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Control of pH
Published in William S. Levine, Control System Applications, 2018
Often, feed is supplied by a pump driven by a level switch in a sump. In this case, feed rate is constant while it is flowing, but there are also periods of no flow. To protect the system against using reagents needlessly, the air supply to their control valves should be cut off whenever the feed pump is not running. At the same time, the pH controller should be switched to the manual mode to avoid integral windup, which would cause overshoot when the pump starts. When the pump is restarted, the controller is returned to the automatic mode at the same output it had when last in automatic; its set point should not be initialized while in manual.
Tracking control of soft dielectric elastomer actuator based on nonlinear PID controller
Published in International Journal of Control, 2022
Peng Huang, Jundong Wu, Chun-Yi Su, Yawu Wang
The reasons for the above settings are as follows. For the proportional control, the function produces a high output when the error is small, which is beneficial to improve the rapidity of the closed-loop control system and facilitates to mitigate the phase-delay caused by the hysteresis nonlinearity of the SDEA. On the other hand, the function produces a low output when the error is large, which is conductive to prevent the high frequency chattering of the SDEA caused by the excessive output. For the integral control and differential control, the function has similar effects. Besides, for the integral control, the function is beneficial to handle the integral windup problem encountered in practical experiments (Gao et al., 2001). More importantly, for the differential control, the function facilitates to produce the favourable differential action in practical experiments of the SDEA. Under the regulation of the differential control, the NEFC can predict the trend of the error and provide the phase-lead compensation to reduce the phase-delay of the SDEA.
Experimental Validation of Adaptive Augmented LQI Control for a 2 DoF Helicopter
Published in IETE Journal of Research, 2022
S. Karthick, S. Kanthalakshmi, E. Vinodh Kumar, V. Joshi Kumar, A. Ezhil Kumaran
Figure 3 shows the control framework for the tracking control of a 2 DoF helicopter. The control scheme consists of an LQR supplemented with an integrator as a baseline controller, a feed-forward controller mainly to compensate for the effect of gravitational torque on the pitch angle, and an adaptive control scheme augmented with the baseline controller to handle the matched uncertainties in the plant. The nonlinear feed-forward control law to counteract the effect of gravity in the 2 DoF helicopter plant is where Kff is the feed-forward gain. Even though adding an integrator improves the steady-state response, the major challenge which we need to address while using an integrator in real-time implementation is the “Integral windup”. When the error between the desired angle and the measured angle is large, the integrator provides a large output voltage to minimize the error, which may lead to actuator saturation [23,24]. Hence, to address this issue, an “anti-windup” compensator using a back-calculation strategy is introduced in the control scheme. Figure 4 shows the schematic of the “anti-windup” control. During actuator saturation, instead of resetting the integrator instantaneously, it is beneficial to reset the integrator dynamically with a time constant Tr. Hence, the anti-windup using back-calculation utilizes an additional feedback path created by the error between the actual actuator input and output. The signal es is modulated by the reset time before being given to the integrator. Without actuator saturation, the error in the additional feedback path is zero and only the normal feedback path regulates the integral gain. However, when the actuator saturates, the additional feedback path gets activated and drives the integrator output dynamically via reset time until the saturation disappears. In the next section, we discuss the adaptive control scheme using MRAC and prove the stability of the closed-loop system using the Lyapunov function (Table 1).