공압모터의 3-루프 기반 슬라이딩 모드 제어

Through LFC tie-line interchange power of the area within prescribed limit and scheduled frequency of the system can maintain constant. Generally, Integral (conventional) Controllers are used in Load Frequency Controllers but they are quite slow. In this paper, we have used the PID controller for two-area power system in place of conventional controller and by using various adaption techniques we are tuning controllers. In order to verify the performance of PID and conventional controller, comparative results of both controllers are also presented. Overshoot and settling time are better with PID controller. The result demonstrates the effectiveness of PID controller over conventional controller. Different techniques like Ziegler Nichols and Nonlinear Control Design technique are used for comparing the responses of conventional controllers with PID controllers.

In interconnected multi-area power system; the real power generation within each area has to be managed through active power frequency control considering scheduled power exchange. In this work, two different controllers, namely cascaded PD-PI and conventional PID controller are considered for analyzing the dynamic response of the two area interconnected power system. Area-1 consists of two reheat type thermal generating power plant units and area-2 comprises of two non-reheat type thermal power plant units. The controller gains of PD-PI and PID controllers are efficiently tuned by ‘Teaching Learning based Optimization Technique’. In area-1, 1% step load disturbance is applied for analyzing the dynamic performance of the proposed controllers. It is being observed that the cascaded PD-PI controller performs better than the other one in many aspects.

Different from hydraulic servo system, pneumatic servo system uses gas as working medium, through pipeline, control components, and actuators (cylinders, pneumatic motors), which consist of pneumatic servo system, to achieve energy transmission, energy regulation, and energy conversion, and act as a control over a process or a single body.

This article presents a fuzzy fractional-order PID (FFOPID) controller scheme for a pneumatic pressure regulating system. The industrial pneumatic pressure systems are having strong dynamic and nonlinearity characteristics; further, these systems come across frequent load variations and external disturbances. Hence, for the smooth and trouble-free operation of the industrial pressure system, an effective control mechanism could be adopted. The objective of this work is to design an intelligent fuzzy-based fractional-order PID control scheme to ensure a robust performance with respect to load variation and external disturbances. A novel model of a pilot pressure regulating system is developed to validate the effectiveness of the proposed control scheme. Simulation studies are carried out in a delayed nonlinear pressure regulating system under different operating conditions using fractional-order PID (FOPID) controller with fuzzy online gain tuning mechanism. The results demonstrate the usefulness of the proposed strategy and confirm the performance improvement for the pneumatic pressure system. To highlight the advantages of the proposed scheme a comparative study with conventional PID and FOPID control schemes is made.

The inverted pendulum is a classic problem in dynamics and control theory and is widely used as a benchmark for testing control algorithms. It is unstable without control. The process is non linear and unstable with one input signal and several output signals. It is hence obvious that feedback of the state of the pendulum is needed to stabilize the pendulum. The aim of the study is to stabilize the pendulum such that the position of the carriage on the track is controlled quickly and accurately. The problem involves an arm, able to move horizontally in angular motion, and a pendulum, hinged to the arm at the bottom of its length such that the pendulum can move in the same plane as the arm. The conventional PID controller can be used for virtually any process condition. This makes elimination the offset of the proportional mode possible and still provides fast response. In this paper, we have modelled the system and studied conventional controller and LQR controller. It is observed that the LQR method works better compared to conventional controller.

In this study, a nonlinear Archimedes wave swing (AWS) energy conversion system was employed to enable the use of irregular sea waves to provide useful electricity. Instead of the conventional PI controllers used in prior research, this study employed fractional-order PID (FOPID) controllers to control the back-to-back configuration of AWS. The aim was to maximize the energy yield from waves and maintain the grid voltage and the capacitor DC link voltage at predetermined values. In this study, six FOPID controllers were used to accomplish the control goals, leading to an array of thirty parameters required to be fine-tuned. In this regard, a hybrid jellyfish search optimizer and particle swarm optimization (HJSPSO) algorithm was adopted to select the optimal control gains. Verification of the performance of the proposed FOPID control system was achieved by comparing the system results to two conventional PID controllers and one FOPID controller. The conventional PID controllers were tuned using a recently presented metaheuristic algorithm called the Coot optimization algorithm (COOT) and the classical particle swarm optimization algorithm (PSO). Moreover, the FOPID was also tuned using the well-known genetic algorithm (GA). The system investigated in this study was subjected to various unsymmetrical and symmetrical fault disturbances. When compared with the standard COOT-PID, PSO-PID, and GA-FOPID controllers, the HJSPSO-FOPID results show a significant improvement in terms of performance and preserving control goals during system instability

