Novel Adaptive Memory Event-Triggered-Based Fuzzy Robust Control for Nonlinear Networked Systems via the Differential Evolution Algorithm

This article mainly investigates the fuzzy optimization robust control issue for nonlinear networked systems characterized by the interval type-2(IT2)fuzzy technique under a differential evolution algorithm.To provide a more reasonable utilization of the constrained communication channel,a novel adaptive memory event-triggered(AMET)mechanism is developed,where two event-triggered thresholds can be dynamically adjusted in the light of the current system information and the transmitted historical data.Sufficient conditions with less conservative design of the fuzzy imperfect premise matching(IPM)controller are presented by introducing the Wirtinger-based integral inequality,the information of membership functions(MFs)and slack matrices.Subsequently,under the IPM policy,a new MFs intelligent optimization technique that takes advantage of the differential evolution algorithm is first provided for IT2 TakagiSugeno(T-S)fuzzy systems to update the fuzzy controller MFs in real-time and achieve a better system control effect.Finally,simulation results demonstrate that the proposed control scheme can obtain better system performance in the case of using fewer communication resources.

Fuzzy Proportional Integral Derivative control of a voice coil actuator system for adaptive deformable mirrors

Research on adaptive deformable mirror technology for voice coil actuators(VCAs)is an important trend in the development of large ground-based telescopes.A voice coil adaptive deformable mirror contains a large number of actuators,and there are problems with structural coupling and large temperature increases in their internal coils.Additionally,parameters of the traditional proportional integral derivative(PID)control cannot be adjusted in real-time to adapt to system changes.These problems can be addressed by introducing fuzzy control methods.A table lookup method is adopted to replace real-time calculations of the regular fuzzy controller during the control process,and a prototype platform has been established to verify the effectiveness and robustness of this process.Experimental tests compare the control performance of traditional and fuzzy proportional integral derivative(Fuzzy-PID)controllers,showing that,in system step response tests,the fuzzy control system reduces rise time by 20.25%,decreases overshoot by 78.24%,and shortens settling time by 67.59%.In disturbance rejection experiments,fuzzy control achieves a 46.09%reduction in the maximum deviation,indicating stronger robustness.The Fuzzy-PID controller,based on table lookup,outperforms the standard controller significantly,showing excellent potential for enhancing the dynamic performance and disturbance rejection capability of the voice coil motor actuator system.

A finite-time fuzzy adaptive output-feedback fault-tolerant control for underactuated wheeled mobile robots systems

This paper investigates the adaptive fuzzy finite-time output-feedback fault-tolerant control (FTC) problemfor a class of nonlinear underactuated wheeled mobile robots (UWMRs) system with intermittent actuatorfaults. The UWMR system includes unknown nonlinear dynamics and immeasurable states. Fuzzy logic systems(FLSs) are utilized to work out immeasurable functions. Furthermore, with the support of the backsteppingcontrol technique and adaptive fuzzy state observer, a fuzzy adaptive finite-time output-feedback FTC scheme isdeveloped under the intermittent actuator faults. It is testifying the scheme can ensure the controlled nonlinearUWMRs is stable and the estimation errors are convergent. Finally, the comparison results and simulationvalidate the effectiveness of the proposed fuzzy adaptive finite-time FTC approach.

Implementation of Fuzzy Logic Control into an Equivalent Minimization Strategy for Adaptive Energy Management of A Parallel Hybrid Electric Vehicle

As government agencies continue to tighten emissions regulations due to the continued increase in greenhouse gas production, automotive industries are seeking to produce increasingly efficient vehicle technology. Hybrid electric vehicles (HEVs) have been introduced to mitigate problems while improving fuel economy. HEVs have led to the demand of creating more advanced controls software to consider multiple components for propulsive power in a vehicle. A large section in the software development process is the implementation of an optimal energy management strategy meant to improve the overall fuel efficiency of the vehicle. Optimal strategies can be implemented when driving conditions are known a prior. The Equivalent Consumption Minimization Strategy (ECMS) is an optimal control strategy that uses an equivalence factor to equate electrical to mechanical power when performing torque split determination between the internal combustion engine and electric motor for propulsive and regenerative torque. This equivalence factor is determined from offline vehicle simulations using a sensitivity analysis to provide optimal fuel economy results while maintaining predetermined high voltage battery state of charge (SOC) constraints. When the control hierarchy is modified or different driving styles are applied, the analysis must be redone to update the equivalence factor. The goal of this work is to implement a fuzzy logic controller that dynamically updates the equivalence factor to improve fuel economy, maintain a strict charge sustaining window of operation for the high voltage battery, and reduce computational time required during algorithm development. The adaptive algorithm is validated against global optimum fuel economy and charge sustaining results from a sensitivity analysis performed for multiple drive cycles. Results show a maximum fuel economy improvement of 9.82% when using a mild driving style and a 95% success rate when maintaining an ending SOC within 5% of the desired SOC regardless of starting SOC.

