Hybrid Dynamic Variables-Dependent Event-Triggered Fuzzy Model Predictive Control

This article focuses on dynamic event-triggered mechanism(DETM)-based model predictive control(MPC) for T-S fuzzy systems.A hybrid dynamic variables-dependent DETM is carefully devised,which includes a multiplicative dynamic variable and an additive dynamic variable.The addressed DETM-based fuzzy MPC issue is described as a “min-max” optimization problem(OP).To facilitate the co-design of the MPC controller and the weighting matrix of the DETM,an auxiliary OP is proposed based on a new Lyapunov function and a new robust positive invariant(RPI) set that contain the membership functions and the hybrid dynamic variables.A dynamic event-triggered fuzzy MPC algorithm is developed accordingly,whose recursive feasibility is analysed by employing the RPI set.With the designed controller,the involved fuzzy system is ensured to be asymptotically stable.Two examples show that the new DETM and DETM-based MPC algorithm have the advantages of reducing resource consumption while yielding the anticipated performance.

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.

Adaptive Fuzzy Dynamic Surface Control of Flexible-Joint Robot Systems With Input Saturation

In this paper, we propose an adaptive fuzzy dynamic surface control(DSC) scheme for single-link flexible-joint robotic systems with input saturation. A smooth function is utilized with the mean-value theorem to deal with the difficulties associated with input saturation. An adaptive DSC design with an auxiliary first-order filter is used to solve the "explosion of complexity"problem. It is proved that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded, and the tracking error eventually converges to a small neighborhood around zero. The main advantage of the proposed method is that only one adaptation parameter needs to be updated,which reduces the computational burden significantly. Simulation results demonstrate the feasibility of the proposed scheme and the comparison results show that the improved DSC method can reduce the computational burden by almost two thirds in comparison with the standard DSC method.

Discounted Iterative Adaptive Critic Designs With Novel Stability Analysis for Tracking Control

The core task of tracking control is to make the controlled plant track a desired trajectory.The traditional performance index used in previous studies cannot eliminate completely the tracking error as the number of time steps increases.In this paper,a new cost function is introduced to develop the value-iteration-based adaptive critic framework to solve the tracking control problem.Unlike the regulator problem,the iterative value function of tracking control problem cannot be regarded as a Lyapunov function.A novel stability analysis method is developed to guarantee that the tracking error converges to zero.The discounted iterative scheme under the new cost function for the special case of linear systems is elaborated.Finally,the tracking performance of the present scheme is demonstrated by numerical results and compared with those of the traditional approaches.

Controlling chaos using Takagi-Sugeno fuzzy model and adaptive adjustment

In this paper, an approach to the control of continuous-time chaotic systems is proposed using the Takagi-Sugeno （TS） fuzzy model and adaptive adjustment. Sufficient conditions are derived to guarantee chaos control from Lyapunov stability theory. The proposed approach offers a systematic design procedure for stabilizing a large class of chaotic systems in the literature about chaos research. The simulation results on Rossler＇s system verify the effectiveness of the proposed methods.

Integrated guidance and control of guided projectile with multiple constraints based on fuzzy adaptive and dynamic surface

Based on fuzzy adaptive and dynamic surface(FADS),an integrated guidance and control(IGC)approach was proposed for large caliber naval gun guided projectile,which was robust to target maneuver,canard dynamic characteristics,and multiple constraints,such as impact angle,limited measurement of line of sight(LOS)angle rate and nonlinear saturation of canard deflection.Initially,a strict feedback cascade model of IGC in longitudinal plane was established,and extended state observer(ESO)was designed to estimate LOS angle rate and uncertain disturbances with unknown boundary inside and outside of system,including aerodynamic parameters perturbation,target maneuver and model errors.Secondly,aiming at zeroing LOS angle tracking error and LOS angle rate in finite time,a nonsingular terminal sliding mode(NTSM)was designed with adaptive exponential reaching law.Furthermore,combining with dynamic surface,which prevented the complex differential of virtual control laws,the fuzzy adaptive systems were designed to approximate observation errors of uncertain disturbances and to reduce chatter of control law.Finally,the adaptive Nussbaum gain function was introduced to compensate nonlinear saturation of canard deflection.The LOS angle tracking error and LOS angle rate were convergent in finite time and whole system states were uniform ultimately bounded,rigorously proven by Lyapunov stability theory.Hardware-in-the-loop simulation(HILS)and digital simulation experiments both showed FADS provided guided projectile with good guidance performance while striking targets with different maneuvering forms.

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.

Indirect adaptive fuzzy control for a class of nonlinear discrete-time systems

An indirect adaptive fuzzy control scheme is developed for a class of nonlinear discrete-time systems. In this method, two fuzzy logic systems are used to approximate the unknown functions, and the parameters of membership functions in fuzzy logic systems are adjusted according to adaptive laws for the purpose of controlling the plant to track a reference trajectory. It is proved that the scheme can not only guarantee the boundedness of the input and output of the closed-loop system, but also make the tracking error converge to a small neighborhood of the origin. Simulation results indicate the effectiveness of this scheme.

