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Thirty-nine women of reproductive age suffering from chronic pelvic inflammatory disease (PID) for at least two years, previously treated pharmacologically with no effect, were enrolled in a four-week therapeutic protocol consisting of 12 acupuncture treatments performed with the frequency of three per week. In each female patient at baseline and after the study, pain score and the following parameters in blood serum were evaluated: concentration of immunoglobulin M (IgM), albumins, α₁-globulins, α₂-globulins and γ-globulins, erythrocyte sedimentation rate (ESR) and white blood cell (WBC) count. During the study, we obtained a significant drop in ESR and IgM levels together with a rise in γ-globulin concentrations. A significant decrease (from 4.89 ± 0.82 to 0.63 ± 1.05) in pain score was obtained. The other parameters remained unchanged. These results suggest that acupuncture treatment of PID exhibits a clear anti-inflammatory and immunocompetent effect.

Owing to the advantages of small size and high integration that accelerometers made by MEMS technology, the Pulse Width Modulation (PWM) closed-loop feedback interface circuit of a sandwich capacitive accelerometer based on ARM is designed. Based on the design, a simulation by SIMULINK and a PCB board-level circuit are conducted. Simulation and measurement are provided to support the design. The realization of Proportion Integral Differential (PID) control algorithm and the generation of PWM are performed by STM32 microcontroller instead of the circuit in the design. The output form of the interface circuit is PWM with different duty cycle and filtered DC voltage. According to the measurement results, the output of the PWM duty cycle change is 16.9%/g, and the sensitivity is 1.045 V/g.

In this paper, a novel efficient optimization method based on reinforcement learning automata (RLA) for optimum parameters setting of conventional proportional-integral-derivative (PID) controller for AVR system of power synchronous generator is proposed. The proposed method is Combinatorial Discrete and Continuous Action Reinforcement Learning Automata (CDCARLA) which is able to explore and learn to improve control performance without the knowledge of the analytical system model. This paper demonstrates the full details of the CDCARLA technique and compares its performance with Particle Swarm Optimization (PSO) as an efficient evolutionary optimization method. The proposed method has been applied to PID controller design. The simulation results show the superior efficiency and robustness of the proposed method.

In this paper, Lagrangian-based method has been proposed for tuning the parameters of fractional order $P{I}^{\alpha }{D}^{\beta }$ controller. In this method, the five parameters ($K_p$, $K_i$, $K_d$, $\alpha$ and $\beta$) of fractional order $P{I}^{\alpha }{D}^{\beta }$ controller (FOPID) are suitably optimized, and successfully applied to a benchmark stable second-order feedback system. To prove the performance of the proposed method, several state-of-the-art approaches were compared. The computational complexity, robustness and stability analysis has been performed to investigate the performance of all these algorithms. Moreover, the precision and flexibility analysis among all these approaches has also been carried out in this paper. The closed loop response of the second-order bench mark stable plant in Simulink has also been depicted in this paper.

The utilization of unmanned aerial vehicles (UAVs) equipped with advanced machine vision and remote sensing technology has ushered in a transformative era across diverse domains such as agriculture, construction, energy, environmental monitoring, and contemporary medical treatment. Particularly within the realm of modern medical treatment, the integration of drone technology into healthcare service delivery has displayed tremendous promise for enhancing patient outcomes, particularly in geographically challenging areas or during emergent situations.

Quad-rotor UAVs have emerged as prominent contenders owing to their exceptional performance, user-friendly controls, and adaptable functionality. Their quad-rotor configuration enables seamless vertical takeoff and landing, rendering them highly suitable for deployment in demanding environments frequently encountered in the context of modern medical treatment. When combined with cutting-edge machine vision and remote sensing capabilities, quad-rotors exhibit proficiency in executing intricate missions encompassing medical supply delivery, mobile target tracking, anomaly detection, topographical mapping, and even facilitating emergency medical response scenarios. These remarkable capabilities render quad-rotors indispensable tools for healthcare professionals including doctors, nurses, and other dedicated personnel in the provision of high-quality healthcare services within the realm of modern medical treatment.