An Adaptive Neuro-Fuzzy Inference System to Improve Fractional Order Controller Performance

The design and analysis of a fractional order proportional integral deri-vate(FOPID)controller integrated with an adaptive neuro-fuzzy inference system(ANFIS)is proposed in this study.Afirst order plus delay time plant model has been used to validate the ANFIS combined FOPID control scheme.In the pro-posed adaptive control structure,the intelligent ANFIS was designed such that it will dynamically adjust the fractional order factors(λandµ)of the FOPID(also known as PIλDµ)controller to achieve better control performance.When the plant experiences uncertainties like external load disturbances or sudden changes in the input parameters,the stability and robustness of the system can be achieved effec-tively with the proposed control scheme.Also,a modified structure of the FOPID controller has been used in the present system to enhance the dynamic perfor-mance of the controller.An extensive MATLAB software simulation study was made to verify the usefulness of the proposed control scheme.The study has been carried out under different operating conditions such as external disturbances and sudden changes in input parameters.The results obtained using the ANFIS-FOPID control scheme are also compared to the classical fractional order PIλDµand conventional PID control schemes to validate the advantages of the control-lers.The simulation results confirm the effectiveness of the ANFIS combined FOPID controller for the chosen plant model.Also,the proposed control scheme outperformed traditional control methods in various performance metrics such as rise time,settling time and error criteria.

ADAPTIVE GENETIC ALGORITHM BASED ON SIX FUZZY LOGIC CONTROLLERS

The performance of genetic algorithm(GA) is determined by the capability of search and optimization for satisfactory solutions. The new adaptive genetic algorithm(AGA) is built for inducing suitable search and optimization relationship. The use of six fuzzy logic controllers(6FLCs) is proposed for dynamic control genetic operating parameters of a symbolic-coded GA. This paper uses AGA based on 6FLCs to deal with the travelling salesman problem (TSP). Experimental results show that AGA based on 6FLCs is more efficient than a standard GA in solving combinatorial optimization problems similar to TSP.

INDIRECT ACCELERATED ADAPTIVE FUZZY CONTROLLER

According to a type of normal nonlinear system, an indirect adaptive fuzzy （IAF） controller has been applied to those systems where no accurate mathematical models of the systems under control are available. To satisfy with system performance, an indirect accelerated adaptive fuzzy （IAAF） controller is proposed, and its general form is presented. The general form IAAF controller ensures necessary control criteria and system＇s global stability using Lyapunov Theorem. It has been proved that the close-loop system error converges to a small neighborhood of equilibrium point. The optimal IAAF controller is derived to guarantee the process＇s shortest settling time. Simulation results indicate the IAAF controller make the system more stable, accurate, and fast.

An Adaptive Fuzzy Controller for Trajectory Tracking of Robot Manipulator

In this paper, an adaptive fuzzy control algorithm is proposed for trajectory tracking of an n-DOF robot manipulator subjected to parametric uncertainty and it is advantageous compared to the conventional nonlinear saturation controller. The asymptotic stability of the proposed controller has been derived based on Lyapunaov energy function. The design procedure is straightforward due to its simple fuzzy rules and control strategies. The simulation results show that the present control strategy effectively reduces the control effort with negligible chattering in control torque signals in comparison to the existing nonlinear saturation controller.

Research on Directly Adaptive Fuzzy Algorithm Based ACC Controller

A directly adaptive fuzzy algorithm is applied in vehicle adaptive cruise control system. The basic principle of the adaptive fuzzy algorithm is analyzed. The initial value of the fuzzy based vector is given by the traditional fuzzy membership. Adaptive law of the adjustable parameters 6 is also determined. The directly adaptive fuzzy ACC controller is designed based on Matlab fuzzy toolbox. Matlab-Simulink is adopted to test the function of the adaptive fuzzy ACC controller. The control system is established using a 7 DOF vehicle dynamics model. Simulation results indicate that the principle of the method is correct and it performs well both in cruise and distance keeping.