The industrial revolution 4.0 has led to a burst in the development of robotic automation and platforms to increase productivity in the industrial and health domains. Hence, there is a necessity for the design and production of smart and multi-functional tools, which combine several cutting-edge technologies, including additive manufacturing and smart control systems. In the current article, a novel multi-functional biomimetic soft actuator with a pneumatic motion system was designed and fabricated by combining different additive manufacturing techniques. The developed actuator was bioinspired by the natural kinematics, namely the motion mechanism of worms, and was designed to imitate the movement of a human finger. Furthermore, due to its modular design and the ability to adapt the actuator’s external covers depending on the requested task, this actuator is suitable for a wide range of applications, from soft (i.e., fruit grasping) or industrial grippers to medical exoskeletons for patients with mobility difficulties and neurological disorders. In detail, the motion system operates with two pneumatic chambers bonded to each other and fabricated from silicone rubber compounds molded with additively manufactured dies made of polymers. Moreover, the pneumatic system offers multiple-degrees-of-freedom motion and it is capable of bending in the range of −180° to 180°. The overall pneumatic system is protected by external covers made of 3D printed components whose material could be changed from rigid polymer for industrial applications to thermoplastic elastomer for complete soft robotic applications. In addition, these 3D printed parts control the angular range of the actuator in order to avoid the reaching of extreme configurations. Finally, the bio-robotic actuator is electronically controlled by PID controllers and its real-time position is monitored by a one-axis soft flex sensor which is embedded in the actuator’s configuration.

Due to cylinder friction, valve dead zone effect, flow nonlinearity, and measurement noise exist in pneumatic system. In the actual machining, it is difficult to achieve precise control of the pneumatic servo polishing system using the traditional PID algorithm, so the machining accuracy of the blades cannot be guaranteed. A sliding mode control (SMC) method based on high gain observer (HGO) is proposed. The HGO observes the output signal of the system and feeds the estimated signal back to the SMC to ensure that the observation error is uniform ultimate boundedness. Simulation and experiments show that compared with the PID algorithm, the steady-state error of HGO-SMC can be reduced by 66.4%. Compared with SMC(sgn), the high-frequency chattering of HGO-SMC can be reduced by 72.5%. Moreover, the polishing processing experiment shows that HGO-SMC can improve the form and position accuracy by 52.6% and reduce the surface roughness by 55.6%, compared with the original PID control algorithm of the polishing machine tool.

The fuzzy PID controller is proposed to improve the performance of the network pressure-superposed water supply (NPSWS) system. Taking the pump as the load of the asynchronous motor, the whole system can be simplified as a second-order model. On the basis of the model, fuzzy rules are designed to complete on-line parameters adjustments of PID controller. Comparisons are made between the fuzzy PID controller and the conventional PID controller in terms of dynamic performance, steady state performance and noise immunity. The simulation results show that the fuzzy PID controller not only has faster response, slighter overshoot, stronger noise immunity, but also has wider adaptation range and higher accuracy in stability than PID controller.

We aim to develop and evaluate an MR-conditional concentric tube robot for intracerebral hemorrhage (ICH) evacuation. We fabricated the concentric tube robot hardware with plastic tubes and customized pneumatic motors. The robot kinematic model was developed using a discretized piece-wise constant curvature (D-PCC) approach to account for variable curvature along the tube shape, and tube mechanics model was used to compensate torsional deflection of the inner tube. The MR-safe pneumatic motors were controlled using a variable gain PID algorithm. The robot hardware was validated in a series of bench-top and MRI experiments, and the robot's evacuation efficacy was tested in MR-guided phantom trials. The pneumatic motor was able to achieve a rotational accuracy of 0.32°±0.30° with the proposed variable gain PID control algorithm. The kinematic model provided a positional accuracy of the tube tip of 1.39 ± 0.54 mm. The robot was able to evacuate an initial 38.36 mL clot, leaving a residual hematoma of 8.14 mL after 5 minutes, well below the 15 mL guideline suggesting good post-ICH evacuation clinical outcomes. This robotic platform provides an effective method for MR-guided ICH evacuation. ICH evacuation is feasible under MRI guidance using a plastic concentric tube, indicating potential feasibility in future live animal studies.

