Abstract:A Proportional Integral Derivative (PID) controller is commonly used to carry out tasks like position tracking in the industrial robot manipulator controller; however, over time, the PID integral gain generates degradation within the controller, which then produces reduced stability and bandwidth. A proportional derivative (PD) controller has been proposed to deal with the increase in integral gain but is limited if gravity is not compensated for. In practice, the dynamic system non-linearities frequently are … Show more
“…Based on the signal vectors from the current observation (3) and the current reward function (2), the decision block (4) derives a new current action. The action of the inference block can be terminated at any time by block (6). After superimposing saturation on the computed action (which is related to mapping the physical constraints of the signal magnitude), the control vector (5) is derived.…”
Section: Resultsmentioning
confidence: 99%
“…At certain iteration intervals, the actor is updated using the gradient policy by computing the expected return from the initial distribution J, according to relation (6).…”
Section: Methodsmentioning
confidence: 99%
“…The problem of optimal control of nonlinear multi-input multi-output (MIMO) systems is very complex [1][2][3][4]. Currently, many academic centers and private entrepreneurs are actively engaged in this topic [5][6][7][8]. In conventional and renewable energy, such circuits are very common.…”
This article presents the results of the optimization of steam generator control systems powered by mixtures of liquid fuels containing biofuels. The numerical model was based on the results of experimental research of steam generator operation in an open system. The numerical model is used to build control algorithms that improve performance, increase efficiency, reduce fuel consumption and increase safety in the full range of operation of the steam generator and the cogeneration system of which it is a component. In this research, the following parameters were monitored: temperature and pressure of the circulating medium, exhaust gas temperature, oxygen content in exhaust gas, percentage control of oil burner power. Two methods of controlling the steam generator were proposed: the classic one, using the PID regulator, and the advanced one, using artificial neural networks. The work shows how the model is adapted to the real system and the impact of the control algorithms on the efficiency of the combustion process. The example is considered for the implementation of advanced control systems in micro-, small- and medium-power cogeneration and trigeneration systems in order to improve their final efficiency and increase the profitability of implementation.
“…Based on the signal vectors from the current observation (3) and the current reward function (2), the decision block (4) derives a new current action. The action of the inference block can be terminated at any time by block (6). After superimposing saturation on the computed action (which is related to mapping the physical constraints of the signal magnitude), the control vector (5) is derived.…”
Section: Resultsmentioning
confidence: 99%
“…At certain iteration intervals, the actor is updated using the gradient policy by computing the expected return from the initial distribution J, according to relation (6).…”
Section: Methodsmentioning
confidence: 99%
“…The problem of optimal control of nonlinear multi-input multi-output (MIMO) systems is very complex [1][2][3][4]. Currently, many academic centers and private entrepreneurs are actively engaged in this topic [5][6][7][8]. In conventional and renewable energy, such circuits are very common.…”
This article presents the results of the optimization of steam generator control systems powered by mixtures of liquid fuels containing biofuels. The numerical model was based on the results of experimental research of steam generator operation in an open system. The numerical model is used to build control algorithms that improve performance, increase efficiency, reduce fuel consumption and increase safety in the full range of operation of the steam generator and the cogeneration system of which it is a component. In this research, the following parameters were monitored: temperature and pressure of the circulating medium, exhaust gas temperature, oxygen content in exhaust gas, percentage control of oil burner power. Two methods of controlling the steam generator were proposed: the classic one, using the PID regulator, and the advanced one, using artificial neural networks. The work shows how the model is adapted to the real system and the impact of the control algorithms on the efficiency of the combustion process. The example is considered for the implementation of advanced control systems in micro-, small- and medium-power cogeneration and trigeneration systems in order to improve their final efficiency and increase the profitability of implementation.
“…In past decades, many studies have been conducted to solve such problems (time-consuming, costly, etc. ; Fatan et al, 2013 ; Kuantama et al, 2017 ; Noordin et al, 2017 ; Rouhani et al, 2017 ; Sumardi and Riyadi, 2017 ; Prayitno et al, 2018 ; Thanh and Hong, 2018 ; Chen et al, 2019 ; Rabah et al, 2019 ; Soriano et al, 2020 ). The Ziegler Nichols method is a well-known for tuning PID parameters (Azman et al, 2017 ).…”
This study presents an online tuning proportional-integral-derivative (PID) controller using a multilayer fuzzy neural network design for quadcopter attitude control. PID controllers are simple but effective control methods. However, finding the suitable gain of a model-based controller is relatively complicated and time-consuming because it depends on external disturbances and the dynamic modeling of plants. Therefore, the development of a method for online tuning of quadcopter PID parameters may save time and effort, and better control performance can be achieved. In our controller design, a multilayer structure was provided to improve the learning ability and flexibility of a fuzzy neural network. Adaptation laws to update network parameters online were derived using the gradient descent method. Also, a Lyapunov analysis was provided to guarantee system stability. Finally, simulations concerning quadcopter attitude control were performed using a Gazebo robotics simulator in addition to a robot operating system (ROS), and their results were demonstrated.
“…e presentation of a nonlinear system with the noninteger derivative is named fractional-order system (FOS). ere are many applications of FOSs on real-world fields, whether in signal processing, chemistry, electricity, thermal, or control theory, for example, observer design [1][2][3], finite-time stability [4], fault estimation [5], and asymptotic stability [6][7][8]. In fact, with regard to observer design, authors in [1] presented a fractional-order observer design for fractionalorder nonlinear systems.…”
In this paper, the controllability of differential systems with the general conformable derivative is studied. By elaborating the rank criterion and the conformable Gram criterion, sufficient and necessary conditions to investigate that a linear general conformable system is null completely controllable are given. We obtain a full generalization to the general conformable fractional-order system case. In addition, Krasnoselskii’s fixed point theorem to obtain a complete controllability result for a semilinear general conformable system is applied.
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