Fuzzy Control for Nonlinear Uncertain Electrohydraulic Active Suspensions With Input Constraint

This paper presents a Takagi-Sugeno (T-S) model-based fuzzy control design approach for electrohydraulic active vehicle suspensions considering nonlinear dynamics of the actuator, sprung mass variation, and constraints on the control input. The T-S fuzzy model is first applied to represent the nonlinear uncertain electrohydraulic suspension. Then, a fuzzy state feedback controller is designed for the obtained T-S fuzzy model with optimized H _infin performance for ride comfort by using the parallel-distributed compensation (PDC) scheme. The sufficient conditions for the existence of such a controller are derived in terms of linear matrix inequalities (LMIs). Numerical simulations on a full-car suspension model are performed to validate the effectiveness of the proposed approach. The obtained results show that the designed controller can achieve good suspension performance despite the existence of nonlinear actuator dynamics, sprung mass variation, and control input constraints.     

引入乘客动力学的1/2车辆液压主动悬挂系统建模与控制

建立了引入乘客动力学以及考虑液压装置动态特性的1/2车辆非线性主动悬挂模型，并在此模型的基础上提出了线性二次型最优控制与非线性Backstepping方法相结合的控制策略．仿真结果验证了该控制策略的有效性．     

Vibration control of a nonlinear quarter-car active suspension system by reinforcement learning

This article presents the investigation of performance of a nonlinear quarter-car active suspension system with a stochastic real-valued reinforcement learning control strategy. As an example, a model of a quarter car with a nonlinear suspension spring subjected to excitation from a road profile is considered. The excitation is realised by the roughness of the road. The quarter-car model to be considered here can be approximately described as a nonlinear two degrees of freedom system. The experimental results indicate that the proposed active suspension system suppresses the vibrations greatly. A simulation of a nonlinear quarter-car active suspension system is presented to demonstrate the effectiveness and examine the performance of the learning control algorithm.     

Nonlinear H/sub /spl infin// controllers for electromagnetic suspension systems

This note presents a unified framework to derive nonlinear H/sub /spl infin// state and output feedback controllers for magnetically levitated (Maglev) vehicles with controlled dc electromagnets, referred to as electromagnetic suspension systems. The theoretical exposition, based on the Taylor series expansion solution to the Hamilton-Jacobi-Isaacs inequality, is followed by an assessment of some of the practical issues in realizing the nonlinear controllers with a digital signal processor and embedded hardware. A select set of experimental results from a single-degree-of-freedom suspension system is included to highlight the effectiveness of the proposed nonlinear stateand output-feedback H/sub /spl infin// controllers to suppress guideway-induced disturbances.     

Fuzzy Sliding Mode Control for Active Suspension System with Proportional Differential Sliding Mode Observer

In this paper, a generalized augmented transformation is considered for the quarter active suspension system with uncertainties. Specifically, the model uncertainties are converted to the augmented states and a new proportion differential sliding mode observer is used to estimate state variables and model uncertainties. A differential geometric method is applied to linearize the nonlinear suspension model. In order to weaken the vibration effect of sliding mode control force and reduce energy consumption, a fuzzy sliding mode controller is designed for the active suspension system and the fuzzy controller is applied to adjust switching control gain according to the reaching condition of sliding mode surface. The simulations are conducted to illustrate the effectiveness and advantages of this proposed observer and control strategy.     

Adaptive backstepping‐based control design for uncertain nonlinear active suspension system with input delay

This paper addresses the control problem of adaptive backstepping control for a class of nonlinear active suspension systems considering the model uncertainties and actuator input delays and presents a novel adaptive backstepping‐based controller design method. Based on the established nonlinear active suspension model, a projector operator–based adaptive control law is first developed to estimate the uncertain sprung‐mass online, and then the desirable controller design and stability analysis are conducted by combining backstepping technique and Lyapunov stability theory, which can not only deal with the actuator input delay but also achieve better dynamics performances and safety constraints requirements of the closed‐loop control system. Furthermore, the relationship between the input delay and the state variables of this vehicle suspension system is derived to present a simple and effective method of calculating the critical input delay. Finally, a numerical simulation investigation is provided to illustrate the effectiveness of the proposed controller.     

多媒体计算机系统软件的核心技术——AVSS的分析与研究

本首先阐述了多媒体计算机系统软件的构成功能和相互之间的关系，指出了ＡＶＳＳ／ＡＶＫ是多媒体计算机系统软件的核心。然后从设计思想、性能特点入手，讨论和分析了ＡＶＳＳ的技术性问题，同时还分别给出了相应的模块结构、任务调度、数据流控制图。     

平面精密磁悬浮轴承的悬浮位置控制

介绍了一种新型平面磁悬浮轴承.该轴承采用3套悬浮线圈实现悬浮,3套推力线圈实现水平驱动.针对其悬浮部分具有参数摄动、多变量、非线性、强耦合的特点,提出一种改进的自抗扰控制器,以克服常规自抗扰控制器非线性误差反馈控制律中非线性函数的不平滑性,并提高了控制品质.仿真对比分析和实验结果表明,应用改进的自抗扰控制器后,平面磁轴承悬浮控制系统具有较好的动、静态特性和鲁棒性.     