SOFC temperature evaluation based on an adaptive fuzzy controller

The operating temperature of a solid oxide fuel cell (SOFC) stack is a very important parameter to be controlled, which impacts the performance of the SOFC due to thermal cycling. In this paper, an adaptive fuzzy control method based on an affine nonlinear temperature model is developed to control the temperature of the SOFC within a specified range. Fuzzy logic systems are used to approximate nonlinear functions in the SOFC system and an adaptive technique is employed to construct the controller. Compared with the traditional fuzzy and proportion-integral-derivative (PID) control, the simulation results show that the designed adaptive fuzzy control method performed much better. So it is feasible to build an adaptive fuzzy controller for temperature control of the SOFC.

Adaptive fuzzy compensation based composite nonlinear feedback controller design for robot manipulators

In order to suppress the influence of uncertain factors on robot system and enable an uncertain robot system to track the reference input accurately,a strategy of combining composite nonlinear feedback(CNF)control and adaptive fuzzy control is studied,and a robot CNF controller based on adaptive fuzzy compensation is proposed.The key of this strategy is to use adaptive fuzzy control to approach the uncertainty of the system online,as the compensation term of the CNF controller,and make full use of the advantages of the two control methods to reduce the influence of uncertain factors on the performance of the system.The convergence of the closed-loop system is proved by feedback linearization and Lyapunov theory.The final simulation results confirm the effectiveness of this plan.

Traffic Signals Control with Adaptive Fuzzy Controller in Urban Road Network

An adaptive fuzzy logic controller (AFC) is presented for the signal control of the urban traffic network. The AFC is composed of the signal control system-oriented control level and the signal controller-oriented fuzzy rules regulation level. The control level decides the signal timings in an intersection with a fuzzy logic controller. The regulation level optimizes the fuzzy rules by the Adaptive Rule Module in AFC according to both the system performance index in current control period and the traffic flows in the last one. Consequently the system performances are improved. A weight coefficient controller (WCC) is also developed to describe the interactions of traffic flow among the adjacent intersections. So the AFC combined with the WCC can be applied in a road network for signal timings. Simulations of the AFC on a real traffic scenario have been conducted. Simulation results indicate that the adaptive controller for traffic control shows better performance than the actuated one.

Indirect Adaptive Fuzzy Output Feedback Control with Supervisory Controller for Uncertain Nonlinear Systems

In this paper, an indirect adaptive fuzzy output feedback controller with supervisory mode for a class of unknown nonlinear systems is developed. The proposed approach does not need the availability of the state variables, moreover, a supervisory controller is appended to the adaptive fuzzy controller to force the state to be within the constraint set. Therefore, if the adaptive fuzzy controller cannot maintain the stability, the supervisory controller starts to work to guarantee stability. On the other hand, if the adaptive fuzzy controller works well, the supervisory controller will be de-activated. The overall adaptive fuzzy control scheme guarantees the stability of the whole closed-loop systems. The simulation results confirm the effectiveness of the proposed method.

Direct adaptive fuzzy control based on integral-type Lyapunov function

A new scheme of direct adaptive fuzzy controller for a class of nonlinear systems with unknown triangular control gain structure is proposed. The design is based on the principle of sliding mode control and the approximation capability of the first type fuzzy systems. By introducing integral-type Lyapunov function and adopting the adaptive compensation term of optimal approximation error, the closed-loop control system is proved to be globally stable, with tracking error converging to zero. Simulation results demonstrate the effectiveness of the approach.

Optimization of Adaptive Fuzzy Controller for Maximum Power Point Tracking Using Whale Algorithm

The advantage of fuzzy controllers in working with inaccurate and nonlinear inputs is that there is no need for an accurate mathematical model and fast convergence and minimal fluctuations in the maximum power point detector.The capability of online fuzzy tracking systems is maximum power,resistance to radiation and temperature changes,and no need for external sensors to measure radiation intensity and temperature.However,the most important issue is the constant changes in the amount of sunlight that cause the maximum power point to be constantly changing.The controller used in the maximum power point tracking(MPPT)circuit must be able to adapt to the new radiation conditions.Therefore,in this paper,to more accurately track the maximumpower point of the solar system and receive more electrical power at its output,an adaptive fuzzy control was proposed,the parameters of which are optimized by the whale algorithm.The studies have repeated under different irradiation conditions and the proposed controller performance has been compared with perturb and observe algorithm(P&O)method,which is a practical and high-performance method.To evaluate the performance of the proposed algorithm,the particle swarm algorithm optimized the adaptive fuzzy controller.The simulation results show that the adaptive fuzzy control system performs better than the P&O tracking system.Higher accuracy and consequently more production power at the output of the solar panel is one of the salient features of the proposed control method,which distinguishes it from other methods.On the other hand,the adaptive fuzzy controller optimized by the whale algorithm has been able to perform relatively better than the controller designed by the particle swarm algorithm,which confirms the higher accuracy of the proposed algorithm.

