Open-loop Tuning Research Articles

Sistem Kendali Suhu Mesin Tetas Telur Ayam Buras Menggunakan Kontroler PID dengan Metode Tuning Ziegler Nichols Open Loop Step Response

An egg incubator is a tool that helps the process of hatching eggs using an electric heater and is equipped with an egg rack that functions to evenly distribute the heat in the incubator. Good temperature control in the hatching process is something that greatly affects the hatching results. In this study, an egg incubator temperature control system was designed using the PID method with the Ziegler Nichols Open Loop Step Response tuning method. The control system is specifically for free-range chicken eggs which require a normal temperature of 37 °C-39 °C. The main control components are the microcontroller, the incandescent lamp heater, and the DHT22 temperature sensor. The open loop test shows a time delay of 20 seconds and a time constant of 385 seconds, so with the Ziegler Nichols open loop tuning method, the values of Kp = 23.1, Ki = 40, and Kd = 10. The test results show that the PID controller can control the temperature properly. In testing the hatching process within 21 days, the temperature control worked well, and the effect of changes in day and night temperature did not significantly affect the performance of the PID controller.

Compensation of bias loads in dynamic positioning of marine surface vessels

This paper investigates different methods for compensating the mean and slowly varying environmental loads, and unmodeled dynamics (bias loads) in dynamic positioning of marine vessels. Four different methods are compared; using the bias estimate from an observer tuned to estimate position and velocity well, using a wave-filtered version of this bias load, using the estimate from a separate observer tuned to work well for estimating the bias loads, and finally traditional integral action on the tracking errors. The results show that the bias from the bias observer is the best solution, both in transients and steady state. Standard integral action matches the steady state performance, but is slower in transients. The estimate from the observer used for position and velocity is fast in transients, but too oscillatory in the bias state. The wave-filtered version of this has less oscillations, but falls short compared to the other methods due to added phase lag from the extra wave filter. Using a bias estimate from an observer has benefits over typical integral action, such as the possibility of offline or open-loop tuning and avoiding integral windup issues. For the comparison study, a 6 DOF simulation model of a supply vessel is used.

Perspective on training fully connected networks with resistive memories: Device requirements for multiple conductances of varying significance

Novel Deep Neural Network (DNN) accelerators based on crossbar arrays of non-volatile memories (NVMs)—such as Phase-Change Memory or Resistive Memory—can implement multiply-accumulate operations in a highly parallelized fashion. In such systems, computation occurs in the analog domain at the location of weight data encoded into the conductances of the NVM devices. This allows DNN training of fully-connected layers to be performed faster and with less energy. Using a mixed-hardware-software experiment, we recently showed that by encoding each weight into four distinct physical devices—a “Most Significant Conductance” pair (MSP) and a “Least Significant Conductance” pair (LSP)—we can train DNNs to software-equivalent accuracy despite the imperfections of real analog memory devices. We surmised that, by dividing the task of updating and maintaining weight values between the two conductance pairs, this approach should significantly relax the otherwise quite stringent device requirements. In this paper, we quantify these relaxed requirements for analog memory devices exhibiting a saturating conductance response, assuming either an immediate or a delayed steep initial slope in conductance change. We discuss requirements on the LSP imposed by the “Open Loop Tuning” performed after each training example and on the MSP due to the “Closed Loop Tuning” performed periodically for weight transfer between the conductance pairs. Using simulations to evaluate the final generalization accuracy of a trained four-neuron-layer fully-connected network, we quantify the required dynamic range (as controlled by the size of the steep initial jump), the tolerable device-to-device variability in both maximum conductance and maximum conductance change, the tolerable pulse-to-pulse variability in conductance change, and the tolerable device yield, for both the LSP and MSP devices. We also investigate various Closed Loop Tuning strategies and describe the impact of the MSP/LSP approach on device endurance.

IMC based robust PID controller tuning for disturbance rejection

It is well-known that the IMC-PID controller tuning gives fast and improved set point response but slow disturbance rejection. A modification has been proposed in IMC-PID tuning rule for the improved disturbance rejection. For the modified IMC-PID tuning rule, a method has been developed to obtain the IMC-PID setting in closed-loop mode without acquiring detailed information of the process. The proposed method is based on the closed-loop step set point experiment using a proportional only controller with gain Kc0. It is the direct approach to find the PID controller setting similar to classical Ziegler-Nichols closed-loop method. Based on simulations of a wide range of first-order with delay processes, a simple correlation has been derived to obtain the modified IMC-PID controller settings from closed-loop experiment. In this method, controller gain is a function of the overshoot obtained in the closed loop set point experiment. The integral and derivative time is mainly a function of the time to reach the first peak (overshoot). Simulation has been conducted for the broad class of processes and the controllers were tuned to have the same degree of robustness by measuring the maximum sensitivity, Ms, in order to obtain a reasonable comparison. The PID controller settings obtained in the proposed tuning method show better performance and robustness with other two-step tuning methods for the broad class of processes. It has also been applied to temperature control loop in distillation column model. The result has been compared to the open loop tuning method where it gives robust and fast response.

A high-speed, tunable silicon photonic ring modulator integrated with ultra-efficient active wavelength control.

We report the first complete 10G silicon photonic ring modulator with integrated ultra-efficient CMOS driver and closed-loop wavelength control. A selective substrate removal technique was used to improve the ring tuning efficiency. Limited by the thermal tuner driver output power, a maximum open-loop tuning range of about 4.5nm was measured with about 14mW of total tuning power including the heater driver circuit power consumption. Stable wavelength locking was achieved with a low-power mixed-signal closed-loop wavelength controller. An active wavelength tracking range of > 500GHz was demonstrated with controller energy cost of only 20fJ/bit.

On QFT tuning of multivariable μ controllers

Optimal control involves feedback problems with explicit plant data and performance criteria for which a solution is either synthesized or ruled out. H ∞ optimal control is probably the most renowned technique in this class where the control synthesis procedure involves various iterations over weightings. In this paper we argue that the integration of optimal control synthesis and manual tuning in the quantitative feedback theory (QFT) design environment enables design of controllers with levels of performance that surpasses what can be achieved using only a single technique. Specifically, using a constructive example, we demonstrate that QFT's open-loop tuning can be more transparent than tuning closed-loop weights. In this example, QFT tunes the μ controller with the objective of reducing control bandwidth while maintaining robust performance ( μ<1).

ADAPTIVE-PASSIVE NOISE CONTROL WITH SELF-TUNING HELMHOLTZ RESONATORS

A tunable Helmholtz resonator and a novel feedback based control law that achieves optimal resonator tuning for time varying tonal noise control applications are presented in this paper. The proposed tuning strategy combines an open loop tuning algorithm based on a simple lumped parameter model of the resonator with a gradient descent approach based on a feedback control structure that ensures robust performance. Optimal tuning of the resonator is achieved despite system uncertainties such as variations in the excitation frequency and environmental changes. An experimental verification of the proposed control law is included. Sound pressure level reductions of up to 30 dB were achieved in an experimental duct system using a tunable Helmholtz resonator.