一类非线性时滞系统的自适应动态面控制

本文对于一类含不确定输入时滞和干扰的非线性系统的跟踪控制问题提出了一种自适应动态面控制方案. 利用动态面控制方法避免了传统的后推设计中存在的复杂度爆炸问题. 分别构造了一个滤波器和一个虚拟观测器来产生辅助信号. 采用神经网络来逼近未知的连续函数. 跟踪误差被证明最终收敛到一个足够小的紧集. 给出了一个数字仿真示例验证了理论结果.     

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.     

不确定离散时滞系统的输出反馈鲁棒预测控制

对一类输入输出受限的不确定离散时滞系统,研究了使得闭环系统渐近稳定且滚动时域性能指标在线最小化的鲁棒预测输出反馈控制器设计问题.基于预测控制的滚动优化原理,给出了输出反馈控制器存在的充分条件.采用锥补线性化思想将控制器的设计转化为一个受线性矩阵不等式(LMI)约束的非线性规划问题,并利用该线性矩阵不等式的可行解给出了输出反馈控制器的构造方法.最后通过仿真验证了该方法的有效性.     

滑模等式约束的时滞系统广义预测控制

提出了一种滑模等式约束的广义预测控制方法.该方法将广义预测控制与离散滑模控制结合起来用于具有大惯性、大时滞、时变和非线性的热力站换热机组的供水温度控制系统中,并采用柔化输入信号的方法,可避免广义预测控制算法中的矩阵求逆,有效缩短了预测时域,减小计算量.最后给出了稳定性分析并通过仿真验证了该方法的有效性.     

具有结构不确定性的时滞系统的闭环鲁棒预测控制器

针对具有结构不确定性的时滞系统,设计了闭环鲁棒预测控制算法.该控制算法基于控制不变集方法,通过采用双模控制和闭环控制策略,增加了控制设计的自由度,进而扩大了系统的初始可行域并能获得较优的控制性能.仿真结果验证了该方法的有效性.     

Adaptive neural control for strict-feedback stochastic nonlinear systems with time-delay

The problem of robust stabilization is investigated for strict-feedback stochastic nonlinear time-delay systems via adaptive neural network approach. Neural networks are used to model the unknown packaged functions, then the adaptive neural control law is constructed by a novel Lyapunov-Krasovskii functional and backstepping. It is shown that all the variables in the closed-loop system are semi-globally stochastic bounded, and the state variables converge into a small neighborhood in the sense of probability.     

不确定非线性时滞系统的非脆弱保成本控制

研究了一类具有状态时滞的不确定非线性系统的非脆弱保成本控制问题。通过利用Lyapunov稳定性理论和线性矩阵不等式方法,设计非脆弱保成本控制律,使得闭环系统渐近稳定,并且系统的性能指标不超过某个确定的上界。通过求解一个具有线性矩阵不等式约束的凸优化问题来设计最优非脆弱保成本控制器,以使得闭环不确定系统的性能指标最小化。仿真结果验证了该控制算法的有效性。     

非线性模糊时滞系统鲁棒自适应控制

研究一类基于模糊T-S模型的非线性时滞系统鲁棒镇定问题．基于记忆型状态反馈策略，首先给出由T-S模糊模型描述的非线性时滞系统在时滞精确已知情况下的鲁棒镇定准则；然后给出非线性时滞系统在时滞未知情况下的鲁棒自适应控制策略．所设计的控制器可确保闭环系统渐近稳定，且具有良好的可操作性．最后通过仿真实例证明了该方法的正确性和有效性．     

Adaptive fuzzy control for unknown nonlinear time-delay systems with virtual control functions

In this paper, adaptive fuzzy control is presented for a class of unknown nonlinear timedelay systems with virtual control functions. By employing fuzzy logic systems and the technique of delay replacement, dynamic surface control (DSC) design approach can be carried out with both unknown delay signals and nonlinearities. This is different from the existing results, which are used to make limitations on the time-delays. It is proved that the proposed design method is able to guarantee semiglobal uniform ultimate boundedness (SGUUB) of all signals in the closed-loop system, with arbitrary small tracking error by appropriately choosing design constants.     

