A time-delay compensation strategy for processes with uncertainties

Real-time random delay compensation with prediction-based digital redesign

“…Though differential equations or transfer functions are utilized in many papers [18], state-space model [15,16,34] is utilized in this article because it is convenient to realize state prediction as demonstrated in (7) [30]. The continuous-time state-feedback controller for the plant described by (1) is given as…”

“…As indicated in [12,13], the achievable performance of a closed-loop feedback control process can be significantly degraded if there is a relatively large time delay compared to the dominant time constant. As such, network induced delay potentially degrade the performance or even destabilize real-time critical applications [4,10,11,[14][15][16][17][18]. Without appropriate time delay compensation, either reduce network nodes to ensure cycle time within affordable range, or preclude the time-critical equipment to be connected through networked control, all these measures restrict the further utilization of NCS [13,19].…”

Model Gain Scheduling Control of an Ethyl tert-Butyl Ether Reactive Distillation Column

“…In this work, the transfer function local models are derived to approximate the nonlinear stable system. In general, they may be formulated as single input single output (SISO) with time delay as shown in eq 1, where y and u are output and input variables, respectively, and G p is the transfer function of the process. T and θ are the process time constant and time delay, respectively, which may have to be determined by experiment.…”

“…The simplest way is to distribute the local models uniformly over the entire operating conditions. However, prior knowledge of the process can be used by employing more local models in the sensitive region so that these models can be distributed more efficiently . In other words, more local models should be established in the operating regions where the process directionality significantly varies.…”

Graphic IMC-PID tuning based on maximum sensitivity for uncertain systems

“…The Smith predictor controller was introduced to uncertain systems and used to design a controller based on bandwidth considerations and minimizing the predictor controller structure to ensure robustness (Garcia-Sanz et al, 2001). Zhao et al (2002) added an on-line optimization filter to pattern-based fuzzy prediction to maintain an appropriate balance between performance and robustness. Although various controllers have been proposed to control uncertain systems, the proportional-integral-differential (PID) controller is still studied because of its wide use in industrial systems.…”