Distributed model predictive control based on adaptive sampling mechanism

In this work,an adaptive sampling control strategy for distributed predictive control is proposed.According to the proposed method,the sampling rate of each subsystem of the accused object is determined based on the periodic detection of its dynamic behavior and calculations made using a correlation function.Then,the optimal sampling interval within the period is obtained and sent to the corresponding sub-prediction controller,and the sampling interval of the controller is changed accordingly before the next sampling period begins.In the next control period,the adaptive sampling mechanism recalculates the sampling rate of each subsystem’s measurable output variable according to both the abovementioned method and the change in the dynamic behavior of the entire system,and this process is repeated.Such an adaptive sampling interval selection based on an autocorrelation function that measures dynamic behavior can dynamically optimize the selection of sampling rate according to the real-time change in the dynamic behavior of the controlled object.It can also accurately capture dynamic changes,meaning that each sub-prediction controller can more accurately calculate the optimal control quantity at the next moment,significantly improving the performance of distributed model predictive control(DMPC).A comparison demonstrates that the proposed adaptive sampling DMPC algorithm has better tracking performance than the traditional DMPC algorithm.

Fuzzy Adaptive Tracking Control of Uncertain Strict-Feedback Nonlinear Systems with Disturbances Based on Generalized Fuzzy Hyperbolic Model

In this paper, a fuzzy adaptive tracking control for uncertain strict-feedback nonlinear systems with unknown bounded disturbances is proposed. The generalized fuzzy hyperbolic model (GFHM) with better approximation performance is used to approximate the unknown nonlinear function in the system. The dynamic surface control (DSC) is used to design the controller, which not only avoids the “explosion of complexity” problem in the process of repeated derivation, but also makes the control system simpler in structure and lower in computational cost because only one adaptive law is designed in the controller design process. Through the Lyapunov stability analysis, all signals in the closed loop system designed in this paper are semi-globally uniformly ultimately bounded (SGUUB). Finally, the effectiveness of the method is verified by a simulation example.

Adaptive Neural Network Dynamic Surface Control for Perturbed Nonlinear Time-delay Systems

This paper proposes an adaptive neural network control method for a class of perturbed strict-feedback nonlinear systems with unknown time delays. Radial basis function neural networks are used to approximate unknown intermediate control signals. By constructing appropriate Lyapunov-Krasovskii functionals, the unknown time delay terms have been compensated. Dynamic surface control technique is used to overcome the problem of ＂explosion of complexity＂ in backstepping design procedure. In addition, the semiglobal uniform ultimate boundedness of all the signals in the closed-loop system is proved. A main advantage of the proposed controller is that both problems of ＂curse of dimensionality＂ and ＂explosion of complexity＂ are avoided simultaneously. Finally, simulation results are presented to demonstrate the effectiveness of the approach.

A Fuzzy-Neural Network Control of Nonlinear Dynamic Systems

In this paper, an adaptive dynamic control scheme based on a fuzzy neural network is presented, that presents utilizes both feed-forward and feedback controller elements. The former of the two elements comprises a neural network with both identification and control role, and the latter is a fuzzy neural algorithm, which is introduced to provide additional control enhancement. The feedforward controller provides only coarse control, whereas the feedback controller can generate on-line conditional proposition rule automatically to improve the overall control action. These properties make the design very versatile and applicable to a range of industrial applications.

Compensation for secondary uncertainty in electro-hydraulic servo system by gain adaptive sliding mode variable structure control

Based on consideration of the differential relations between the immeasurable variables and measurable variables in electro-hydraulic servo system,adaptive dynamic recurrent fuzzy neural networks(ADRFNNs) were employed to identify the primary uncertainty and the mathematic model of the system was turned into an equivalent linear model with terms of secondary uncertainty.At the same time,gain adaptive sliding mode variable structure control(GASMVSC) was employed to synthesize the control effort.The results show that the unrealization problem caused by some system's immeasurable state variables in traditional fuzzy neural networks(TFNN) taking all state variables as its inputs is overcome.On the other hand,the identification by the ADRFNNs online with high accuracy and the adaptive function of the correction term's gain in the GASMVSC make the system possess strong robustness and improved steady accuracy,and the chattering phenomenon of the control effort is also suppressed effectively.

A New Fuzzy Adaptive Genetic Algorithm

Multiple genetic algorithms (GAs) need a large population size, which will take a long time for evolution. A new fuzzy adaptive GA is proposed in this paper. This algorithm is more effective in global search while keeping the overall population size constant. The simulation results of function optimization show that with the proposed algorithm, the phenomenon of premature convergence can be overcome effectively, and a satisfying optimization result is obtained.

