Nonsingular path following control of a unicycle in the presence of parametric modelling uncertainties

A new type of control law is derived to steer the dynamic model of a wheeled robot of unicycle type along a desired path. The methodology adopted for path following control deals explicitly with vehicle dynamics and plant parameter uncertainty. Furthermore, it overcomes stringent initial condition constraints that are present in a number of path following control strategies described in the literature. This is done by controlling explicitly the rate of progression of a ‘virtual target’ to be tracked along the path, thus bypassing the problems that arise when the position of the virtual target is simply defined by the projection of the actual vehicle on that path. In the paper, a nonlinear adaptive control law is derived that yields convergence of the (closed‐loop system) path following error trajectories to zero. Controller design relies on Lyapunov theory and backstepping techniques. Simulation results illustrate the performance of the control system proposed. Copyright © 2006 John Wiley & Sons, Ltd.     

Type-2 fuzzy sliding mode control without reaching phase for nonlinear system

A new sliding mode control (SMC) algorithm for the nth order nonlinear system suffering from parameters uncertainty and subjected to an external perturbation is proposed. The algorithm employs a time-varying switching plane. At the initial time t=t₀, the plane passes through the point determined by the system initial conditions in the error state space. Afterwards, the plane moves to the origin of the state space. Since the nonlinear system is sensible to the perturbations and uncertainties during the reaching phase, the elimination of such phase yields in a considerable amelioration of system robustness. Switching plane is chosen such that: (1) the reaching phase is eliminated, (2) the nonlinear system is insensitive to the external disturbance and the model uncertainty from the initial time (3) the convergence of the tracking error to zero. Furthermore, a Type-2 fuzzy system is used to approximate system dynamics (assumed to be unknown) and to construct the equivalent controller such that: (1) all signals of closed-loop system are uniformly ultimately bounded, (2) the problems related to adaptive fuzzy controllers like singularity and constraints on the control gain are resolved. To ensure the robustness of the overall closed-loop system, analytical demonstration using Lyapunov theorem is considered. Finally, a robot manipulator is considered as a real time system in order to confirm the efficiency of the proposed approach. The experimentation is done for both regulation and tracking control problems.     

基于自适应二阶终端滑模的飞行器再入姿态控制

针对飞行器再入过程中存在着模型不确定性因素以及气动环境复杂等鲁棒控制问题,提出一种基于自适应二阶非奇异终端滑模的控制方案.设计的控制器保证姿态跟踪误差在有限的时间内收敛于零,不需要内外扰的先验知识,通过在线自适应辨识扰动上界以消除其影响.最后以气动参数摄动50%作为扰动条件进行了飞行器再入姿态控制仿真,结果表明了该方案的快速性和鲁棒性.     

基于RISE反馈的串联弹性驱动器最优控制方法

串联弹性驱动器（Series elastic actuator,SEA）是机器人交互系统中的一种理想力源.本文针对非线性SEA的力矩控制问题提出一种基于RISE（Robust integral of the sign of the error）反馈的最优控制方法,能够克服模型参数不确定和有界扰动,实现SEA输出力矩在交互过程中快速平稳地收敛到期望值.具体来说,首先对SEA的模型进行分析和变换;然后假设模型参数和扰动均已知,并在此基础上基于二次型指标设计最优控制律;之后基于RISE反馈重新设计控制律抵消模型参数不确定性和有界扰动,基于Lyapunov理论分析控制器的收敛性和信号的有界性,实验结果表明这种基于RISE反馈的最优控制方法具有良好的控制性能和对有界扰动的鲁棒性.     

