SISTEM KENDALI DAN MONITORING LINGKUNGAN RUMAH KACA DENGAN METODE IoT

Budidaya tanaman menggunakan greenhouse merupakan salah satu metode yang dapat diaplikasikan pada beberapa jenis tanaman. Pada dasarnya parameter yang digunakan pada greenhouse adalah kondisi lingkungan seperti suhu, kelembaban udara, dan intensitas cahaya sehingga tanaman dapat bertumbuh dengan optimal. Namun kondisi tersebut masih belum bisa terpantau dengan baik sehingga pertumbuhan tanaman masih belum bisa maksimal. Oleh karena itu, dibuatlah sebuah sistem yang dapat mengontrol secara otomatis serta dapat memonitor greenhouse dalam jarak jauh. Sistem ini menggunakan NodeMCU sebagai mikrokontroler dan menggunakan sensor DHT11 untuk mengukur suhu dan kelembaban di dalam greenhouse serta menggunakan sensor LDR untuk mengukur intensitas cahaya. Output dari sistem ini meliputi fan, bohlam, motor servo dan water sprayer. Nilai pembacaan sensor akan dibandingkan dengan nilai yang dibutuhkan tanaman untuk menggerakkan output. Pada pengukuran intensitas cahaya menggunakan sistem kendali PID. Setpoint intensitas cahaya yang digunakan adalah 65 lux. Selain itu, sistem ini juga menggunakan metode IoT dengan data dikirimkan ke NodeMCU dan data akan diolah oleh system interface sehingga greenhouse dapat dipantau dari jarak jauh. Sistem ini telah dapat mengendalikan greenhouse secara otomatis sesuai dengan nilai yang dibutuhkan tanaman sawi yaitu untuk suhu adalah 15°C - 25°C, pada kelembaban udara yaitu 80%RH – 90%RH dan pada intensitas cahaya nilai yang dibutuhkan 65lux. Kata Kunci : Greenhouse, Suhu, Kelembaban, NodeMC

Precise Positioning Control Strategy of Machine Tools: A Review

In this article, a precise positioning control strategy for the nonlinearity of machine tools is thoroughly reviewed. Precise positioning is crucial in machine tools industry where nonlinear phenomenon must be considered. Therefore, this paper aims to review various techniques used to enhance the precision of nonlinearity of machine tools. In the introduction, a significant review of machine tools is discussed based on deadzone phenomenon and high bandwidth. After that, linear control strategies are reviewed involving Proportional-Integral-Derivative (PID) and Cascade P/PI controller. This is followed by nonlinear control strategies, Nonlinear PID (NPID), Adaptive NPID (ANPID), Feedforward NPID (FNPID), Adaptive Robust Controller (ARC), Nominal Characteristics Trajectory Following (NCTF) controller and lastly, the fuzzy and neural network control is then reviewed. Finally, conclusions are presented according to the past researches conducted. Further studies regarding the topic can be improved by the implementation of several additional modules such as deadzone and feedforward compensators and disturbance observer that focus on both disturbance forces such as cutting force and friction force

Precise Positioning Control Strategy Of Machine Tools: A Review

In this article, a precise positioning control strategy for the nonlinearity of machine tools is thoroughly reviewed. Precise positioning is crucial in machine tools industry where nonlinear phenomenon must be considered. Therefore, this paper aims to review various techniques used to enhance the precision of nonlinearity of machine tools. In the introduction, a significant review of machine tools is discussed based on deadzone phenomenon and high bandwidth. After that, linear control strategies are reviewed involving Proportional-Integral-Derivative (PID) and Cascade P/PI controller. This is followed by nonlinear control strategies, Nonlinear PID (NPID), Adaptive NPID (ANPID), Feedforward NPID (FNPID), Adaptive Robust Controller (ARC), Nominal Characteristics Trajectory Following (NCTF) controller and lastly, the fuzzy and neural network control is then reviewed. Finally, conclusions are presented according to the past researches conducted. Further studies regarding the topic can be improved by the implementation of several additional modules such as deadzone and feedforward compensators and disturbance observer that focus on both disturbance forces such as cutting force and friction force

An intelligent fault detection system for a heat pump installation based on a geothermal heat exchanger

The heat pump with geothermal exchanger is one of the best methods to heat up a building. The heat exchanger is an element with high probability of failure due to the fact that it is an outside construction and also due to its size. In the present study, a novel intelligent system was designed to detect faults on this type of heating equipment. The novel approach has been successfully empirically tested under a real dataset obtained during measurements of one year. It was based on classification techniques with the aim of detecting failures in real time. Then, the model was validated and verified over the building; it obtained good results in all the operating conditions ranges

Design and evaluation of advanced intelligent flight controllers

Reinforcement learning based methods could be feasible of solving adaptive optimal control problems for nonlinear dynamical systems. This work presents a proof of concept for applying reinforcement learning based methods to robust and adaptive flight control tasks. A framework for designing and examining these methods is introduced by means of the open research civil aircraft model (RCAM) and optimality criteria. A state-of-the-art robust flight controller - the incremental nonlinear dynamic inversion (INDI) controller - serves as a reference controller. Two intelligent control methods are introduced and examined. The deep deterministic policy gradient (DDPG) controller is selected as a promising actor critic reinforcement learning method that currently gains much attraction in the field of robotics. In addition, an adaptive version of a proportional-integral-derivative (PID) controller, the PID neural network (PIDNN) controller, is selected as the second method. The results show that all controllers are able to control the aircraft model. Moreover, the PIDNN controller exhibits improved reference tracking if a good initial guess of its weights is available. In turn, the DDPG algorithm is able to control the nonlinear aircraft model while minimizing a multi-objective value function. This work provides insight into the usability of selected intelligent controllers as flight control functions as well as a comparison to state-of-the-art flight control functions

階層型クラスタリング小脳演算モデルを用いた制御システムの設計

広島大学(Hiroshima University)博士(工学)Doctor of Engineeringdoctora