We propose an extension of the input-output feedback linearization for a class of multivariate systems that are not input-output linearizable in a classical manner. The key observation is that the usual input-output linearization problem can be interpreted as the problem of solving simultaneous linear equations associated with the input gain matrix: thus, even at points where the input gain matrix becomes singular, it is still possible to solve a part of linear equations, by which a subset of input-output relations is made linear or close to be linear. Based on this observation, we adopt the task priority-based approach in the input-output linearization problem. First, we generalize the classical Byrnes-Isidori normal form to a prioritized normal form having a triangular structure, so that the singularity of a subblock of the input gain matrix related to lower-priority tasks does not directly propagate to higher-priority tasks. Next, we present a prioritized input-output linearization via the multi-objective optimization with the lexicographical ordering, resulting in a prioritized semilinear form that establishes input output relations whose subset with higher priority is linear or close to be linear. Finally, Lyapunov analysis on ultimate boundedness and task achievement is provided, particularly when the proposed prioritized input-output linearization is applied to the output tracking problem. This work introduces a new control framework for complex systems having critical and noncritical control issues, by assigning higher priority to the critical ones.

翻译：本文提出一类经典方式下不可输入-输出线性化的多变量系统的输入-输出反馈线性化扩展方法。关键发现为：常规输入-输出线性化问题可被解释为与输入增益矩阵相关的联立线性方程求解问题；因此，即使输入增益矩阵在奇异点处，仍可求解部分线性方程，从而使输入-输出关系的子集实现线性化或近似线性化。基于此发现，我们在输入-输出线性化问题中引入基于任务优先级的方法。首先，将经典Byrnes-Isidori标准型推广为具有三角结构的优先级标准型，使得与低优先级任务相关的输入增益矩阵子块的奇异性不会直接传播至高优先级任务。其次，通过采用词典序多目标优化，提出优先级输入-输出线性化方法，得到具有优先级半线性形式的系统，该形式建立输入-输出关系时，使高优先级子集保持线性或近似线性。最后，针对所提优先级输入-输出线性化应用于输出跟踪问题的情况，给出关于最终有界性和任务完成的Lyapunov分析。本研究通过为关键与非关键控制问题赋予不同优先级，为具有关键/非关键控制问题的复杂系统建立了新的控制框架。