Adaptive control of a class of nonlinear discrete-time systemsusing neural networks

Layered neural networks are used in a nonlinear self-tuning adaptive control problem. The plant is an unknown feedback-linearizable discrete-time system, represented by an input-output model. To derive the linearizing-stabilizing feedback control, a (possibly nonminimal) state-space model of the plant is obtained. This model is used to define the zero dynamics, which are assumed to be stable, i.e., the system is assumed to be minimum phase. A linearizing feedback control is derived in terms of some unknown nonlinear functions. A layered neural network is used to model the unknown system and generate the feedback control. Based on the error between the plant output and the model output, the weights of the neural network are updated. A local convergence result is given. The result says that, for any bounded initial conditions of the plant, if the neural network model contains enough number of nonlinear hidden neurons and if the initial guess of the network weights is sufficiently close to the correct weights, then the tracking error between the plant output and the reference command will converge to a bounded ball, whose size is determined by a dead-zone nonlinearity. Computer simulations verify the theoretical result     

State observer based dynamic fuzzy logic system for a class of SISO nonlinear systems

This paper presents an observer based dynamic fuzzy logic system (DFLS) scheme for a class of unknown single-input single-output (SISO) nonlinear dynamic systems with external disturbances. The proposed approach does not need the availability of the state variables. Within this scheme, the DFLS is employed to identify the unknown nonlinear dynamic system. The control law and parameter adaptation laws of the DFLS are derived based on Lyapunov synthesis approach. The control law is robustfied in H _∞ sense to attenuate external disturbance, model uncertainties, and fuzzy approximation errors. It is shown that under appropriate assumptions, it guarantees the boundedness of all the signals in the closed-loop system and the asymptotic convergence to zero of tracking errors. The proposed method is applied to an inverted pendulum system to verify the effectiveness of the proposed algorithms.     

基于DRFNN的共振频率自适应反推控制

针对共振破碎机频率控制系统的不确定性问题,提出基于动态递归模糊神经网络的自适应反推控制策略。建立了破碎机频率控制系统的数学模型,在忽略不确定性项的前提下,设计了基于自适应Back-stepping方法控制律。其次将电液系统中影响频率控制性能的不确定性因素定义为待估计项,采用动态递归模糊神经网络对其进行实时估计,给出了基于动态递归模糊神经网络的参数自适应律,并通过了Lyapunov的稳定性分析。仿真实验和车载测试结果表明,对于系统参数的不确定性,该方法具有较好地频率控制性能。     

Output regulation of uncertain nonlinear systems with nonlinear exosystems

An adaptive control algorithm is proposed for output regulation of uncertain nonlinear systems in output feedback form under disturbances generated from nonlinear exosystems. A new nonlinear internal model is proposed to generate the desired input term for suppression of the disturbances. The proposed internal model design is based on boundedness of the disturbance, high gain design and saturation. It is capable to tackle disturbances in any specified initial conditions. Some uncertainties in the systems are allowed, provided that they do not affect the desired feedforward control term, and they are tackled by using nonlinear dominant functions and an adaptive control coefficient. The proposed control algorithm ensures the global convergence of the state variables to the invariant manifold, which implies that the measurement or the tracking error approaches to zero asymptotically.     

Observer-based adaptive iterative learning control for nonlinear systems with time-varying delays

An observer-based adaptive iterative learning control (AILC) scheme is developed for a class of nonlinear systems with unknown time-varying parameters and unknown time-varying delays. The linear matrix inequality (LMI) method is employed to design the nonlinear observer. The designed controller contains a proportional-integral-derivative (PID) feedback term in time domain. The learning law of unknown constant parameter is differential-difference-type, and the learning law of unknown time-varying parameter is difference-type. It is assumed that the unknown delay-dependent uncertainty is nonlinearly parameterized. By constructing a Lyapunov-Krasovskii-like composite energy function (CEF), we prove the boundedness of all closed-loop signals and the convergence of tracking error. A simulation example is provided to illustrate the effectiveness of the control algorithm proposed in this paper.     

基于并行学习鲁棒自适应的行驶车辆特性参数估计方法研究

针对车辆行驶过程中的特性参数估计问题,基于并行学习思想提出一种鲁棒自适应参数估计方法.通过低通滤波技术,设计一组系统状态和响应函数的一阶滤波变量.结合并行学习,构建特性参数估计的回归向量,并基于参数估计误差向量,设计鲁棒自适应参数更新律.以某型车辆为例,对该方法的有效性进行仿真验证.仿真结果表明,在无/有扰动情形下,该...     

