状态与输入受限的二阶多输入多输出非线性系统自适应控制（英文）

针对含有状态和输入受限的二阶多输入多输出非线性系统的控制问题,提出了一种自适应控制策略.通过综合利用障碍Lyapunov函数和动态面控制方法的特性,使得系统的状态满足约束条件而且能够减少计算量.此外,为了处理输入约束和系统中的不确定性的影响,分别设计了辅助系统和自适应算法.通过理论分析表明,闭环系统的所有状态都是有界的,而且系统的状态和输入都满足约束条件.最后,通过一个数值仿真算例和一个实际的航天器姿态控制系统的仿真来验证所提出的自适应控制策略的有效性.     

有界扰动多变量Hammerstein系统输入到 状态稳定模型预测控制

考虑具有状态和控制约束的有界未知扰动多变量Hammerstein系统,提出一种具有输入到状态稳定和有限L_2增益性能的鲁棒非线性模型预测控制策略.基于多变量线性子系统H_∞控制律,滚动预测非线性代数方程的解算误差,继而在线优化计算满足系统约束条件的预测控制量.利用输入到状态稳定性概念和L_2增益思想,建立闭环系统关于该扰动信号具有鲁棒稳定性和L_2增益的充分条件,使闭环系统不仅满足系统约束,而且对不确定扰动输入和解算误差具有鲁棒性.最后以工业聚丙烯多牌号切换过程控制为例,仿真验证本文算法的有效性.     

考虑量化输入和输出约束的互联系统自适应分散跟踪控制

本文考虑具有量化输入和输出约束的一类非线性互联系统的自适应分散跟踪控制设计. 分别针对量化参数已知和未知两种情况, 基于反推(Backstepping)设计法, 利用神经网络逼近特性, 设计自适应分散跟踪控制策略. 通过定义新的未知常量和非线性光滑函数, 设计自适应参数估计项来消除未知互联项对系统的影响. 进一步考虑量化参数未知的情形, 引入一个新的不等式来转化输入信号, 并构建新的自适应补偿项来处理量化影响. 同时, 障碍李雅普诺夫函数的引入, 确保了系统输出不违反约束条件. 与现有量化输入设计相比, 本文所提方法不要求未知非线性项满足李普希兹条件, 并且允许量化参数未知. 该设计方法保证了闭环系统所有信号最终一致有界, 而且跟踪误差能够收敛到原点的小邻域内, 同时保证输出不违反约束条件. 最后, 仿真算例验证了所提方法具备良好的跟踪控制性能.     

一种新型的输入受约束的自适应控制

在系统输入受约束时,采用一般的广义预测控制有可能会造成算法的不可行,从而使系统的控制质量变坏,甚至造成系统的不稳定。针对输入受约束系统,采用简化的三角化脉冲响应模型,提出一种输入受约束的自适应模型算法控制。该算法只需在线辨识一个参数。不必求逆矩阵,极大地减少了计算量。而且,在输入和输入增量约束条件下,通过调整参考输出,使得约束条件的个数简化为一个,并可保证闭环系统的渐近稳定和全局收敛性。仿真结果进一步证实了该算法的有效性。     

基于仿射控制输入的输入状态稳定非线性预测控制

针对有扰动的约束非线性系统,提出了一种基于仿射控制输入的反馈预测控制策略.采用无穷范数定义有限时域代价函数,对其进行极大极小优化得到预测控制律,并应用输入状态稳定分析了闭环系统的鲁棒稳定性,同时还给出了确定容许扰动上界的方法.最后,数值仿真说明本文的预测控制策略是有效的.     

考虑输入约束的半主动悬架非线性自适应控制

针对具有输入约束及参数不确定性问题的汽车半主动悬架系统,提出一种考虑输入饱和的非线性自适应Backstepping控制器.该方法引入一个辅助系统,通过设计新的误差变量,实现对控制饱和的补偿,解决控制输入的幅值约束问题.同时,考虑到悬架系统的参数不确定性问题,采用映射自适应算法设计自适应律,通过构造适当的Lyapunov函数,保证悬架系统的稳定性.仿真结果表明,所设计的控制器具有良好的隔振性能,而且能够有效降低输入约束和不确定参数对系统性能的影响.     

