1 FLSs and the second-layer IT2 FLSs. A state observer is applied to handle the unmeasurable states, and the Nussbaum gain method is employed to cope with the unknown control direction. In order to guarantee the prescribed performance of the output tracking error, a tracking error transformation method is utilized, and an improved robust adaptive control scheme is proposed based on the backstepping technique. Then, the stability of the closed-loop system and the achievability of the prescribed tracking performance are proved. Finally, simulation results illustrate the efficacy of the proposed control method. [ABSTRACT FROM AUTHOR]

This paper analyses the management process of the vessel traffic control on one-way section on navigable canal with the adaptive time-sequential filter (traffic lights). One-way section on canal significantly decreases waterway capacity and requests special attention in control and regulation of the vessel traffic. The vessel traffic is a stochastic variable, and the vessel traffic control needs to be flexible and adaptive in order to achieve the required traffic flow with minimal delays. On the one-way section, two independent variable vessel flows from opposite directions are encountered, and fixed (predefined) signal plans lead to an increase in vessel delays. An appropriate solution is development of a Fuzzy Control System (FCS) for the vessel traffic control. A control algorithm is designed according to a set of linguistic rules that describes input parameters for the control strategy. The estimated and approximate input parameters are implemented in the algorithm as fuzzy sets. The final result of the developed algorithm is the traffic light scheme (duration of green light for certain direction). The presented control system can be used as an adaptive automatic control system for the vessel traffic control processes on navigable canals or on critical sections of other waterways. [ABSTRACT FROM AUTHOR]

In this research work, a novel fuzzy adaptive control is proposed to achieve a projective synchronization for a class of fractional-order chaotic systems with input nonlinearities (dead-zone together with sector nonlinearities). These master-slave systems under consideration are supposed to be with distinct models, different fractional-orders, unknown models, and dynamic external disturbances. The proposed control law consists of two main terms, namely: a fuzzy adaptive control term for appropriately approximating the uncertainties and a fractional-order variable-structure control term for robustly dealing with these inherent input nonlinearities. A Lyapunov approach is used to derive the updated laws and to prove the stability of the closed-loop control system. At last, a set of computer simulation results is carried out to illustrate and further validate the theoretical findings. [ABSTRACT FROM AUTHOR]

Abstract This paper presents the control scheme of a pseudo-satellite in situations where error constraints, uncertainties, and external disturbances occur. The control scheme of a pseudo-satellite includes planar path following control scheme and altitude control scheme. In order to control the altitude of a pseudo-satellite, the backstepping technique is used to control the quality of air in the air balloon. To deal with the error constrained requirements of a pseudo-satellite's position, a tan-type barrier Lyapunov function (TBLF) is proposed and incorporated with the guidance control schemes. Adaptive fuzzy disturbance observers are presented to estimate uncertainties and external disturbances. Rigorous stability analysis proves that the altitude error of the pseudo-satellite can be uniformly bounded, and the position error of which can be maintained within the range of constrained requirements, while all closed-loop signals are uniformly bounded regardless of uncertainties and disturbances. [ABSTRACT FROM AUTHOR]

An effort is made to design the fuzzy proportional-derivative (PD) plus I controller for a nonlinear cruise control system in automobiles, which provides adaptive capability in set-point tracking performance. A cruise control system has been considered as a nonlinear first order plus delay time model to exhibit the control behaviour involved in both conventional proportional-integral-derivative (PID) control and fuzzy control. The paper demonstrates the design of fuzzy PD plus I controller including comparative investigation with control structures like PID, I - PD, and PI - D using Simulink modelling. Real-time implementation has been carried out on a robotic prototype using Arduino UNO micro-controller. Based on the performance measures such as integral absolute error (IAE) and integral squared error (ISE), the proposed fuzzy PD plus I structure shows superior performance on servo and regulatory problems in the cruise control system. This control structure avoids integral windup issues and to suppress the derivative kick in the cruise control system. [ABSTRACT FROM AUTHOR]