Nonlinear dynamic matrix control based on multiple operating models

This paper presents a method of nonlinear dynamic matrix control based on multiple operating models. Its closed-loop stability and asymptotically unbiased tracking properties are proved. The application software (SMART-NLS) is developed using this method, and the simulation results of two nonlinear benchmark processes verify the feasibility and effectiveness of the proposed method.     

Robust passivity-based control design for active nonlinear suspension system

In this article, we propose a novel interconnection and damping assignment passivity-based control (IDA-PBC) design for a quarter car nonlinear active suspension system. As an energy shaping method, IDA-PBC is suitable for applying the main concept of skyhook (SH) control. In addition to the damping term, we utilize the characteristics of the energy shaping method to change the sprung and unsprung masses, thereby strengthening the vibration suppression effect. An IDA-PBC-based controller design for an active suspension system, which includes a nonlinear spring, a nonlinear damper, and mass uncertainty, is proposed. Different from most IDA-PBC applications, which tend to control the position or the velocity, our methods focus on transforming a nonlinear suspension system into a desired linear system with ideal aseismatic properties. Unlike a conventional controller using the SH control strategy, we design a virtual vehicle body and an unsprung mass in addition to the damper coefficients. By deriving the port-Hamiltonian form of the suspension system from its dynamics and rewriting it based on the relative coordinates, we obtain a feedback law that only uses the relative displacement and velocity of the suspension system. We derive the conditions for ensuring the global asymptotical stability of the suspension system and propose the guidelines for parameter selection that can guarantee robust stability against parameter uncertainties.     

Asynchronous Byzantine Agreement with optimal resilience

We present an efficient, optimally-resilient Asynchronous Byzantine Agreement (ABA) protocol involving $n = 3t+1$ parties over a completely asynchronous network, tolerating a computationally unbounded Byzantine adversary, capable of corrupting at most $t$ out of the $n$ parties. In comparison with the best known optimally-resilient ABA protocols of Canetti and Rabin (STOC 1993) and Abraham et al. (PODC 2008), our protocol is significantly more efficient in terms of the communication complexity. Our ABA protocol is built on a new statistical asynchronous verifiable secret sharing (AVSS) protocol with optimal resilience. Our AVSS protocol significantly improves the communication complexity of the only known statistical and optimally-resilient AVSS protocol of Canetti et al. Our AVSS protocol is further built on an asynchronous primitive called asynchronous weak commitment (AWC), while the AVSS of Canetti et al. is built on the primitive called asynchronous weak secret sharing (AWSS). We observe that AWC has weaker requirements than AWSS and hence it can be designed more efficiently than AWSS. The common coin primitive is one of the most important building blocks for the construction of an ABA protocol. In this paper, we extend the existing common coin protocol to make it compatible with our new AVSS protocol that shares multiple secrets simultaneously. As a byproduct, our new common coin protocol is more communication efficient than all the existing common coin protocols.     

Direct adaptive fuzzy backstepping control of uncertain nonlinear systems in the presence of input saturation

In this paper, a novel direct adaptive fuzzy control approach is presented for uncertain nonlinear systems in the presence of input saturation. Fuzzy logic systems are directly used to tackle unknown nonlinear functions, and the adaptive fuzzy tracking controller is constructed by using the backstepping recursive design techniques. To overcome the problem of input saturation, a new auxiliary design system and Nussbaum gain functions are incorporated into the control scheme, respectively. It is proved that the proposed control approach can guarantee that all the signals of the resulting closed-loop system are semi-globally uniformly ultimately bounded (SGUUB), and the tracking error converges to a small neighborhood of the origin. A simulation example is included to illustrate the effectiveness of the proposed approach. Two key advantages of the scheme are that (i) the direct adaptive fuzzy control method is proposed for uncertain nonlinear system with input saturation by using Nussbaum function technique and (ii) The number of the online adaptive learning parameters is reduced.     

Robust Control System Design for an Uncertain Nonlinear System Using Minimax LQG Design Method

A systematic approach to design a nonlinear controller using minimax linear quadratic Gaussian regulator (LQG) control is proposed for a class of multi‐input multi‐output nonlinear uncertain systems. In this approach, a robust feedback linearization method and a notion of uncertain diffeomorphism are used to obtain an uncertain linearized model for the corresponding uncertain nonlinear system. A robust minimax LQG controller is then proposed for reference command tracking and stabilization of the nonlinear system in the presence of uncertain parameters. The uncertainties are assumed to satisfy a certain integral quadratic constraint condition. In this method, conventional feedback linearization is used to cancel nominal nonlinear terms and the uncertain nonlinear terms are linearized in a robust way. To demonstrate the effectiveness of the proposed approach, a minimax LQG‐based robust controller is designed for a nonlinear uncertain model of an air‐breathing hypersonic flight vehicle (AHFV) with flexibility and input coupling. Here, the problem of constructing a guaranteed cost controller which minimizes a guaranteed cost bound has been considered and the tracking of velocity and altitude is achieved under inertial and aerodynamic uncertainties.     