Adaptive state feedback control for time-delay stochastic nonlinear systems based on dynamic gain method

This paper studies the adaptive state feedback control for p normal form time-delay stochastic nonlinear systems with unknown parameters by dynamic gain technique. The power order restriction is completely removed and tracking problem is further studied. Through the inductive design method, the virtual controllers are constructed in each step, and the corresponding dynamic gain is introduced to eliminate residual terms generated by the differential operator of Lyapunov–Krasovskii functional in the subsequent step, which is used to deal with the time-delay terms. The unknown parameters are addressed by the modified tuning function method. Based on the constructed adaptive controller, the boundedness of the tracking error and other state variables can be guaranteed. Especially, if the reference signal is zero, the state variables can converge to equilibrium almost surely. Finally, simulation results are presented to illustrate the effectiveness of the proposed method.     

Multi-objective optimal control for a class of nonlinear time-delay systems via adaptive dynamic programming

A novel multi-objective adaptive dynamic programming (ADP) method is constructed to obtain the optimal controller of a class of nonlinear time-delay systems in this paper. Using the weighted sum technology, the original multi-objective optimal control problem is transformed to the single one. An ADP method is established for nonlinear time-delay systems to solve the optimal control problem. To demonstrate that the presented iterative performance index function sequence is convergent and the closed-loop system is asymptotically stable, the convergence analysis is also given. The neural networks are used to get the approximative control policy and the approximative performance index function, respectively. Two simulation examples are presented to illustrate the performance of the presented optimal control method.     

带有持续扰动的时滞非线性大系统的最优跟踪控制

研究具有外界持续扰动的时滞非线性大系统的无静差最优跟踪控制问题.将时滞非线性大系统分解为带有互联项的N个时滞非线性子系统,基于内模原理对子系统构造扰动补偿器,将带有外部持续扰动的子系统化为无扰动的增广系统.通过灵敏度法求解不含时滞的两点边值问题,得到子系统的最优跟踪控制律,截取最优跟踪控制律的前N项作为次优控制律来近似系统的最优控制律.仿真实例表明了该设计方法的有效性.     

受正弦扰动时滞非线性系统的近似最优减振控制

研究时滞非线性系统在正弦扰动作用下的最优减振控制问题,给出一种无时滞近似最优减振控制律的迭代方法．通过假设Lagrange算子,将由原系统最优控制问题得到的既含时滞项又含有超前项的非线性两点边值问题转换为新的有利于求解的形式,再通过构造序列将其转化为不舍时滞项和超前项的线性非齐次两点边值问题序列．证明了该序列的收敛性．通过交替迭代序列得到了系统最优减振控制律．仿真结果表明,该方法在不同时滞下对扰动都具有很好的鲁棒性．     

随机时滞系统的神经网络输出反馈动态面控制

针对一类具有未知控制方向的随机时滞系统设计自适应神经输出反馈控制器.首先,利用状态观测器估计不可测量的系统状态;其次,选择合适的Lyapunov-Krasovskii函数消除未知延迟项对系统的影响,利用Nussbaum-type函数处理系统的未知控制方向问题,通过神经网络逼近未知的非线性函数,以及用动态表面控制(DSC)解决控制器设计中出现的复杂性问题;最后,通过Lyapunov稳定性理论,构造一个鲁棒自适应神经网络输出反馈控制器,可以保证闭环系统中所有信号在二阶或四阶矩意义下一致最终有界,跟踪误差能收敛到零值小的领域内.仿真实例验证了所提出方法的有效性.     

非线性时滞系统的抖振削弱自适应滑模控制

针对一类非线性时滞系统的鲁棒自适应滑模控制器设计问题,提出了一种基于偏微分变换的自适应滑模控制方法,该方法能够有效地削弱系统的输入抖动．基于自适应滑模控制技术和Ｌｙａｐｕｎｏｖ稳定方法,克服了时滞影响,不但保证系统状态可以在有限的时间内到达滑模面,而且保证了系统的渐近稳定特性．最后给出的仿真结果验证了该控制方案的有效性．     

Approximation-based control of nonlinear MIMO time-delay systems

Approximation-based control is presented for a class of multi-input multi-output (MIMO) nonlinear systems in block-triangular form with unknown state delays. Neural networks (NNs) are utilized to approximate and compensate for unknown functions in the system dynamics, including the unknown bounds of the functions of delayed states. The use of a separation technique removes the need for any assumption on the function of delayed states, and allows the handling of multiple delays in each function of delayed states. By combining the use of Lyapunov-Krasovskii functionals and adaptive NN backstepping, the proposed control guarantees that all closed-loop signals remain bounded, while the outputs converge to a neighborhood of the desired trajectories. Simulation results demonstrate the effectiveness of the proposed scheme.