Design Of Automatic Control Research Articles

Adaptive event-triggering mechanism based Takagi-Sugeno fuzzy automatic generation controller design for offshore wind power system

Interconnected offshore wind power system (OWPS) attracts widespread attention due to its special advantage. With the development of wind power technology, higher requirements are put forward for the reliability of networked OWPS. Considering the nonlinear characteristics of OWPS and limited network bandwidth resource, an adaptive multi-event triggering mechanism based distributed Takagi-Sugeno fuzzy robust H∞ control scheme is investigated in this paper. A modeling method for OWPS integrating communication constraints is proposed. By Lyapunov stability theory, the sufficient condition for the asymptotical stability of OWPS has been derived. Compared with traditional control scheme, the proposed Takagi-Sugeno fuzzy controller provides smoother output power, smaller torque oscillation and higher wind energy utilization. Furthermore, the co-design strategy increases the utilization rate of network bandwidth. The feasibility of adaptive multi-event triggering mechanism based Takagi-Sugeno fuzzy controller is demonstrated through a simulation example.

On the assessment of meta-heuristic algorithms for automatic voltage regulator system controller design: a standardization process

On the assessment of meta-heuristic algorithms for automatic voltage regulator system controller design: a standardization process

Optimal design of automatic generation control based on BBPSO-tuned PI for a restructured environment

ABSTRACT This paper intends to model an AGC regulator for a restructured environment using Bare Bone Particle Swarm Optimization (BBPSO). The gain-controlled Proportional–Integral (PI) Controller is designed here to enhance the performance of the BBPSO algorithm along with Gaussian distribution. The practical difficulty in handling the area control error to zero is the sudden variations in load. In practice, the tremendous contribution of deregulation in the power sector causes volatility in frequency and tie-line power deviations. To ensure the robustness of the proposed controller, three different cases of power system transactions have been considered. The performance has been validated by comparing it with Real Coded Genetic Algorithm-tuned PI controller (RCGA-PI) and Differential Evolutionary Algorithm-tuned PI controller (DE-PI) for the five area Thermal-Thermal generation test system. Moreover, the dynamic performance of an extensive range of demands and disturbances of all areas like settling time and overshoot against parametric precariousness has been done on the proposed test system.

Design and Fabrication of Automatic Temperature Control for Chicken Shade

Design and Fabrication of Automatic Temperature Control for Chicken Shade

Load Frequency Control and Optimization of Load Allocation for Multi-Area Power System with Electric Vehicle Charging Load

The integration of electric vehicles (EVs) into power systems has introduced new challenges for load frequency control due to the additional charging load they impose. This research article investigates the design and analysis of automatic load frequency control in a two-area power system, considering the presence of EV charging load. The study employs Artificial Neural Network (ANN) based PI control to manage both traditional load demand and the dynamic charging requirements of EVs. Maintaining a stable power system frequency and balancing generation with the EV charging load have become crucial tasks. Automatic Generation Control (AGC) or Load Frequency Control (LFC) systems need to adapt and account for the variability and uncertainty associated with EV charging patterns. The integration of ANN-based PI control provides an intelligent and adaptive approach to address these challenges. Using MATLAB, a power system model is simulated to evaluate the effectiveness of the proposed control scheme. This investigation conducts a comparative analysis of the system's frequency responses under various scenarios, including different EV charging load profiles. It highlights the benefits and challenges of utilizing ANN-based PI control to manage the combined load of traditional demand and EV charging. Moreover, the load distribution among the distribution stations varied from 0.08% to 12.50% when compared between particle swarm optimization and genetic algorithm, respectively. By considering the dynamic interaction between power system operation and EV charging, this research aims to enhance the reliability, efficiency and sustainability of power systems in the context of evolving transportation trends and the increasing electrification of vehicles.

Contribution to the Dimensioning of Measurement Station for DC motors

This article focuses on ongoing research for dimensioning the Automatic Identification and Control Design Station for DC motors. The aim is to develop a device capable of automatically identifying DC permanent magnet motors and designing control suited for their diverse applications. The proposed measurement platform comprises essential components, including a tested motor, H-bridge, load motor, microcontroller, rotary encoder, power supply, and torque sensor. The dimensioning process carefully considers parameters like sampling period and encoder increments, crucial for accurate motor testing and control design. Authors strive to develop a universal methodology applicable to a broad range of DC motor parameters. The findings offer valuable insights into designing and dimensioning the measurement platform, laying a foundation for future research.