SUMMARY In this paper, a fuzzy adaptive backstepping control law that combines mass observer and fuzzy control strategy with backstepping control is developed for the trajectory tracking control of a quadrotor unmanned aerial vehicle. In the control law, the mass change disturbance to the flight control could be restrained by the mass observer to estimate the real‐time mass and correct the mass parameter of the quadrotor, and the time‐varying external disturbance to the flight control could be restrained by fuzzy compensation of the fuzzy control law. Lyapunov stability theorem is used to analyze the stability of the closed‐loop control system. Two cases of trajectory tracking numerical simulations are presented in MATLAB/SIMULINK simulation environment. In addition, the quadrotor hovering experiment was completed on the actual flight experimental system with the time‐varying disturbance from wind outdoors. The simulation and actual flight results illustrate that the proposed control strategy is effective and stable under the simulating environment and the actual environment. [ABSTRACT FROM AUTHOR]

Abstract The isolator based on magnetorheological elastomers (MREs) that is used with semi-active controllers has emerged as a kind of smart devices that could potentially improve vibration control in traditional systems. The nonlinear damping characteristics of the isolator make the controller design more difficult. Generally, the command current/voltage is determined according to the relative responses in conventional semi-active controllers. However, these controllers may exhibit unsatisfactory isolation performance and even cause instability owing to significant excitations or nonlinear effects. In this study, the design of the new semi-active controller for an MRE-based isolator was investigated to overcome the drawbacks of traditional controllers from two perspectives. Firstly, an inverse model is designed for the isolator so that it can be used to predict an appropriate electric current supplied to the electromagnet based on the desired control force. Secondly, a robust adaptive controller for semi-active control is proposed for a nonlinear system with unknown dynamic parameters. The control scheme consists of three parts: a standard adaptive linearizing controller, an adaptive sliding mode controller, and a single robust controller. The proposed method guarantees zero convergence of the displacement response and provides robust stability. In addition, the singularity problem that usually appears in standard adaptive control is eliminated. Simulations demonstrate that the proposed controller exceeds the performance of the passive system as assessed in the protection of a two-story shear building during seismic events. Highlights • An inverse MRE model for the isolator is designed. • A robust adaptive controller for semi-active control is proposed for uncertain dynamic system. • The proposed controller overcomes the drawbacks of the conventional semi-active controller. • The proposed controller works remarkably better than the fuzzy controller in seismic responses. [ABSTRACT FROM AUTHOR]

Abstract This paper focuses on the problem of adaptive funnel control for strict-feedback nonlinear systems with unmodeled dynamics. To present a control scheme with prescribed performance bounds on tracking errors, an improved funnel error transformation is introduced and merged into the controller design. Fuzzy logic systems are employed to handle uncertain nonlinear functions, an adaptive fuzzy funnel controller is constructed via backstepping. It is proven that the presented controller ensures that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded and the tracking error evolves within a pre-specified performance funnel. The developed control method is verified through one numerical example. [ABSTRACT FROM AUTHOR]

This work presents a novel adaptive fuzzy controller based on the inversely fuzzified values associated with the H infinity technique and it is applied to vibration control of a vehicle seat suspension system installed with a magneto-rheological damper. In this work, the fuzzified values are embedded into the Riccati-like equation to achieve enhanced robustness of control function and the sliding surface function of sliding mode control is used for designing the main control function. The synthetic function of controller includes a model of online interval type 2 fuzzy neural network system and a fast calculation for clustering. The combination between the fuzzy model system and the proposed control algorithm can shorten the calculation time and improve control responses of the system in the presence of disturbances and uncertainties. After formulating the controller, the effectiveness is validated through both simulation and experiment. In computer simulation, three different road profiles of random bump, regular bump, and random step wave are adopted to emulate severe external disturbances. It is shown from the simulation that both displacement and acceleration of the driver are remarkably well controlled under three different road conditions. This advantage is also validated in the experimental work. It is demonstrated that vibration control performances under random step wave road are much better than the comparative adaptive fuzzy controller. It is identified from the results that the use of the inversely fuzzified values plays a major role to enhance the robustness and hence better control responses in the presence of the disturbances. [ABSTRACT FROM AUTHOR]