Optimum Design for the Magnification Mechanisms Employing Fuzzy Logic-ANFIS

To achieve high work performance for compliant mechanisms of motion scope,continuous work condition,and high frequency,we propose a new hybrid algorithm that could be applied to multi-objective optimum design.In this investigation,we use the tools of finite element analysis(FEA)for a magnificationmechanism to find out the effects of design variables on the magnification ratio of the mechanism and then select an optimal mechanism that could meet design requirements.A poly-algorithm including the Grey-Taguchi method,fuzzy logic system,and adaptive neuro-fuzzy inference system(ANFIS)algorithm,was utilized mainly in this study.The FEA outcomes indicated that design variables have significantly affected on magnification ratio of the mechanism and verified by analysis of variance and analysis of the signal to noise of grey relational grade.The results are also predicted by employing the tool of ANFIS in MATLAB.In conclusion,the optimal findings obtained:Its magnification is larger than 40 times in comparison with the initial design,the maximum principal stress is 127.89MPa,and the first modal shape frequency obtained 397.45 Hz.Moreover,we found that the outcomes obtained deviation error compared with predicted results of displacement,stress,and frequency are 8.76%,3.6%,and 6.92%,respectively.

考虑推力损失的运载火箭主动段容错控制

运载火箭上升段易出现推力下降等动力系统故障,传统的控制方法在干扰性较大场合难以满足高精度、高稳定的控制需求。本文进行了运载火箭自适应增广控制与模糊PD控制方法的研究,并分析两者的控制特性;提出一种基于模糊规则的动态切换控制算法,通过计算自适应PD控制与模糊PD控制的输出量加权,得到系统的最优控制方案;将控制力矩重构与动态切换控制算法结合,进行火箭容错控制研究。结果表明,基于模糊规则的动态切换控制算法能有效提高姿态控制品质,相比于其他控制方法,偏航角最大偏差减小20%以上,系统响应时间至少快15%,且控制过程中未出现振荡。

船用起重机四旋翼减摇吊钩动力学特性

【目的】设计一种船用起重机减摇装置并探究其防摆机理,以期解决当前船用起重机吊装过程的摇摆问题。【方法】设计一种船用起重机四旋翼减摇吊钩装置,建立船舶-起重机-四旋翼减摇吊钩的动力学模型,并考虑风载荷的影响。设计自适应模糊比例-积分-微分(PID)控制策略应用于四旋翼减摇吊钩控制器,根据摆角偏差量控制相应电机转动产生抑制吊重摆动的推力,实现摆角在线实时控制。通过动力学仿真分析和实验验证,探究其减摇特性和运动规律。【结果】在船舶激励下吊重面内和面外摆角分别减少约66.8%和69.7%,吊重摆动投影在x和y轴最大偏移量平均降低62.5%。自适应模糊PID控制策略下的四旋翼减摇吊钩较PID控制响应速度提高25.0%,稳态性能提升13.0%。通过实验与仿真对比分析,吊重摆角曲线趋势基本吻合,在16 s时吊重趋近于稳定,验证了四旋翼减摇吊钩动力学模型的准确性。【结论】四旋翼减摇吊钩能有效抑制吊重摆动,可为船用起重机轻载吊装防摆研究提供新的可行性减摇方案。

储能调频控制参数自适应优化与退出机制设计

为解决储能控制参数设置不当引起的频率稳定性问题以及储能调频效果和循环使用寿命难以平衡的问题,提出一种储能调频控制参数自适应优化与退出机制设计方法。首先分析储能下垂、虚拟正(负)惯量系数对系统频率动态性能及稳定性的影响,得到参数稳定变化的范围;然后,根据控制参数稳定变化区间,设计含虚拟负惯量的模糊自适应控制方法,实现控制方式之间的平滑切换和优势互补,加快频率恢复;再则,利用Logistic函数设计考虑储能荷电状态(SOC)的自适应反馈控制策略,同时引入储能退出机制,反馈调节优化储能出力,以期解决储能调频效果和循环使用寿命难以平衡的问题。最后,在Matlab/Simulink仿真平台上进行仿真验证,验证所提策略的有效性及对频率的改善作用。

一种基于Fuzzy-PID控制的组播拥塞控制机制

针对现有实际通用组播(pragmatic general multicast protocol,PGM)拥塞控制方案难以适应网络的动态变化等不足,提出了一种基于模糊比例积分微分(fuzzy proportional plus integral plus derivative,Fuzzy-PID)控制的组播拥塞控制机制(fuzzy-PID-controlled multicast congestion control mechanism,FPIDMCC)。FPIDMCC在源端和接收端代表间运用Fuzzy-PID控制方案,使源端能快速响应网络拥塞,实时调整发送速率并使之趋于稳定,增强了对动态网络的适应性;此外,采用基于代表和中间节点反馈聚集相结合的方式进行反馈控制,可有效避免反馈爆炸。其中Fuzzy-PID控制方案结合了传统PID和模糊推理的优点,由模糊推理得到PID控制参数,减少了对系统模型的依赖性。仿真结果表明,FPIDMCC机制拥塞响应速度快、系统稳定性好、动态适应能力强。