Automatic Generation Control Ancillary Service Cost-Allocation Methods Based on Causer-Pays Principle in Electricity Market

The electric power system is rapidly transforming to address the urgent need for decarbonization and combat climate change. Integration of renewable energy sources into the power grid is accelerating, creating new challenges such as intermittency and uncertainty. To address these challenges, this paper proposes a new design of automatic generation control (AGC) ancillary service cost allocation based on the causer-pays rule. The proposed design treats reserves as inventory and aims to minimize them by allocating costs among consumers based on the causative factors for AGC operation. Two cost-allocation methods based on the causer-pays principle are introduced. The first method distributes costs according to the changes in loads causing ancillary service operation, while the second method considers opportunity costs. The case study on the IEEE 39 Bus System demonstrates that the proposed methods incentivize consumers to minimize volatility, resulting in reduced reserve requirements for system operation. In particular, the opportunity cost-based approach encourages loads and variable renewable energy (VRE) to actively reduce volatility, resulting in more efficient power system operation. In conclusion, the novel AGC ancillary service cost allocation methods offer a promising strategy for minimizing spinning reserves, increasing the power system’s efficiency, and incentivizing consumers to actively participate in frequency regulation for a more sustainable and reliable electricity market.

Federated Reinforcement Learning for Collective Navigation of Robotic Swarms

The recent advancement of Deep Reinforcement Learning (DRL) contributed to robotics by allowing automatic controller design. The automatic controller design is a crucial approach for designing swarm robotic systems, which require more complex controllers than a single robot system to lead a desired collective behaviour. Although the DRL-based controller design method showed its effectiveness for swarm robotic systems, the reliance on the central training server is a critical problem in real-world environments where robot-server communication is unstable or limited. We propose a novel Federated Learning (FL) based DRL training strategy (FLDDPG) for use in swarm robotic applications. Through the comparison with baseline strategies under a limited communication bandwidth scenario, it is shown that the FLDDPG method resulted in higher robustness and generalisation ability into a different environment and real robots, while the baseline strategies suffer from the limitation of communication bandwidth. This result suggests that the proposed method can benefit swarm robotic systems operating in environments with limited communication bandwidth, e.g., in high-radiation, underwater, or subterranean environments.

Application of floating recirculation coefficient in automatic mine ventilation control design

Application of floating recirculation coefficient in automatic mine ventilation control design

Design and Development of Swimming Pool Water pH Level Monitoring System and Automatic Selenoid Valve Control Based on the Internet of Things

A device for monitoring the pH level of swimming pool water and an IoT-based automatic solenoid valve control has been specially designed. This system consists of several main components, including a pH sensor that continuously monitors the pH level in swimming pool water. This sensor will be connected to the Esp32 Board microcontroller which has been programmed to retrieve pH data periodically. The collected data will be sent via an internet connection to the cloud platform and can then be accessed via a mobile application to display real-time pH level messages. In addition, this system is also equipped with automatic solenoid valve control. Based on the measured pH data, the system will be able to make a decision to open or close the solenoid valve. If the pH level is outside the set limit, the system will automatically activate the solenoid valve to open the floodgates for filling water into the swimming pool, thus maintaining the pH balance automatically. In the design system for monitoring the pH level of swimming pool water and controlling this automatic solenoid valve using a pH-014 sensor which functions to detect the pH level in swimming pool water.

Design and Stability Analysis of a Digital Automatic Power Control Based on a PI Controller for Laser Drivers

Laser diodes are widely used in research and industrial applications in areas such as measurements, communications and health. In most of these applications, stability in the emitted light power is required. This can be realized by modifying the internal parameters, such as the current supply, by using an analog automatic power control (APC). This research presents the design and analysis of a feedback laser driver (digital APC system) based on a proportionall–integral (PI) controller. The controller’s theoretical design acting on the supply current in a laser was obtained by algebraically solving the general equations of a PI controller over a laser described as a steady-state system. The required steady-state model can be determined from the lightl–current curve obtained either from the laser data sheet or experimentally. A posterior numerical analysis shows that the proportional gain of the PI controller is only limited numerically by the reciprocal of the slope efficiency of the laser when the characteristic time of the system is greater than the sampling period. Finally, the APC model was tested in an experimental setting using a laser diode ADL-65052TL at several temperatures. The results show that the proposed relations for the proportional gain and the integral time are valid, achieving the desired power stability with a drift of less than 0.1%.