Driving condition adaptive control is an effective vehicle fuel-saving technique, and the key challenge lies in improving the recognition accuracy of current driving condition. The state-of-the-art approach is based on recognizing historical driving data with a fixed length sliding window to detect current driving condition. However, few research has been conducted to directly recognize the occurring micro-trip (a speed time series between two starts). That is because at the beginning stage of an occurring micro-trip, its known speed time series is too short to be correctly recognized. In this paper, we addressed this issue by proposing a hybrid method for the occurring micro-trip recognition, and two efforts are made to improve recognition accuracy. First, a hybrid recognition procedure is proposed, which combines the Markov chain prediction model and the fuzzy classification model. Second, a statistic approach is proposed to estimate the best time to switch between above-mentioned two models to achieve higher accuracy in detecting current driving condition. Our evaluation results on real-world driving data show that our proposed solution has better accuracy than the state-of-the-art approach. [ABSTRACT FROM AUTHOR]

A variable-adaptive-law control algorithm for application to common problems like multi-objective control, actuator output constraints, and suboptimal adaptive laws is proposed in this paper. The multi-objective control problem of a nonlinear suspension is converted to the constrained stability problem of a sprung mass using a quarter nonlinear-suspension model. A variable-adaptive-law controller is then used, along with feedback from the output error, and considering the constraints of the actuator output. The controller modifies the adaptive law to reduce the active control force and restores it to the unsaturated zone. This ensures that the suspension system is always in a controlled state when the output saturation occurs. The controller was simulated for the following two cases: (i) a bump road and (ii) a C-grade road. The analysis is verified by experiments in the end. [ABSTRACT FROM AUTHOR]

In traditional multi-infeed AC/DC transmission systems, decentralized and coordinated controllers are usually used to achieve AC/DC transmission control without considering the state and output of the system. Therefore, it cannot reasonably regulate the state of output based on the demand of multi-target control, which leads to poor control effect and weak adaptability. Therefore, a multi- sliding mode adaptive fuzzy controller is designed for the multi-infeed AC/DC transmission system. When the controller is designed, the state equation and the output equation of the multi-infeed AC/DC transmission system are considered. Based on the three different design parameters and the multi-sliding mode surface of the thickness of the saturated layer, the adaptive controller based on multi-sliding mode is designed. This controller is used to set up the dynamic characteristics of some observable measurements in the multi-infeed AC/DC transmission system. Based on the setting results, the results of the comprehensive decision of the system are obtained by the adaptive fuzzy controller. According to the results of a comprehensive decision, the disturbance degree of feedback point is judged. Through the fuzzy algorithm based onthe second component function, the weighting matrices of the output feedback gain matrix are modified, so that the optimal control feedback gain is variable gain, to ensure that the control effect of the system meet the multi-objective control of engineering, and realizing the multi-sliding mode adaptive fuzzy controller ofthe multi-infeed AC/DC transmission system. The experimental results show that the designed multi-sliding mode adaptive fuzzy controller has good control effect on multi-infeed AC/DC transmission system, and has strong adaptability, and it can improve the dynamic performance of the system. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of an adaptive fuzzy event-triggered control (ETC) for uncertain multi-input and multi-output nonlinear systems. To reduce the communication burden of the network control systems, a novel state-dependent event-triggering condition is designed to decide when to update the controllers. By combining the backstepping and event-trigged techniques, the adaptive fuzzy ETC strategies are developed and the resulting closed-loop system is semi-global bounded. Finally, the analytical results are substantiated using simulation studies. [ABSTRACT FROM AUTHOR]