Sliding mode neural network inference fuzzy logic control foractive suspension systems

In the automotive industry, suspension systems are designed to provide desirable vehicle ride and handling properties. This paper presents the development of a robust intelligent nonlinear controller for active suspension systems based on a comprehensive and realistic nonlinear model. The inherent complex nonlinear system model's structure, and the presence of parameter uncertainties, have increased the difficulties of applying conventional linear and nonlinear control techniques. Recently, the combination of sliding mode, fuzzy logic, and neural network methodologies has emerged as a promising technique for dealing with complex uncertain systems. In this paper, a sliding mode neural network inference fuzzy logic controller is designed for automotive suspension systems in order to enhance the ride and comfort. Extensive simulations are performed on a quarter-car model, and the results show that the proposed controller outperforms existing conventional controllers with regard to body acceleration, suspension deflection, and tire deflection     

Adaptive discrete-time sliding-mode control of nonlinear systems described by Wiener models

In this paper, we propose an adaptive control scheme that can be applied to nonlinear systems with unknown parameters. The considered class of nonlinear systems is described by the block-oriented models, specifically, the Wiener models. These models consist of dynamic linear blocks in series with static nonlinear blocks. The proposed adaptive control method is based on the inverse of the nonlinear function block and on the discrete-time sliding-mode controller. The parameters adaptation are performed using a new recursive parametric estimation algorithm. This algorithm is developed using the adjustable model method and the least squares technique. A recursive least squares (RLS) algorithm is used to estimate the inverse nonlinear function. A time-varying gain is proposed, in the discrete-time sliding mode controller, to reduce the chattering problem. The stability of the closed-loop nonlinear system, with the proposed adaptive control scheme, has been proved. An application to a pH neutralisation process has been carried out and the simulation results clearly show the effectiveness of the proposed adaptive control scheme.     

具有非线性扰动多时滞系统的鲁棒故障检测

基于状态观测器的方法研究了一类具有非线性扰动的多重状态时滞系统的鲁棒故障检测问题，应用RBF神经网络逼近系统的非线性扰动，采用线性矩阵不等式(LMI)给出了与时滞上界相关的增益阵设计方法，并利用Lyapunov函数和一致有界引理证明了故障检测残差信号的一致有界稳定条件和对非线性扰动的鲁棒性，仿真示例说明了该方法的有效性。     

Discrete terminal sliding mode repetitive control for a linear actuator with nonlinear friction and uncertainties

A robust discrete terminal sliding mode repetitive controller is proposed for a class of nonlinear positioning systems with parameter uncertainties and nonlinear friction. The terminal sliding mode control (TSMC) part is designed to improve the transient characteristics of the system, as well as the robustness against parameter uncertainties, nonperiodic nonlinearities, and disturbances. The repetitive control (RC) part is then integrated to eliminate the effects of the periodic uncertainties present in the system. Moreover, a pure phase lead compensator is incorporated into the RC to improve the tracking at high frequencies. A robust stability analysis and an analysis of the finite time convergence properties of the proposed controller are also provided in this paper. Simulation testing and an experimental validation using a linear actuator system with nonlinear friction and parameter uncertainties are conducted to verify the effectiveness of the proposed controller.     

Adaptive fuzzy dynamic surface control for uncertain nonlinear systems

In this paper, a robust adaptive fuzzy dynamic surface control for a class of uncertain nonlinear systems is proposed. A novel adaptive fuzzy dynamic surface model is built to approximate the uncertain nonlinear functions by only one fuzzy logic system. The approximation capability of this model is proved and the model is implemented to solve the problem that too many approximators are used in the controller design of uncertain nonlinear systems. The shortage of ＂explosion of complexity＂ in backstepping design procedure is overcome by using the proposed dynamic surface control method. It is proved by constructing appropriate Lyapunov candidates that all signals of closed-loop systems are semi-globally uniformly ultimate bounded. Also, this novel controller stabilizes the states of uncertain nonlinear systems faster than the adaptive sliding mode controller （SMC）. Two simulation examples are provided to illustrate the effectiveness of the control approach proposed in this paper.     

采样非线性系统的神经网络稳定自适应控制

针对一类动力学未知或难以建模的采样非线性系统,提出了一种基于神经网络的跟随控 制器稳定自适应控制方法.控制器采用径向基函数神经网络近似对象的动力学非线性,神经 网络参数的自适应规律由稳定理论得到.文中给出了系统稳定性和跟随误差收敛性的证明, 并通过仿真实例揭示了所提方法的性能.     

An RBFN–Wiener hybrid filter using higher order signal statistics

In this paper we propose a radial basis function network (RBFN) based nonlinear filter with a basic framework of a linear Wiener filter. In addition, in order to improve the filtering performance, we further propose a novel nonlinear filter, which is synthesized by a hybridization of an RBFN filter and a linear Wiener filter. The proposed filters are realized with a least mean square error scheme using higher order statistics of a target signal and an observation noise.The validity and the effectiveness of the proposed filters have been verified by applying them to the actual filtering problems of the noisy images.