一类切换系统的输入-状态稳定性分析与优化控制

利用驻留时间法和 Gronwall-Bellman不等式研究了一类切换系统的输入一状态稳定性分析与优化控制问题.在保证切换系统输入-状态稳定的前提下,将切换时刻和切换次数约束条件转化为线性约束,提出了一种新的切换系统优化问题目标函数的形式.与已有的方法相比,该方法无需引入新的状态变量,无需同时满足构造输入-状态稳定控制李亚普诺夫函数和所有子系统都是输入-状态稳定的条件,为控制器的优化设计提供了便利.最后,通过算例仿真证实了文中所提方法的可行性.     

虚拟控制系数未知的非完整系统的自适应神经网络控制

针对一类虚拟控制系数未知的多输入链式非完整控制系统,提出了一种自适应神经网络控制策略.在控制策略的设计中,采用了State-scaling与Backstepping技术相结合的方法.Nussbaum-type增益技术用来解决系统的控制方向完全未知的问题.所提出的自适应神经网络控制策略解决了由复杂系统所引起的奇异问题,并通过选择适当的控制参数,使闭环系统半全局一致有界,且系统的状态渐近收敛到包含原点的任意小的一个收敛域.一种基于切换策略的自适应控制方法解决了当x0(t0)=0时所引起的系统不可控问题.仿真结果验证了算法的有效性.     

带输入饱和的欠驱动吊车非线性信息融合控制

针对欠驱动吊车系统的控制问题,提出了一种非线性信息融合控制方法. 通过融合二次型性能指标函数中包含的未来参考轨迹和控制能量的软约束信息,以及吊车系统状态方程和输出方程的硬约束信息,获得协状态和控制量的最优估计. 针对控制量输入饱和的问题,提出了一种控制能量软约束信息自适应调节算法,使求出的控制量满足限制要求. 信息融合控制方法基于被控对象的离散模型设计,具有易于实现的特点. 仿真结果表明了该方法的有效性.     

输入受限约束下的汽车横摆力矩混合切换控制策略研究

目前在汽车横摆力矩控制器设计中,往往忽略了系统输入是有限的这一约束条件,因此所设计的控制器并不能总是工作在预先设定的反馈规律下。这在实际环境中将导致系统性能的不可靠。为此,在二次最优控制器的基础上,增加抗饱和控制器的设计,并通过混合切换控制策略来达到既保证良好的控制性能,同时又能适应系统输入有限这一约束条件。仿真实验表明,所设计的控制策略与这些控制器单独控制相比,不仅抗饱和性能得到提高,而且控制性能也同样令人满意。该方案的设计为更加合理的汽车稳定性控制策略提供参考。     

Adaptive fault tolerant control for a class of input and state constrained MIMO nonlinear systems

In this work, we present a novel adaptive fault tolerant control (FTC) scheme for a class of control input and system state constrained multi‐input multi‐output (MIMO) nonlinear systems with both multiplicative and additive actuator faults. The input constraints can be asymmetric, and the state constraints can be time‐varying. A novel tan‐type time‐varying Barrier Lyapunov Function (BLF) is proposed to deal with the state constraints, and an auxiliary system is designed to analyze the effect of the input constraints. We show that under the proposed adaptive FTC scheme, exponential convergence of the output tracking error into a small neighbourhood of zero is guaranteed, while the constraints on the system state will not be violated during operation. Estimation errors for actuator faults are bounded in the closed loop. An illustrative example on a two degree‐of‐freedom robotic manipulator is presented to demonstrate the effectiveness of the proposed FTC scheme. Copyright © 2015 John Wiley & Sons, Ltd.     

控制方向未知的全状态约束非线性系统的鲁棒自适应跟踪控制

针对一类控制方向未知的含有时变不确定参数和未知时变有界扰动的全状态约束非线性系统,本文提出了一种基于障碍Lyapunov函数的反步自适应控制方法.障碍Lyapunov函数保证了系统状态在运行过程中始终保持在约束区间内;Nussbaum型函数的引入解决了系统控制方向未知的问题;光滑投影算法确保了不确定时变参数的有界性.障碍Lyapunov函数、Nussbaum型函数及光滑投影算法与反步自适应方法的有效结合首次解决了控制方向未知的全状态约束非线性系统的跟踪控制问题.所设计的自适应鲁棒控制器能在满足状态约束的前提下确保闭环系统的所有信号有界.通过恰当地选取设计参数,系统的跟踪误差将收敛于0的任意小的邻域内.仿真结果表明了控制方案的可行性.     