Experimental Research on Motion Analysis Model and Trajectory Planning of GLT Palletizing Robot

To improve wood structure processing efficiency, a palletizing robot suitable for loading and unloading glued laminated timber (GLT) has been developed. The robot comprises a six-axis connecting rod mechanism and a sponge sucker as a grasping actuator, which can enable the intelligent automatic loading and unloading and palletizing operations for small-sized GLT. Matlab robotics was used to construct the kinematic model of the GLT loading and unloading robot. Based on Matlab and Monte Carlo methods, the robot workspace was simulated and analyzed to determine the scope of the robot workspace. Using the high-order quintic and sixtic polynomial curve interpolation method, the trajectory of wood structure parts in the process of loading and unloading operations was planned, respectively, under the two conditions of staying and not staying. Tests verified that the simulation results of the pose of the end-effector were consistent with the actual pose of the robot. The robot’s working range could be analyzed intuitively and effectively. The robot’s operation trajectory planning provides data support and a parameter basis for the automatic control and program design of a loading, unloading and palletizing robot.

Design of Automatic Pond Water Quality Control in Koi Fish Farm

Koi fish is a type of freshwater ornamental fish that has high economic value. Koi fish have attractive body colors and ideal body shapes so they have good sales prospects. However, koi fish farming has several obstacles, one of which is the low recovery rate and the relatively slow growth of fish. Water quality is the main parameter in the success of a fishing effort. Therefore, this study designed a tool that can help koi fish farmers in unifying the quality of pond water so that koi fish can grow and develop properly. This monitoring system is designed by implementing an automatic control monitoring system to produce good water quality for koi fish farming. Data obtained from water quality monitoring are parameters of pH, ammonia, and water temperature. The results of this cultivator's research can monitor the quality of fish pond water and immediately get water quality control automatically.

A Novel Toolbox for Automatic Design of Fractional Order PI Controllers Based on Automatic System Identification from Step Response Data

This paper describes a novel automatic control toolbox, designed for non-experienced practitioners. Fractional order (FO) controllers are easily tuned with the main purpose of easy practical implementation. Experimental step data are required for the automatic FO controller tuning. An embedded system identification algorithm uses the step data to obtain a process model as a second order plus dead-time (SOPDT) system. Finally, the FO controller is computed based on the previously estimated SOPDT model in order to fulfil a set of user-imposed frequency domain performance specifications: phase margin, gain crossover frequency and gain margin maximization. Experimental step response data from a strongly nonlinear vertical take-off and landing unit have been used to design an FO controller using the toolbox. The experimental closed loop results validate the proposed toolbox. The end result is a user-friendly automatic fractional order controller tuning with endless possibilities of real-world applicability.

Evolutionary design of marginally robust multivariable PID controller

Two Margin-based design methods for tuning of decentralized PI controllers for multivariable systems are presented. The first method is predefined marginally robust controller (PMR) which is based on solving a constrained optimization problem to satisfy the desired values of the multivariable phase and gain margins that are set based on the designer’s knowledge of the working condition of the plant. The second method is self-setting marginally robust (SMR) that is an automatic controller design procedure. This method, by solving a many-objective optimization problem (MaOP), sets controller coefficients and finds optimum gain and phase margins. It offers a trade-off between tracking, interaction, and robustness. Evolutionary algorithm is applied to solve the problems and disk margin to define the multivariable stability margins. Proposed methods by combining the simplicity of margin-based design and effectiveness of optimization problems offer a practical solution in tuning the industrial multivariable controllers. Simulation results show that these methods are simple, practical, and at the same time, comparable with other robust multivariable controllers.