In this paper, an intelligent system use an adaptation fuzzy controller using Mamdani algorithm modified by relation models for a two wheeled self balancing robot is developed. Hardware model of the robot and sensor signal processing are described. The signals from sensors are filtered by a discrete complementary filter. A mathematical model of the robot is derived based on Newtonian mechanics. The proposed control system comprises two loops for regulation of the pitch angle and tracking the desired position of the robot. The inner loop uses a PD controller for position tracking. The outer loop is designed with an adaptive fuzzy controller to regulate balancing of the robot. The proposed controllers are tested in simulations using the mathematical model. These controllers are also designed and implemented in the real time system using a STM32F4 DISCOVERY kit which is equipped with a 32-bit ARM7 microprocessor. Simulations and experimental results show advantages of the adaptation fuzzy controller. Using the adaptive fuzzy controller for stability of the robot will allow a more effective and robust control to be implemented. [ABSTRACT FROM AUTHOR]

Traffic congestion has become a serious phenomenon in the cities. In order to achieve the effective control of intersections, multi-lane four-phase intersection is studied. The corresponding queue length model and vehicular delay model are established. Aiming at the dynamic uncertainty problem in the intersection, a type-2 fuzzy logic controller is designed. The green time of each phase is dynamically decided according to the real-time traffic information for purpose of achieving the smallest vehicular average delay, so as to enhance the traffic efficiency in the intersection. The excellent performance of the designed controller is confirmed through simulation experiments under different conditions. Finally, in view of the difficulty of parameter settings in type-2 fuzzy controller, DNA evolutionary algorithm is applied to online optimize and adjust the parameters of membership function. One group of parameters is difficult to fit all traffic situations, so on-line optimization and adjustment is necessary for reflecting the real-time change of traffic flow in time, which is of great significance for the practical application. The experimental results indicate that the online optimized type-2 fuzzy traffic control method has better effect. [ABSTRACT FROM AUTHOR]

This paper investigates the finite-time H ∞ control problem for switched fuzzy systems using the edge-dependent average dwell time switching. A dynamic event-triggered mechanism is adopted to alleviate communication pressure. An adaptive law is set up to adjust the threshold on-line, which deeply affects the triggering times. It is rigorously proved that the Zeno behavior is excluded. A set of dynamic event-triggered controllers and edge-dependent average dwell time switching signals are co-designed to achieve desired performance. Finally, a chemical reaction example is given to validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Summary: This paper investigates an adaptive fuzzy control method for accommodating actuator faults in a class of uncertain stochastic nonlinear systems with both immeasurable states and unmodeled dynamics. The considered faults are modeled as both loss of effectiveness and lock‐in‐place. To deal with the immeasurable states, a novel state observer containing the actuator faults is designed. Combining with the backstepping technique and stochastic small‐gain theorem, an adaptive fuzzy output feedback control method is developed. The presented design scheme can guarantee that the closed‐loop system is input‐to‐state practically stable in probability. Finally, a simulation example is shown to verify the effectiveness of the proposed control method. [ABSTRACT FROM AUTHOR]

This paper investigates the consensus tracking problem of nonlinear multi-agent systems with state constraints and unknown disturbances. An observer is presented for the case that the states of each follower and its neighbors are unmeasurable. State constraint for multi-agent systems is a challenging problem. Barrier Lyapunov functions are applied in this paper to deal with this difficulty. Then based on adaptive back-stepping control approach and dynamic surface control technique, an adaptive fuzzy distributed controller is proposed to guarantee that the tracking errors between all followers and the leader converge to a small neighborhood of the origin. Moreover, it is proved that all the signals in the multi-agent systems are semi-globally uniformly ultimately bounded (SUUB). Finally, some numerical simulation results are presented to testify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a new mechatronics index is proposed for the evaluation of the alternatives in the conceptual design phase. The criteria aggregated to the index are acquired mostly by the collective knowledge presented in the Multi Annual Roadmap for robotics in Europe and adapted by considering the recent advancements in mechatronics. The mathematical formulation of this design index is based on the discrete Choquet integral, taking into account the interactions among the criteria. An application of the proposed mechatronics index to the design of an educational firefighting robot is presented as a case study to demonstrate the support to the designers in the conceptual design evaluation process. [ABSTRACT FROM AUTHOR]