Robust adaptive control for a class of semi‐strict feedback systems with state and input constraints

This paper proposes a dynamic surface control (DSC)–based robust adaptive control scheme for a class of semi‐strict feedback systems with full‐state and input constraints. In the control scheme, a constraint transformation method is employed to prevent the transgression of the full‐state constraints. Specifically, the state constraints are firstly represented as the surface error constraints, then, an error transformation is introduced to convert the constrained surface errors into new equivalent variables without constraints. By ensuring the boundedness of the transformed variables, the violation of the state constraints can be prevented. Moreover, in order to obtain magnitude limited virtual control signal for the recursive design, the saturations are incorporated into the control law. The auxiliary design systems are constructed to analyze the effects of the introduced saturations and the input constraints. Rigorous theoretical analysis demonstrates that the proposed control law can guarantee all the closed‐loop signals are uniformly ultimately bounded, the tracking error converges to a small neighborhood of origin, and the full‐state constraints are not violated. Compared with the existing results, the key advantages of the proposed control scheme include: (i) the utilization of the constraint transformation can handle both time‐varying symmetric and asymmetric state constraints and static ones in a unified framework; (ii) the incorporation of the saturations permits the removal of a feasibility analysis step and avoids solving the constrained optimization problem; and (iii) the “explosion of complexity” in traditional backstepping design is avoided by using the DSC technique. Simulations are finally given to confirm the effectiveness of the proposed approach.     

Finite-time tracking control for strict-feedback nonlinear systems with full state constraints

In this paper, an adaptive finite-time controller is considered for a class of strict-feedback nonlinear systems with parametric uncertainties and full state constraints. Novel tan-type barrier Lyapunov functions are proposed to ensure the boundedness of the fictitious state tracking errors. A new tuning function is constructed to eliminate the effect of uncertainties by using the extended finite-time stability condition. It is shown that under the proposed backstepping control scheme the finite-time convergence of system output tracking error to a small set around zero is realised and the full state constraints are not violated. A numerical example is provided to demonstrate the effectiveness of the proposed finite-time control scheme.     

Adaptive Consensus Quantized Control for a Class of High-Order Nonlinear Multi-Agent Systems With Input Hysteresis and Full State Constraints

For a class of high-order nonlinear multi-agent systems with input hysteresis, an adaptive consensus output-feedback quantized control scheme with full state constraints is investigated. The major properties of the proposed control scheme are: 1) According to the different hysteresis input characteristics of each agent in the multi-agent system, a hysteresis quantization inverse compensator is designed to eliminate the influence of hysteresis characteristics on the system while ensuring that the quantized signal maintains the desired value. 2) A barrier Lyapunov function is introduced for the first time in the hysteretic multi-agent system. By constructing state constraint control strategy for the hysteretic multi-agent system, it ensures that all the states of the system are always maintained within a predetermined range. 3) The designed adaptive consensus output-feedback quantization control scheme allows the hysteretic system to have unknown parameters and unknown disturbance, and ensures that the input signal transmitted between agents is the quantization value, and the introduced quantizer is implemented under the condition that only its sector bound property is required. The stability analysis has proved that all signals of the closed-loop are semi-globally uniformly bounded. The StarSim hardware-in-the-loop simulation certificates the effectiveness of the proposed adaptive quantized control scheme.      

Robust Tracking Control Of The Variable Stiffness Actuator Based On The Lever Mechanism

This paper is concerned with the design of a robust adaptive tracking control scheme for a class of variable stiffness actuators (VSAs) based on the lever mechanisms. For these VSAs based on the lever mechanisms, the AwAS‐II developed at Italian Institute of Technology (IIT) is chosen as the study object, and it is an enhanced version of the original realization AwAS (actuator with adjustable stiffness). Firstly, for the dynamic model of the AwAS‐II system in the presence of parametric uncertainties, unknown bounded friction torques, unknown bounded external disturbance and input saturation constraints, by using the coordinate transformations and the static state feedback linearization, the state space model of the AwAS‐II system with composite disturbances and input saturation constraints is transformed into an uncertain multiple‐input multiple‐output (MIMO) linear system with lumped disturbances and input saturation constraints. Subsequently, a combination of the feedback linearization, disturbance observer, sliding mode control and adaptive input saturation compensation law is adopted for the design of the robust tracking controller that simultaneously regulates the position and stiffness of the AwAS‐II system. Under the proposed controller, the semi‐global uniformly ultimately bounded stability of the closed‐loop system has been proved via Lyapunov stability analysis. Simulation results illustrate the effectiveness and the robustness of the proposed robust adaptive tracking control scheme.     

