Proportional Integral Derivative Control System Research Articles

A new method for controlling refrigerant flow in automobile air conditioning

This paper describes the improvement of the refrigerant flow control method by using an electronic expansion valve (EEV) which is driven by a stepper motor in automobile air conditioning system. An EEV can make a quick response to the abrupt change in the refrigerant flow rate during the change in automobile speed and the thermostatic on/off operation. The flow rate characteristic of the EEV for automobile air conditioning was presented. A microcontroller is used to receive the input signal and generate the output signal to control the opening of the EEV. The fuzzy self-tuning proportional–integral–derivative (PID) control method is employed. Experimental results show that the new control method can feed adequate refrigerant flow into the evaporator in various operations. The evaporator discharge air temperature has dropped by approximately 3 °C as compared with that of the conventional PID control system.

Fuzzy logic for constant force control of end milling

The machining condition usually has significant variation resulting from the change of cutting depth and the intrinsic property of the workpiece. In order to maintain the performance of a classical proportional integral derivative control system, the tool life and machining quality, conservative feedrate, and cutting depth change are prespecified as the limitations of computer numerically controlled operators. Therefore, constant cutting force control is proposed as a useful approach for increasing the metal removal rate and the tool life. However, the model-based controller cannot handle the nonlinearity of a force control system due to cutting condition variations. Here, a fuzzy controller with learning ability was employed to improve both the system performance and the adaptability. This control approach vias implemented on a retrofit old-fashioned milling machine for the end milling process. The experimental results show that this control strategy has smooth feedrate and good cutting force dynamic responses.

A Fuzzy Expert System for Complex Closed‐loop Control: A Non‐Mathematical Approach

A fuzzy expert system uses fuzzy logic control (Zadeh, 1965) which is based on a ‘superset’ of Boolean logic that has been extended to handle the concept of ‘partial truth’ and it replaces the role of a mathematical model with another that is built from a number of rules with fuzzy variables such as output temperature and fuzzy terms such as ‘hot’, ‘fairly cold’, ‘probably correct’. In control areas, an expert system using fuzzy logic principles has advantages over conventional Proportional‐Integral‐Derivative (PID) closed‐loop control such as the ability to offer a method for representing and implementing the expert’s knowledge, the ability to implement simple but robust solutions as well as comparatively less development and maintenance time of the involved control system. A fuzzy expert system may further outperform conventional PID control system when dealing with ‘multiple‐input multiple‐output’ control situations where more complex control algorithms for conventional PID system are required. This paper presents a methodology for the implementation of such a fuzzy expert system with a step‐by‐step, non‐mathematical approach, which is able to handle complex closed‐loop control situations. A practical example is employed to illustrate the ‘road‐map’ of the methodology with realistic data based on the implementation of this system in a local company.

Performance and gain and phase margins of well-known PID tuning formulas

The performance and robustness of well-known PID formulas for process with deadtime to time constant ratio between 0.1 and 1 are discussed in this paper. The Ziegler-Nichols, Cohen-Coon, and tuning formulas that optimize for load disturbance response (integral absolute error, integral squared error, and integral time-weighted absolute error) give gain margins of about 1.5. The phase margins increase from about 30 to 60/spl deg/ as the process deadtime to time constant ratio increases from 0.1 to 1. Tuning formulas that optimize setpoint response give gain margins of about two and phase margins of about 65/spl deg/. These formulas mostly make use of the proportional-integral derivative (PID) controller zeros to cancel the process poles. Approximate analytical formulas to compute gain and phase margins of PID control systems are also derived in this paper to facilitate online computation which would be particularly useful for implementing adaptive control.

Closed-Loop Automatic Tuning of Single-Input-Single-Output Systems

Two closed-loop autotuning methods are developed to provide superior alternatives to tune a proportional-integral-derivative (PID) controller for single-input-single-output systems. The first method is a closed-loop extension of the Astrom-Hagglund autotuner. A relay is connected in the feedback loop to the PID control system to produce a stable limit cycle. On the basis of the resulting oscillations, the process is identified as a first- or second-order plus time-delay model. The second method is a time domain approach, which identifies the process as a second-order plus time-delay model via an underdamped transient. The identification scheme permits the use of any control mode (P, PI, or PID) and any test input signal (step, pulse, or impulse) applied in setpoint. Simple-to-use correlation formulas based on the identified model are derived to provide optimum PID settings which yield the desired decay ratio, robustness, and minimum integral of the absolute error. With an initial stabilizing controller, the two methods allow the control parameters to be adjusted iteratively to the correct values. The proposed methods are demonstrated to be valid for a wide range of process dynamics and insensitive to measurement noise and disturbances.