Adaptive Neuro-fuzzy Controller Design for Non-affine Nonlinear Systems

An adaptive neuro-fuzzy control is investigated for a class of non-affine nonlinear systems.To do so,rigorous description and quantification of the approximation error of the neuro-fuzzy controller are firstly discussed.Applying this result and Lyapunov stability theory,a novel updating algorithm to adapt the weights,centers,and widths of the neuro-fuzzy controller is presented.Consequently,the proposed design method is able to guarantee the stability of the closed-loop system and the convergence of the tracking error.Simulation results illustrate the effectiveness of the proposed adaptive neuro-fuzzy control scheme.

Adaptive Fuzzy Sliding Controller with Dynamic Compensation for Multi-Axis Machining

The precision of multi-axis machining is deeply influenced by the tracking error of multi-axis control system. Since the multi-axis machine tools have nonlinear and time-varying behaviors, it is difficult to establish an accurate dynamic model for multi-axis control system design. In this paper, a novel adaptive fuzzy sliding model controller with dynamic compensation is proposed to reduce tracking error and to improve precision of multi-axis machining. The major ad-vantage of this approach is to achieve a high following speed without overshooting while maintaining a continuous CNC machine tool process. The adaptive fuzzy tuning rules are derived from a Lyapunov function to guarantee stability of the control system. The experimental results on GJ-110 show that the proposed control scheme effectively minimizes tracking errors of the CNC system with control performance surpassing that of a traditional PID controller.

Adaptive Self‐Tuning Fuzzy Controller for a Soft Rehabilitation Machine Actuated by Pneumatic Artificial Muscles

Pneumatic artificial muscles (PAMs) have the highest power to weight and power to volume ratios of any actuator. Therefore, they can be used not only in rehabilitation engineering, but also as actuators in robots, including industrial and therapy robots. Because PAMs have highly nonlinear and time‐varying behavior associated with gas compression and the nonlinear elasticity of bladder containers, achieving excellent tracking performance using classical controllers is difficult. An adaptive self‐tuning fuzzy controller (ASTFC) including adaptive fuzzy sliding mode control (AFSMC) and functional approximation (FA) was developed in this study for overcoming the aforementioned problems. The FA technique was used to release the model‐based requirements and the update laws for the coefficients of the Fourier series function parameters were derived using a Lyapunov function to guarantee control system stability. The experimental results verified that the proposed approach can achieve excellent control performance despite external disturbance.

FUZZY ADAPTIVE CONTROL OF FLEXIBLE-LINK ROBOT MANIPULATOR

A fuzzy adaptive control method is proposed for a flexible robot manipulator. Due to the structure characteristics of the flexible manipulator, the vibration modes must be controlled to realize the high-precision tip position. The Lagrangian principle is utilized to model the dynamic function of the single-degree flexible manipulator incorporating the assumed modes method. Simulation results of the fuzzy adaptive control method in the location control and the trajectory tracking with different tip disturbances are presented and compared with the results of the classic PD control. It shows that the controller can obtain the stable and robust performance.

Small unmanned helicopter's attitude controller by an on-line adaptive fuzzy control system

Since small unmanned helicopter flight attitude control process has strong time-varying characteristics and there are random disturbances,the conventional control methods with unchanged parameters are often unworkable.An on-line adaptive fuzzy control system(AFCS)was designed,in a way that does not depend on a process model of the plant or its approximation in the form of a Jacobian matrix.Neither is it necessary to know the desired response at each instant of time.AFCS implement a simultaneous on-line tuning of fuzzy rules and output scale of fuzzy control system.The two cascade controller design with an inner(attitude controller)and outer controller(navigation controller)of the small unmanned helicopter was proposed.At last,an attitude controller based on AFCS was implemented.The flight experiment showed that the proposed fuzzy logic controller provides quicker response,smaller overshoot,higher precision,robustness and adaptive ability.It satisfies the needs of autonomous flight.