Prescribed Performance Control of Uncertain Euler–Lagrange Systems Subject to Full-State Constraints
Prescribed Performance Control of Uncertain Euler–Lagrange Systems Subject to Full-State Constraints
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DOI：
10.1109/tnnls.2017.2727223
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发表时间：
2018-08
影响因子：
10.4
通讯作者：
Kai Zhao;Yongduan Song;Tiedong Ma;Liu He
中科院分区：
文献类型：
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作者：
Kai Zhao;Yongduan Song;Tiedong Ma;Liu He
研究具有全状态约束和非参数不确定性的欧拉-拉格朗日系统的零误差跟踪控制问题。将误差变换与barrier Lyapunov函数相结合，提出了一种神经自适应跟踪控制方案，该方案具有以下几个显著特征：1)控制动作连续且$\mathscr C^{1}$平滑；2)在给定的有限时间内，全状态跟踪误差以可统一预定的可控收敛速度收敛到原点附近的一个规定的紧集；3)随着努斯鲍姆增益的加入，跟踪误差进一步缩小为零，为$t\to \infty $；4)神经网络单元可以在整个系统运行包络期间安全地包含在环路中，而不会违反神经网络训练输入的紧集前提条件的危险。进一步，利用李雅普诺夫分析，证明了闭环系统的所有信号都是半全局一致最终有界的。通过计算机仿真验证了所提控制方法的有效性和优越性。
This paper studies the zero-error tracking control problem of Euler-Lagrange systems subject to full-state constraints and nonparametric uncertainties. By blending an error transformation with barrier Lyapunov function, a neural adaptive tracking control scheme is developed, resulting in a solution with several salient features: 1) the control action is continuous and $\mathscr C^{1}$ smooth; 2) the full-state tracking error converges to a prescribed compact set around origin within a given finite time at a controllable rate of convergence that can be uniformly prespecified; 3) with Nussbaum gain in the loop, the tracking error further shrinks to zero as $t\to \infty $ ; and 4) the neural network (NN) unit can be safely included in the loop during the entire system operational envelope without the danger of violating the compact set precondition imposed on the NN training inputs. Furthermore, by using the Lyapunov analysis, it is proven that all the signals of the closed-loop systems are semiglobally uniformly ultimately bounded. The effectiveness and benefits of the proposed control method are validated via computer simulation.