This paper proposes a method for optimizing the parameters of a PID controller used in Load Frequency Control (LFC) for a simplified power system. The optimization is performed using a Genetic Algorithm (GA), fully implemented in MATLAB. The main objective is to demonstrate that an evolutionarily tuned PID controller can achieve superior performance compared to conventional tuning methods.The power system model includes a first-order turbine and the integration of mechanical power in a single-area power system. Numerical simulations were carried out in MATLAB over a 30-second interval under a step load disturbance with an amplitude of ±5%.The results show a 34% reduction in settling time, a 38% decrease in maximum overshoot, and approximately a 35% reduction in the Integral of Absolute Error (IAE), compared to classic PID control. These results confirm the effectiveness of the genetic algorithm in optimizing frequency control in power systems.The paper highlights the potential of evolutionary techniques in designing robust controllers for critical energy applications.

The photovoltaic (PV) system’s nonlinear nature, uncertainty in temperature, and light intensity in the environment are responsible for the fluctuation of the maximum power point (MPP). Many maximum power point tracking (MPPT) algorithms are proven effective in the literature, including use of artificial intelligence. Proportional integral derivative controller and fuzzy controllers found effective to track MPP. Using the combination of Fuzzy-PID controller, swiftly adapt to changes in the atmosphere to confirm the solar array is continually operating at its highest efficiency with MPP. Easy and effective oscillation elimination is possible with PID control. To regulate the maximum power of the PV system combination of Fuzzy-PID self-tuning controllers is found effective.The goal is to enhance a PV system’s dynamic response to in presence of irradiation and temperature fluctuations. For tuning Fuzzy logic controller (FLC), gain scheduler values of the proportional, integral, and derivative constants are fundamental. The design processes for non-linear conventional PID controllers, FLC, and series combination of Fuzzy-PID controller are discussed. It compares the traditional PID controller, FLC, and Fuzzy-PID controller. The comparison of outputs produced for PID, FLC, and series Fuzzy-PID controller confirms the effectiveness of the algorithm. Use of Fuzzy controller helps in self-tuning of PID controller constant value. MATLAB/Simulink based environment used to perform the simulations.

Generally Quad-rotor type Unmanned Aerial Vehicles are unstable in nature, so to stabilize it, attitude controller is used. This paper presents a Fuzzy Logic based PID control system (FPID) for attitude stabilization of Quad-rotor Unmanned Aerial Vehicle (UAV). The Fuzzy logic controller updates the PID controller gains efficiently in such a way that it efficiently stabilizes the attitude of quad-rotor UAV. This method enhances the capabilities of conventional PID Controller to a dynamic PID Controller. The propose FPID control technique for attitude controlling of Quad-rotor UAV was tested on developed prototype of Quad rotor UAV for experimental purpose. Experimental results proved that proposed Fuzzy Logic based PID (FPID) controller quickly stabilized Quad-rotor UAV and its response time and settling time is reasonably good in attitude stabilization control application.

SummaryProportional Integral Derivative (PID) controllers still represent the core control method for achieving output regulation of either linear or nonlinear systems in the majority of industrial applications. However, conventional PID control cannot guarantee specific state constraint requirements for the plant, when the system introduces uncertainties. In this paper, a novel nonlinear PID control that achieves output regulation and guarantees a desired state limitation below a given value for a wide class of nonlinear systems with constant uncertainties is proposed. Using nonlinear ultimate boundedness theory, it is shown that the proposed state‐limiting PID (sl‐PID) control maintains a given bound for the desired system states at all times, ie, even during transients, whereas an analytic method for selecting the controller gains is also presented to ensure closed‐loop system stability and convergence at the desired equilibrium. Two nonlinear engineering examples that include an electric motor and a dc/dc converter are investigated using the conventional PID and the proposed sl‐PID to validate the superiority of the proposed controller in achieving the desired output regulation with a given bounded state requirement.

Cascade fuzzy control of a tubular chemical reactor