机械臂带角度修正的开闭环迭代学习轨迹跟踪控制

在迭代学习控制研究中, 通常的一个假设是: 系统每次迭代初态与期望初态一致或迭代初态固定. 针对迭代学习控制律在迭代初态的限制下难以应用到机械臂轨迹跟踪控制中的问题, 本文对机械臂系统模型降阶变换, 将其转化为低阶系统. 对于变换设计后的机械臂系统模型, 提出一种带有角度修正的开闭环迭代学习控制算法, 该算法利用误差信号及相邻两次误差的偏差信号对系统控制律进行逐次修正, 与常规P型算法相比, 充分利用了系统已存的和当前的有效信息, 与常规PD型算法相比, 避免了由于微分作用而带来的不稳定影响. 同时, 用输出向量的角度关系作为评估控制输入好坏的标准对所设计的迭代学习律的变化趋势进行“奖-惩”, 从而实现了良好的跟踪效果并具有较快的收敛速度. 本文还针对机械臂系统存在关节转角限位的情况对控制算法进行改进, 以使机械臂在实际运作中真正实时地完成指定工作任务. 仿真结果表明了所提控制策略的有效性.     

Hybrid adaptive robust control of static var compensator in power systems

To improve the transient response of an electric power transmission system, a hybrid adaptive robust control method is proposed in this paper for the static var compensator by incorporating the immersion and invariance adaptive (I&I adaptive) and L₂‐gain control. In contrast to the standard I&I adaptive control algorithm, establishing a target system is not required in constructing the robust control law with the proposed method. Thus, the procedure of solving PDEs to satisfy the immersion condition can be avoided. In addition, both parametric and non‐parametric uncertainties, which commonly exist in electric power transmission systems, are considered. The parametric uncertainty induced by the damping coefficient of the system is estimated by the designed adaptive law, which is constructed by ensuring the estimation error converges to zero. The non‐parametric uncertainty is caused by external disturbances and approximation errors in modeling the uncertain structure. By assuming that the L₂‐gain of the system to the non‐parametric uncertainties satisfies a dissipation inequality, we found that the robustness of the controller can be guaranteed. It is proved that all the system states are globally bounded and converge to a new stable equilibrium. Simulation results are also presented to show the effectiveness of the proposed control method in improving the transient response of the system and the convergence speed of the system states. Copyright © 2013 John Wiley & Sons, Ltd.     

Active Disturbance Rejection Control for Uncertain Nonlinear Systems With Sporadic Measurements

This paper deals with the problem of active disturbance rejection control (ADRC) design for a class of uncertain nonlinear systems with sporadic measurements. A novel extended state observer (ESO) is designed in a cascade form consisting of a continuous time estimator, a continuous observation error predictor, and a reset compensator. The proposed ESO estimates not only the system state but also the total uncertainty, which may include the effects of the external perturbation, the parametric uncertainty, and the unknown nonlinear dynamics. Such a reset compensator, whose state is reset to zero whenever a new measurement arrives, is used to calibrate the predictor. Due to the cascade structure, the resulting error dynamics system is presented in a non-hybrid form, and accordingly, analyzed in a general sampled-data system framework. Based on the output of the ESO, a continuous ADRC law is then developed. The convergence of the resulting closed-loop system is proved under given conditions. Two numerical simulations demonstrate the effectiveness of the proposed control method.      

考虑扰动及模型不确定的无人船路径跟踪控制

针对外界扰动与模型不确定因素影响下的无人船路径跟踪控制问题,引入Serret-Frenet坐标系对无人船的路径跟踪问题进行数学描述,根据给定的期望跟踪路线与当前无人船的位置信息,利用李雅普诺夫直接法设计无人船航行速度与航向角度的期望值作为路径跟踪的虚拟控制律,通过设计滑模控制器实现对虚拟控制量的误差跟踪控制,通过设计切换函数避免无人船的控制量出现饱和或抖振现象,进而降低模型不确定及干扰对路径跟踪控制的影响。仿真实验表明,设计的控制器可在外界时变扰动与模型不确定的前提下完成对给定路线的理想跟踪。     

非参数不确定系统的有限时间迭代学习控制

针对任意初态情形,引入初始修正作用,研究一类非参数不确定时变系统能够达到实际完全跟踪性能的迭代学习控制方法. 采用Lyapunov-like综合,设计迭代学习控制器处理不确定性时变系统非参数化问题,其中含有有限时间控制作用,以实现在预先指定区间上的零误差跟踪. 并且,运用完全限幅学习机制,保证闭环系统中各变量的一致有界性以及跟踪误差的一致收敛性. 仿真结果表明了所提出控制方法的有效性.     

