目前，各种控制算法均侧重于满足系统的稳态性能，而较少关注系统的瞬态性能（主要指超调量和收敛速度）。希腊学者Bechlioulis等于2008年首次提出的预设性能控制(Prescribed Performance Control, PPC)，为解决性能控制问题提供了新思路。所谓 PPC 技术是指设计控制器使得闭环系统的跟踪误差收敛到一个预先设定的允许范围内的同时，保证收敛速度和超调量满足预先设定的条件，即要求瞬态和稳态性能同时得到满足，以提高控制系统的性能。

PPC方法的核心是设计性能包络和控制器，将受控系统的状态限制在所设计的性能包络中，从而达到定量化设计瞬态与稳态性能的目的。PPC围绕性能函数和空间对等变换这2个基本环节展开构建等效模型，再对等效模型进行控制器设计。下面结合下图具体说明。

PPC-R22.1N-N-Q1-NN-NN-FW

PPC-R22.1N-N-Q1-P2-NN-FW by Rexroth, Indramat, Bosch in the PPC Series. The PPC-R unit is a powerful controller in a small size in IP 20 rating. It is a general-purpose platform that works as a PLC or as an NC controller, depending on the application. This PPC-R22.1N-N-Q1-P2-NN-FW offers a Housing Version for RECO unit, and a Configuration of Cross Communication, ARCNet / PROFIBUS-DP Slave Module / Not Equipped.

At present, all kinds of control algorithms focus on the steady-state performance of the system, but pay less attention to the transient performance of the system (mainly refers to the overshoot and convergence rate). Greek scholar Bechlioulis Prescribed Performance Control (PPC) was first proposed in 2008, providing a new way to solve the problem of performance control. The so-called PPC technology refers to the design of the controller to make the tracking error of the closed-loop system converge to a pre-set allowable range, while ensuring that the convergence speed and overshoot meet the pre-set conditions, that is, the transient and steady-state performance are required to be met at the same time to improve the performance of the control system.

The core of PPC method is to design the performance envelope and controller, which limits the state of the controlled system to the designed performance envelope, so as to achieve the purpose of quantifying the transient and steady state performance. PPC builds the equivalent model around the two basic links of performance function and spatial equivalent transformation, and then carries on the controller design to the equivalent model. The following illustration is combined with the following figure.