Output tracking control for generalised high-order nonlinear system with serious uncertainties

This paper focuses on the problem of output tracking control for a class of generalised high-order uncertain nonlinear systems. Serious uncertainties are composed of unknown high-order terms, unknown nonlinear functions and the signal to be tracked. The new feedback scheme guarantees that the tracking error belongs to a prescribed small neighbourhood of the origin in finite time. Design procedures are presented by combining improved adding a power integrator method with the recursive construction. As an application, the control methodology is used in the tracking control of the mass-spring mechanical system.     

Distributed adaptive tracking control for high-order multi-agent systems with unknown dynamics

In this paper, we investigate the adaptive tracking problem of high-order multi-agent systems with unknown parameters and unknown nonlinear functions. Under the assumption that the leader is the root of a spanning tree, a distributed adaptive controller with tuning function is constructed recursively based on backstepping design method. The designed controller can guarantee that the tracking errors and the parameter errors eventually converge to an arbitrarily small compact set by choosing design parameters. A simulation example demonstrates the effectiveness of the design scheme.     

电熔镁砂熔炼过程带输出补偿的PID控制

电熔镁砂熔炼过程是以三相电机转动方向与频率为输入,以三相电极电流为输出的强非线性工业过程,其模型参数埋弧电阻率、熔池电阻率和熔池高度随熔炼过程和原矿颗粒长度及杂质成分的变化而变化.本文采用线性模型和未知高阶非线性项来描述电熔镁砂熔炼过程,其中未知高阶非线性项用已知的前一时刻高阶非线性项和其变化率来描述,采用线性模型设计PID控制器,并设计消除前一时刻高阶非线性项的补偿器和消除其变化率的补偿器,提出了带输出补偿的PID控制器,同时采用一步最优前馈控制律和一步最优调节律设计控制器参数.通过仿真实验和电熔镁炉的工业应用,表明当该过程的动态特性发生未知随机变化时,本文所提方法在所有运行时间内可以将电流跟踪误差控制在目标值范围内.     

Adaptive consensus tracking of high-order nonlinear multi-agent systems with directed communication graphs

This paper studies the adaptive consensus tracking problem for multi-agent systems modeled as high-order integrators with uncertain nonlinear dynamics under directed communication graphs. By parameterizing the unknown dynamics of the agents and the unknown input of the leader, two decentralized tracking control laws are designed using the state information and only the output information, respectively. With state information, globally uniformly ultimately bounded consensus tracking with arbitrarily small tracking errors can be achieved and when only output information is available, based on high-gain observers, semi-global result can be obtained. Numerical examples are provided to illustrate the effectiveness of the controllers.     

具有输入和输出约束的高阶随机系统神经网络控制

针对高阶非线性系统,开展自适应神经网络跟踪控制器设计,系统受到随机扰动的影响.首次把输入和输出约束问题引入到高阶系统的跟踪控制中,并假定系统动态是未知.首先借用高斯误差函数表达连续可微的非对称饱和模型以实现输入约束,和障碍Lyapunov函数保证系统输出受限;其次,针对高阶非线性系统,径向基函数(RBF)神经网络用来克服未知系统动态和随机扰动.在每一步的backstepping计算中,仅用到单一的自适应更新参数,从而克服了过参数问题;最后,基于Lyapunov稳定性理论提出自适应神经网络控制策略,并减少了学习参数.最终结果表明设计的控制器能保证所有闭环信号半全局最终一致有界,并能使跟踪误差收敛到零值小的邻域内.仿真研究进一步验证了提出方法的有效性.     

非线性系统高阶迭代学习算法

结合迭代学习控制算法中的开环和闭环方案，本文针对更一般的非线性系统，讨论高阶算法的广泛适用性。理论和仿真结果表明了高阶算法在输出跟踪和干扰抑制方面的有效性。     

控制方向未知的高次不确定非线性系统低复杂度预定性能控制

本文研究了控制方向未知的高次不确定非线性系统预定性能控制问题.基于有理分式函数类型的误差转换,通过设计具有切换形式的控制器,提出了一种低复杂度的控制方法,并利用Lyapunov理论和反证法证明了闭环系统稳定性.与现有其他方法相比,该方法具有适用范围广、跟踪精度高、容错能力强、控制器简单等优点.仿真结果验证了本文结论.     

Distributed adaptive containment control for high-order nonlinear multi-agent systems

This paper considers the adaptive containment control of high-order nonlinear multi-agent systems with nonlinear parameterisation. Without imposing any conditions on the unknown nonlinearities and unknown parameters, the distributed controllers are constructed recursively with only neighbours’ information by using the backstepping design method. Under the assumption that the leaders set is globally reachable, it is shown that all the signals of the closed-loop systems are global uniformly ultimately bounded (UUB), and all the followers will exponentially converge to the convex hull spanned by the dynamic leaders with adjustable tracking errors. Finally, two simulation examples demonstrate the effectiveness of the control scheme.     