Consequences of an Analysis Using Biblical Analogies for Automated Vehicle Control Design

"The paper proposes an analysis of learning-based approaches for automated vehicle control systems from an ethical viewpoint. An analysis using analogies between selected biblical texts and operation concepts of learning-based approaches is performed. Thus, analogies for supervised, unsupervised, and reinforcement learning-based approaches are created. Through the analogies, the root of the automatic control design problems, i.e. forming objective functions, on a theological level is explored. The analysis leads to three consequences, which are related to the difficulty of forming control objective, the difficulty of considering human objectives in control, and the necessity of viewing systems in all their complexity. The paper proposes the application of the consequences in an illustrative route selection vehicle control example. A multi-layer control concept involving the consequences of the analysis is proposed, with which some ethical challenges of the selected control problem can be handled. Keywords: biblical analogies, automated vehicle control, ethical challenges, machine learning "

Barrier Lyapunov function based fixed-time control for automatic carrier landing with disturbances

This paper studies the automatic carrier landing controller design problem with output constraint and fixed-time convergence requirements. The proposed flight controller is separated into guidance, attitude control, and approach power compensation system. In each submodule, universal barrier Lyapunov function and fixed-time control scheme are applied in the controller design. In addition, adaptive estimation method is incorporated to improve the robustness to the external disturbances. The proposed controller ensures the position tracking error, attitude tracking error, and angle of attack tracking error not violate the predesigned boundaries, while the tracking errors will converge to a small set around zero in fixed-time. The output constraint and fixed-time convergence properties of the proposed controller improve the landing security to a narrow flight deck in a limited operation time. The simulation results verify the proposed controller in automatic carrier landing mission.

Data-driven simultaneous identification of the 6DOF dynamic model and wave load for a ship in waves

In marine operations, the performance of model-based automatic control design and decision support systems highly relies on the accuracy of the representative mathematical models. Model fidelity can be crucial for safe voyages and offshore operations. This paper proposes a data-driven parametric model identification of a ship with 6 degrees of freedom (6DOF) exposed to waves using sparse regression according to the vessel motion measurements. The features of the complex ship dynamics are extracted and expressed as a linear combination of several functions. Thruster inputs and environmental loads are considered. The hydrodynamic coefficients and wave-induced loads are simultaneously estimated. Unlike earlier studies using a limited number of unknown functions, a library of abundant candidate functions is applied to fully consider the coupling effects among all DOFs. The benefit of the proposed method is that it does not require the exact construction of the library functions. Based on the estimated model, short-term motion prediction is achievable. The algorithm is verified through experiments. The method can be extended to other types of floating structures.

A Theoretical Scientific Programming Framework for Application of Linear Matrix Transformation in Plane Computer Graphics

Plane computer graphics are basic information carriers in many industrial scenarios, such as engineering simulation, automatic control, and software design. Plane computer graphics are generally a kind of digital signals guided by mathematical symbols, and each vertex of a plane computer graph forms a graph matrix. Therefore, linear matrix transformation serves as the most common algorithmic unit to realize various information processing operations. To improve ease of graph matrix computing in practical engineering scenarios, this paper proposes a theoretical scientific programming framework for application of linear matrix transformation in plane computer graphics. Firstly, theoretical basis of linear matrix transformation in homogeneous plane coordinates is displayed and analyzed. Then, the universal theorem about linear transformation of graph matrices is deduced, and corresponding proofs are also given. Finally, a case study is set up to demonstrate the main workflow of the proposed theoretical scientific programming framework. The simulative results reveal feasibility of the proposal.

Adaptive terminal sliding mode control of a regulated multidimensional electrospinning device for polymeric structures

This study intends to present an automatic control design for a Cartesian electrospinning system with a dual moving platforms for the collector and the syringe subsystems which are mobilized using linear actuators. The proposed device is including a set of dual robotized structures, that is also carrying a structure to create the Taylor cone that regulates the nano-fiber deposition over the supporting platform. The suggested control form considers the synchronization between the syringe tip and the collector using a cascade tracking structure. A class of distributed state-dependent terminal sliding mode control form solves the tracking problem that yields the syringe-collector pair moves in a synchronized form tracking a bidimensional design that should be reproduced with the fibers deposition made of polyvinyl acid–based polymer. This controller also considers the working space restrictions in the Cartesian electrospinning system device using state-dependent gains which are obtained using a control barrier Lyapunov function. The application of the second stability method of Lyapunov serves to both design the state-dependent gains and prove that the tracking error trajectory is ultimate bound. The proposed controller is evaluated using a cyber-physical representation of the Cartesian electrospinning system device where the proposed controller is evaluated. This evaluation considers a comparison of the control effectiveness (using the root mean square value of the tracking error) with a traditional non-adaptive state feedback and a first-order sliding mode control. This comparison shows that the proposed adaptive controller induces the smallest root mean square among the tested control forms while reducing the magnitude of the overshoot for each actuator.