This article proposes a comparative method to assess the performance of artificial neural network’s direct inverse control (DIC-ANN) with the PID control system. The comparison served as an analysis tool to assess the advantages of DIC-ANN over conventional control method for a UAV attitude controller. The development of ANN method for UAV control purposes arises due to the limitations of the conventional control method, which is the mathematical based model, involving complex expression, and most of them are difficult to be solved directly into analytic solution. Although the linearization simplified the solving process for such mathematical based model, omitting the nonlinear and the coupling terms is unsuitable for the dynamics of the multirotor vehicle. Thus, the DIC-ANN perform learning mechanism to overcome the limitation of PID tuning. Therefore, the proposed comparative method is developed to obtain conclusive results of DIC-ANN advantages over the linear method in UAV attitude control. Better achievement in the altitude dynamics was attained by the DIC-ANN compared to PID control method. [ABSTRACT FROM AUTHOR]

Although the proton exchange membrane fuel cell (PEMFC) is still attracting enormous R&D interest because of its high energy density, its commercialization is hampered by many challenges including cutting cost, improving performance and increasing durability. While they could be solved by material selection, the durability of PEMFC is also affected by voltage reversals and fuel starvation. In this paper, PEMFC control sub-systems namely the reaction, thermal, water management and power electronic subsystems are reviewed critically, with special attention on control strategies to avoid fuel starvation. Classical proportional integral and derivative (PID) controllers are commonly used in feedback voltage control and feed-forward current control by manipulating hydrogen and air flow rates. Self-tuning PID controllers or sliding mode controllers adapt to changing dynamics and respond faster. Adaptive controllers (AC) such as load governors and extremum seeking controllers update control action continuously. Model predictive control (MPC) uses a PEMFC model to predict system behavior and update controller action. Recently, artificial intelligence such as neural network control (NNC), fuzzy logic control (FLC) and FLC-PID control have been used in PEMFC system control because they are simpler and cheaper to implement without heavy computational burden of the AC and MPC but produce better results. [ABSTRACT FROM AUTHOR]

This paper considers the problem of adaptive output feedback tracking control for a class of nonstrict-feedback nonlinear systems with unknown time-varying delays and unknown backlash-like hysteresis. Fuzzy logic systems are used to estimate the unknown nonlinear functions. Based on the LyapunovKrasovskii method, the control scheme is constructed by using the backstepping and adaptive technique. The proposed adaptive controller guarantees that all the closed-loop signals are semiglobally uniformly ultimately bounded and the tracking error can converge to a small neighborhood of the origin. Finally, Simulation results further show the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Future power systems control will require large-scale activation of reserves at distribution level. Despite their high potential, distributed energy resources (DER) used for frequency control pose challenges due to unpredictability, grid bottlenecks, etc. To deal with these issues, this study presents a novel strategy of power frequency characteristic dynamic adjustment based on the imbalance state. This way, the concerned operators become aware of the imbalance location but also a more accurate redistribution of responsibilities in terms of reserves activations is achieved. The proposed control is based on the concept of "cells" which are power systems with operating capabilities and responsibilities similar to control areas (CAs), but fostering the use of resources at all voltage levels, particularly distribution grids. Control autonomy of cells allows increased RES hosting. In this study, the power frequency characteristic of a cell is adjusted in real time by means of a fuzzy controller, which curtails part of the reserves, in order to avoid unnecessary deployment throughout a synchronous area, leading to a more localised activation and reducing losses, congestions and reserves exhaustion. Simulation tests in a four-cell reference power system prove that the controller significantly reduces the use of reserves without compromising the overall stability. [ABSTRACT FROM AUTHOR]