An “impossibility” theorem on a class of high-order discrete-time nonlinear control systems

A high-order discrete-time nonlinear system with unknown parameters and Gaussian noise is studied in this paper, and it is shown that under a simple algebraic condition concerning the nonlinearity of the system, there does not exist any feedback controller that globally stabilizes the uncertain system. This impossibility result attempts to understand the capability and limitation of feedback mechanism of a high-order discrete-time nonlinear system and continues a series of previous research on similar first-order systems. This result shows that for the high-order discrete-time nonlinear system studied here, a polynomial corresponding to the first-order principle part of the system determines the limit of the feedback mechanism.     

A framework for stability analysis of high-order nonlinear systems based on the CMAC method

A framework for analyzing the stability of a class of high-order minimum-phase nonlinear systems of relative degree two based on the characteristic model-based adaptive control (CMAC) method is presented. In particular, concerning the tracking problem for such high-order nonlinear systems, by introducing a consistency condition for quantitatively describing modeling errors corresponding to a group of characteristic models together with a certain kind of CMAC laws, we prove closed-loop stability and show that such controllers can make output tracking error arbitrarily small. Furthermore, following this framework, with a specific characteristic model and a golden-section adaptive controller, detailed sufficient conditions to stabilize such groups of highorder nonlinear systems are presented and, at the same time, tracking performance is analyzed. Our results provide a new perspective for exploring the stability of some high-order nonlinear plants under CMAC, and lay certain theoretical foundations for practical applications of the CMAC method.     

Adaptive neural output feedback finite-time command filtered backstepping control for nonlinear systems with full-state constraints

In this paper, an adaptive neural finite-time control method via barrier Lyapunov function, command filtered backstepping, and output feedback is proposed to solve the tracking problem of uncertain high-order nonlinear systems with full-state constraints and input saturation. By utilizing the neural network (NN) to approximate unknown nonlinear functions, the finite-time command filters are used to filtering the virtual control signals and get the intermediate control signals in a finite time in the backstepping process. Because there are errors between the output of finite-time command filters and the virtual control signals, the error compensation signals are added to eliminate the influence of filtering errors. Based on the proposed control scheme, the states of the system can be constrained in the predetermined region, all signals in the system are bounded in finite time, and the tracking error can converge to the desired region in finite time. At last, a simulation example is given to show the effectiveness of the proposed control method.     

Feedback nonlinear discrete-time systems

In this paper, we design an adaptive iterative learning control method for a class of high-order nonlinear output feedback discrete-time systems with random initial conditions and iteration-varying desired trajectories. An n-step ahead predictor approach is employed to estimate future outputs. The discrete Nussbaum gain method is incorporated into the control design to deal with unknown control directions. The proposed control algorithm ensures that the tracking error converges to zero asymptotically along the iterative learning axis except for the beginning outputs affected by random initial conditions. A numerical simulation is carried out to demonstrate the efficacy of the presented control laws.     

Robust adaptive uniform exact tracking control for uncertain Euler–Lagrange system

This paper offers a solution to the robust adaptive uniform exact tracking control for uncertain nonlinear Euler–Lagrange (EL) system. An adaptive finite-time tracking control algorithm is designed by proposing a novel nonsingular integral terminal sliding-mode surface. Moreover, a new adaptive parameter tuning law is also developed by making good use of the system tracking errors and the adaptive parameter estimation errors. Thus, both the trajectory tracking and the parameter estimation can be achieved in a guaranteed time adjusted arbitrarily based on practical demands, simultaneously. Additionally, the control result for the EL system proposed in this paper can be extended to high-order nonlinear systems easily. Finally, a test-bed 2-DOF robot arm is set-up to demonstrate the performance of the new control algorithm.     

Adaptive fuzzy leader-follower consensus control using sliding mode mechanism for a class of high-order unknown nonlinear dynamic multi-agent systems

In this article, a distributed leader-follower consensus approach is developed for a class of high-order unknown nonlinear dynamic multi-agent systems (MASs). Because every agent of the MAS contains multiple state variables, the existing consensus methods are not completely applicable for it. In order to find the qualified consensus protocol for this high-order MAS, sliding mode mechanism can be naturally considered for designing the consensus control because it can manage multiple state variables with the help of a constructed hyperplane. To this consensus control design, the sliding mode term is composed of all tracking error variables. Since the method does not require the switching control term around sliding surface, it can avoid the chattering phenomenon, which exits in most of the published sliding mode controls (SMCs). Furthermore, to handle the unknown nonlinear dynamic problem, the adaptive approximation strategy is implemented by employing fuzzy logic system (FLS). In the light of Lyapunov stability analysis, it is demonstrated that the proposed control approach can accomplish the consensus tasks. Finally, a numerical example is implemented to further show the desired results.     