Adaptive nonlinear information fusion preview control for autonomous surface vessels subject to measurement noises and unknown input saturations

This study presents an adaptive nonlinear information fusion preview control (NIFPC) method for trajectory tracking of autonomous surface vessels (ASVs) subject to system uncertainty, measurement noise, and unknown input saturations. The NIFPC is developed based on the nonlinear information fusion estimation methodology, in which the system's future reference trajectory information, noise information, performance index requirements, and system dynamic model are all transformed into information equations related to control input, and then the current control action is obtained by fusing these previewed future information via the nonlinear information fusion optimal estimation. In order to avoid the unknown input saturation constraints, a fuzzy asymmetric saturated approximator (FASA) is designed and integrated into the controller, where the fuzzy logic system (FLS) is used to adaptively adjust the key boundary parameters of the approximator. As a result, the negative effects caused by system uncertainty and measurement noise can be effectively suppressed, while the completely unknown input saturation constraints in the system actuator are guaranteed not to be violated. The convergence of the tracking errors of the closed-loop system is guaranteed via Lyapunov stability theory. Numerical simulation results have been provided to demonstrate the satisfactory performance of the proposed control scheme.     

Adaptive optimal dynamic surface control of strict‐feedback nonlinear systems with output constraints

In this paper, an adaptive optimal control strategy is proposed for a class of strict‐feedback nonlinear systems with output constraints by using dynamic surface control. The controller design procedure is divided into two parts. One is the design of feedforward controller and the other is the design of optimal controller. To guarantee the satisfaction of output constraints in feedforward controller, nonlinear mapping is utilized to transform the constrained system into an unconstrained system. Neural‐network based adaptive dynamic programming algorithm is employed to approximate the optimal cost function and the optimal control law. By theoretical analysis, all the signals in the closed‐loop system are proved to be semi‐globally uniformly ultimately bounded and the output constraints are not violated. A numerical example illustrates the effectiveness of the proposed scheme.     

Adaptive finite‐time fault‐tolerant tracking control for a class of MIMO nonlinear systems with output constraints

In this work, we present a novel adaptive finite‐time fault‐tolerant control algorithm for a class of multi‐input multi‐output nonlinear systems with constraint requirement on the system output tracking error. Both parametric and nonparametric system uncertainties can be effectively dealt with by the proposed control scheme. The gain functions of the nonlinear systems under discussion, especially the control input gain function, can be not fully known and state‐dependent. Backstepping design with a tan‐type barrier Lyapunov function and a new structure of stabilizing function is presented. We show that under the proposed control scheme, finite‐time convergence of the output tracking error into a small set around zero is guaranteed, while the constraint requirement on the system output tracking error will not be violated during operation. An illustrative example on a robot manipulator model is presented in the end to further demonstrate the effectiveness of the proposed control scheme. Copyright © 2016 John Wiley & Sons, Ltd.     

Iterative learning control for output‐constrained nonlinear systems with input quantization and actuator faults

In this work, we propose a novel iterative learning control algorithm to deal with a class of nonlinear systems with system output constraint requirements and quantization effects on the system control input. Actuator faults have also been considered, which include multiplicative, additive, and stuck actuator faults. To the best of our knowledge, this is the first reported work in the iterative learning control literature to deal with quantization effects for the control input of nonlinear systems under the effects of actuator faults and system output constraints. Under the proposed scheme, using backstepping design and composite energy function approaches in the analysis, we show that uniform convergence of the state tracking errors can be guaranteed over the iteration domain, and the constraint requirement on the system output will not be violated at all time. In the end, a simulation study on a single‐link robot model is presented to demonstrate the effectiveness of the proposed scheme.