This paper is concerned with the adaptive fuzzy control problem for a class of twin-roll strip casting systems. By using fuzzy logic systems (FLSs) to approximate the compounded nonlinear functions, a novel robust output tracking controller with adaptation laws is designed based on the high gain observer. First, the nonlinear dynamic equations for the roll gap and the molten steel level are constructed, respectively. Then, the mean value theorem is employed to transform the nonaffine nonlinear systems to the corresponding affine nonlinear systems. Moreover, it is also proved that all the closed-loop signals are bounded and the systems output tracking errors can converge to the desired neighborhoods of the origin via the Lyapunov stability analysis. Finally, simulation results, based on semiexperimental system dynamic model and parameters, are worked out to show the effectiveness of the proposed adaptive fuzzy design method. [ABSTRACT FROM AUTHOR]

The Electric power steering (EPS) system, a typical non-linear system, is easy to be influenced by parameters perturbation and disturbance of the road. Traditional linear control method based on a simplified linear model such as PID control cannot reach good dynamic performance. To reduce the influence of parameters perturbation and disturbance of the road and enhance the robustness of the system, an Adaptive fuzzy sliding mode control (AFSMC) method is proposed in this paper. First, fuzzy sliding mode control is employed to enhance the dynamic performance of the system. Then, to improve the precision of the controller, genetic algorithm is used to optimize the control rules which are essential to fuzzy control. The experimental results on non-linear EPS model demonstrate that AFSMC is more stable than Sliding mode control (SMC) method and more efficient to the non-linear system than SFPID control method. They can also prove that AFSMC can provide a stable driving in the presence of parameters perturbation and disturbance of the road. [ABSTRACT FROM AUTHOR]

Intelligent and adaptive control systems are naturally suitable to deal with dynamic uncertain systems with non-smooth nonlinearities; they constitute an important advantage over conventional control approaches. This control technology can be used to design powerful and robust controllers for complex vibration engineering problems such as vibration control of civil structures. Fuzzy logic based controllers are simple and robust systems that are rapidly becoming a viable alternative for classical controllers. Furthermore, new control devices such as magnetorheological (MR) dampers have been widely studied for structural control applications. In this paper, we design a semi-active fuzzy controller for MR dampers using an adaptive neuro-fuzzy inference system (ANFIS). The objective is to verify the effectiveness of a neuro-fuzzy controller in reducing the response of a building structure equipped with a MR damper operating in passive and semi-active control modes. The uncontrolled and controlled responses are compared to assess the performance of the fuzzy logic based controller. [ABSTRACT FROM AUTHOR]

In this paper, the fault-tolerant control (FTC) problem is investigated for a class of multi-input multiple output nonlinear systems with time-varying delays, and an active FTC method is proposed. The controlled system contains unknown nonlinear functions, unknown control gain functions and actuator faults, which integrates time-varying bias and gain faults. Then, fuzzy logic systems are used to approximate the unknown nonlinear functions and unknown control gain functions, fuzzy adaptive observers are used for fault detection and isolation. Further, based on the obtained information, an accommodation method is proposed for compensating the actuator faults. It is shown that all the variables of the closed-loop system are semi-globally uniformly bounded, the tracking error converges to an arbitrary small neighbourhood of the origin. A simulation is given to demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper investigates observer-based adaptive fuzzy control for a class of delayed nonlinear systems in pure-feedback form. We first proposed a linear observer to get the estimation of the system's state. And then utilise fuzzy logic systems to model those nonlinearities. Further, adaptive fuzzy approach and backstepping technique are combined to develop the desired controller via Lyapunov analysis. The suggested adaptive fuzzy control scheme ensures that all the signals of the resulting nonlinear system converge to a small neighbourhood of the origin. Then a real simulation is used to illustrated the effectiveness of our results. [ABSTRACT FROM AUTHOR]

This paper proposes a fuzzy adaptive prescribed performance control scheme for a class of uncertain chaotic systems with unknown control gains and unknown dead-zone inputs. Firstly, an error transformation is introduced to transform the original constrained system into an equivalent unconstrained one. Then, based on the error transformation technique and the predefined performance technique, a fuzzy adaptive feedback control method is developed. It is shown that all the signals of the resulting closed-loop system are bounded. Finally, an illustrative example is given to demonstrate the effectiveness and usefulness of the proposed technique. [ABSTRACT FROM AUTHOR]