一类死区非线性系统的自适应模糊控制设计

为了实现对具有时变摄动死区非线性系统的跟踪控制,本文提出了一种基于自适应模糊逼近器的Backstepping控制方法。该方法通过将死区特性合理分解,并将自适应模糊逼近器嵌入到Backstepping设计步骤中,逐步递推得到控制律。所提出的控制方法适用于高阶非线性系统,并且不要求被控系统满足匹配条件;所采用的模糊逼近器是非线性参数化的,亦即不要求其模糊基函数是完全确定已知的,从而降低了对先验知识的依赖性。为了得到未知参数的自适应律,本文先应用Taylor级数展开式将具有非线性关系的未知参数相互分离,使其呈现线性关系,然后根据Lyapunov稳定性定理给出在线可调参数的自适应律。此外,所设计的自适应律是对与未知参数向量的范数相关的变量进行在线调节,这样可以有效减少需要在线调节的参数数量,从而降低了控制器的在线计算负担,提高了系统的响应速度和控制精度。本文给出的控制设计能够有效地克服死区特性对系统性能的影响,使得闭环系统所有信号均指数收敛到原点的指定邻域内,系统输出可以按给定的精度跟踪参考信号。最后,本文用一个仿真实例验证了所给控制方法的有效性。     

一类具有未知幂次的高阶不确定非线性系统的自适应控制

研究了一类具有未知幂次的高阶不确定非线性系统的自适应跟踪控制问题. 在无需系统函数先验知识的条件下, 采用积分反推技术和障碍李雅普诺夫函数, 提出了一种新颖的自适应跟踪控制算法. 该控制算法的显著特点是所设计的自适应控制器均与系统幂次无关, 并且能够保证闭环系统的所有信号皆有界. 仿真算例验证了该控制算法的有效性.     

Tracking control for magnetic-suspension systems with online unknown mass identification

This paper proposes a new nonlinear tracking control scheme with simultaneous unknown mass identification for magnetic suspension systems. Specifically, an amplitude-saturated adaptive control law is developed to achieve stable tracking and accurately estimate the unknown suspended mass simultaneously. The stability is assured with rigorous Lyapunov-based analysis. As far as we know, this is the first continuous control method for magnetic suspension systems with unknown levitated ball mass and actuator saturation, yielding an asymptotic result to achieve simultaneous tracking control and mass identification. Through hardware experiments, we verify the performance of the proposed method and compare it with existing methods.     

Efficient time-optimal feedrate planning under dynamic constraints for a high-order CNC servo system

In this paper, the time-optimal feedrate planning problem under confined feedrate, axis velocity, axis acceleration, axis jerk, and axis tracking error for a high-order CNC servo system is studied. The problem is useful in that the full ability of the CNC machine is used to enhance the machining productivity while keeping the machining precision under a given level. However, the problem is computationally challenging. The main contribution of this paper is to approximate the problem nicely by a finite-state convex optimization problem which can be solved efficiently. The method consists of two key ingredients. First, a relationship between the tracking error and the input signal in a high-order CNC servo system is established. As a consequence, the tracking error constraint is reduced to a constraint on the kinematic quantities. Second, a novel method is introduced to relax the nonlinear constraints on kinematic quantities to linear ones. Experimental results are used to validate the proposed method.     

周期时变时滞非线性参数化系统的自适应学习控制

针对一阶未知非线性参数化周期时变时滞系统, 设计了一种自适应学习控制方案. 假设未知时变参数, 时变时滞和参考信号的共同周期是已知的, 通过重构系统方程, 将包含时变时滞在内的所有未知时变项合并成为一个周期时变向量, 采用周期自适应律估计该向量. 通过构造一个Lyapunov-Krasovskii型复合能量函数证明了所有信号有界并且跟踪误差收敛. 结果被推广到一类含有混合参数的高阶非线性系统. 通过两个仿真例子说明本文所提出的控制算法的有效性.     

一类非线性参数化系统的迭代学习控制

针对一类含有时变和时不变参数的高阶非线性系统,提出了一种新的自适应迭代学习控制方法。该算法利用参数分离性原理和改进的Backstepping方法相结合,可以处理非线性参数化系统的跟踪问题。非线性参数化不确定项利用分离性原理来解决,而Backstepping方法处理不匹配的不确定项。通过构造参数的微分型自适应律和差分型自适应律,使得跟踪误差的平方在一个有限区间上的积分收敛于零。构造了Lyapunov-like函数和自适应学习控制律,证明了所有信号均在有限区间上的积分的意义下是有界的。仿真结果验证了所提算法的有效性和可行性。该方法为以后设计类似的非线性参数化系统的跟踪问题提供了先验知识。