Proportional control

About: Proportional control is a research topic. Over the lifetime, 3756 publications have been published within this topic receiving 49050 citations.

Multiple-Model Adaptive Control of Blood Pressure Using Sodium Nitroprusside

Abstract: This paper presents a multiple-model adaptive control procedure for sodium nitroprusside regulation of arterial pressure. Pole-placement, via state-variable feedback, is included in the controller to achieve desired performance characteristics. A Smith predictor in the controller effectively removes the infusion delay time, thus simplifying the control analysis and design. Proportional-plus-integral control is used to achieve zero steady-state error. Computer simulations on linear transfer function models with varying gains, time constants, and infusion delays demonstrate the robustness of this multiple-model controller. Additional simulations on nonlinear, pulsatile-flow cardiovascular models lend further support to the ultimate use of this controller for blood-pressure regulation.

Fuzzy control of mean arterial pressure in postsurgical patients with sodium nitroprusside infusion

Abstract: A fuzzy control system to provide closed-loop control of mean arterial pressure (MAP) in postsurgical patients in a cardiac surgical intensive care unit setting by regulating sodium nitroprusside (SNP) infusion is discussed. The fuzzy controller, originally expert-system-based, was analytically converted to ten nonfuzzy control algorithms, which reduced execution time dramatically. The core of the control algorithms was a nonlinear proportional-integral (PI) controller whose proportional gain and integral gain adjusted continuously according to error and rate change of error of the process output. The gains become larger when process output was far from desired setpoint and smaller when process output was close to desired setpoint, resulting in more dynamic and stable control performance than the regular PI controller, especially when a linear process with time-delay or a nonlinear process was involved. The control algorithms, encoded in C programming language, were implemented to control MAP in patients. Preliminary clinical results showed that the average percentage of time in which MAP stayed between 90% and 110% of the MAP setpoint was 89.31%, with a standard deviation of 4.96%. These were calculated based on 12 patient trials, with total trial time of 95 and 13 min. >

Lyapunov Function-Based Current Controller to Control Active and Reactive Power Flow From a Renewable Energy Source to a Generalized Three-Phase Microgrid System

Abstract: In this paper, a novel current control technique, implemented in the a-b-c frame, for a three-phase inverter is proposed to control the active and reactive power flow from the renewable energy source to a three-phase generalized microgrid system. The proposed control system not only controls the grid power flow but also reduces the grid current total harmonic distortion in the presence of typical nonlinear loads. The control system shapes the grid current taking into account the grid voltage unbalance, harmonics as well as unbalance in line side inductors. The stability of the control system is ensured by the direct method of Lyapunov. A SRC is also proposed to improve the performance of the current controller by estimating the periodic disturbances of the system. The proposed control system (implemented digitally) provides superior performance over the conventional multiple proportional-integral and proportional-resonant control methods due to the absence of the PARK's transformation blocks as well as phase lock loop requirement in the control structure. A new inverter modeling technique is also presented to take care of unbalances both in grid voltages and line side inductors. Experimental results are provided to show the efficacy of the proposed control system.

The optimal location of piezoelectric actuators and sensors for vibration control of plates

Abstract: This paper considers the optimal placement of collocated piezoelectric actuator–sensor pairs on a thin plate using a model-based linear quadratic regulator (LQR) controller. LQR performance is taken as objective for finding the optimal location of sensor–actuator pairs. The problem is formulated using the finite element method (FEM) as multi-input–multi-output (MIMO) model control. The discrete optimal sensor and actuator location problem is formulated in the framework of a zero–one optimization problem. A genetic algorithm (GA) is used to solve the zero–one optimization problem. Different classical control strategies like direct proportional feedback, constant-gain negative velocity feedback and the LQR optimal control scheme are applied to study the control effectiveness.