基于情感神经网络的有源电力滤波器智能终端滑模控制

为了增强有源电力滤波器的电流跟踪控制性能,提出一种基于连续径向基情感神经网络的递归终端滑模控制方案.首先介绍包括集总不确定的有源电力滤波器数学模型;然后构造递归终端滑模面,该滑模面由快速非奇异终端滑模面和递归积分终端滑模面组成,不仅可确保跟踪误差在有限时间内收敛到零,而且可通过为滑模面参数设置适当的初始值消除滑模面的到达模态.为了有效克服系统不确定因素的影响,采用连续径向基情感神经网络逼近系统不确定参数,并运用Lyapunov方法对其进行稳定性和收敛性分析.所设计的连续径向基情感神经网络,不仅结构简单、响应速度快,而且具备参数在线调节能力.仿真和实验结果均表明,该控制方案具有优异的电流跟踪能力以及抗干扰能力.     

随机变批次长度的反馈辅助PD型量化迭代学习控制

针对离散线性系统,研究批次长度随机变化的反馈辅助PD型量化迭代学习控制问题.考虑系统信号经量化后传输到控制器或执行器的情况,给出两种量化方案:跟踪误差信号量化和控制输入信号量化.基于两种不同的量化信号,在批次长度和初始条件随机变化前提下设计反馈辅助PD型迭代学习控制算法.采用扇形界的处理方法和堆积系统框架,推导数学期望下的学习收敛条件:在误差信号量化情况下,所提出控制算法可以保证跟踪误差渐近收敛到零;在控制输入信号量化情况下,所提出控制算法能够保证跟踪误差有界收敛.仿真示例对比验证了两种量化方案下所提出方法的有效性和优越性.     

An improved continuous sliding mode controller for flexible air‐breathing hypersonic vehicle

An improved continuous sliding mode control algorithm is proposed for a flexible air‐breathing hypersonic vehicle (FAHV), including nonsingular fast fixed‐time sliding surface (NFFS) and dual‐layer adaptive continuous twisting reaching law (DACTL). Firstly, the nonlinear control‐oriented model of FAHV is processed using input/output feedback linearization method with the significant flexible effects modeling as unknown matched disturbances. Secondly, a novel NFFS is improved from conventional fixed‐time sliding surface by adjusting power exponent to accelerate convergence rate. In the meanwhile, in order to avoid singularity aroused by fractional power term, an exponential convergent sliding surface is switched when tracking error approaches zero. Thirdly, a DACTL is proposed to realize finite‐time convergence of sliding mode variable with higher convergence precision and less chattering. Dual‐layer adaptive law is utilized to adjust the gain in DACTL based on equivalent control concept so as to enhance robustness automatically and avoid overestimation of control gain. Meanwhile, disturbances can be compensated without knowledge of Lipschitz constants. Ultimately, simulations on longitudinal control of FAHV demonstrate the control algorithm proposed is superior to conventional quasi‐continuous sliding mode controller in the aspect of convergence accuracy and chattering suppression.     

电液伺服系统多项式非线性建模与控制一体化设计

常规电液伺服系统PID控制无法克服非线性因素影响,存在跟踪准确性和鲁棒性问题.因此,本文提出电液伺服系统多项式非线性H_∞控制律设计方法,改进电液伺服系统的控制性能与鲁棒性.首先利用多项式非线性模型对电液伺服系统进行系统辨识,得到以误差作为状态变量的多项式非线性模型;然后设计多项式非线性控制律,证明所提出控制律可以保证系统从干扰至控制输出L₂增益小于等于设定值,并且在系统干扰为零时保证误差全局渐进收敛,同时给出了控制律的求解方法.最后对提出的控制律进行实验验证.实验结果表明:相较于常规PID控制,多项式非线性控制律能够改善实验台伺服缸控制过程的瞬态响应,具有更好的抗干扰能力.本文提出的设计方法为非线性H_∞控制在电液伺服系统控制领域的实际应用提供了可行方案.