The design and implementation of a dynamic tracking system predicated on machine vision and remote sensing technology assume paramount importance in ensuring optimal UAV operations in modern medical treatment applications. A meticulously crafted system should possess the capacity to process visual information through sophisticated machine vision algorithms, thereby transforming it into precise control data for the UAV. Employing a remote sensing target detection model serves to accurately locate and identify pertinent information embedded within optical remote sensing imagery, thereby generating a comprehensive output feature map information set. Integration of this information with the cascade PID (Proportion Integral Differential) control algorithm facilitates the realization of a dynamic tracking system tailored specifically for quad-rotor UAVs.

Through meticulous experimentation, the system can be effectively harnessed for portable high-altitude visual detection, thereby endowing healthcare services with promptness and efficiency. For instance, the system can be leveraged to meticulously track the movement of medical supplies during transportation, remotely monitor patients’ vital signs, and even facilitate the expeditious delivery of essential medications or vaccines to individuals situated in remote and inaccessible locations during modern medical treatment. As the demand for healthcare services continues to surge, the prevalence of drones equipped with state-of-the-art machine vision and remote sensing technology within modern medical treatment contexts is expected to increase manifold. Consequently, sustained research and development endeavors dedicated to advancing this technology assume paramount significance, thereby ensuring its seamless and efficacious implementation within the modern medical treatment field.

Let $R={\oplus }_{\alpha \in \mathrm{\Gamma }}{R}_{\alpha }$ be an integral domain graded by a nontrivial torsionless grading monoid $\mathrm{\Gamma }$. Among other things, we show that if $\mathrm{\Gamma }\cap (-\mathrm{\Gamma })=\{0\}$, then every nonzero homogeneous ideal of $R$ is invertible if and only if $R_0$ is a field and $R\cong R_0[X]$, where $X$ is an indeterminate over $R_0$, if and only if $R$ is a Dedekind domain, if and only if $R$ is a PID.

In this study, we focused on the design and control of a single DoF laterally supported knee exoskeleton robot designed to improve the load-carrying and strength capacity of healthy individuals, especially soldiers and workers. First, a nonlinear second-order differential model of the robotic knee orthosis was produced. Then, a single degree of freedom exoskeleton robot was designed and manufactured. An interactive motion control method based on the relative angle measurement principle between the knee joint of the user and the exo-suit knee joint was proposed. The user’s knee joint motion is detected via an IMU sensor, and a controller was provided to allow the exo-suit to track the human knee joint in synchrony. In this study, a conventional PID, SMC, and moving surface SMC controllers were designed, and the controllers’ performance were tested in real-time experiments. The maximum tracking errors in the PID, SMC, and MSMC controllers were 2.631^∘, 1.578^∘, and 1.289^∘, respectively, and the average tracking time errors were 0.10, 0.08, and 0.06 s, respectively. In addition, the designed and produced knee orthosis weighs 2.5 kg, making it one of the lightest and most compact designs for use by healthy individuals. The knee orthosis was made of steel, and thus it is very durable for use in all kinds of terrain conditions (military, industrial, etc.). Experimental findings about the proposed design and control method are analyzed in the results and discussion section.

Unmanned Aerial Vehicles (UAVs) have been recently used for different civilian applications such as remote sensing, search, and rescue (SAR), precision agriculture (PA), etc. A UAVs ability to sense and find targets remotely and, based on that, hover close to the target for a particular action makes it an ideal platform for the aforementioned applications. There has been extensive work carried out in the field of visual-based detection, navigation, and control, but the problem of detecting different ground targets and performing certain actions is still an open research area. This study proposes a novel framework for multiple target detection, recognition, and navigation of the UAV to the desired target and closely inspect it. This proposed framework can be deployed for accurately spot spraying in PA applications or SAR. The target detection and recognition in the framework are achieved through a computationally efficient Convolutional Neural Network (CNN) trained model, whereas the close inspection of the target is achieved through a PID-based tracking algorithm which ensures the UAV hover around the target for few seconds. The developed framework performed the desired objective in five stages employing Lawson’s control theory of sense, process, compare, decide and act. The target detection and recognition in the framework were validated with the field experiment, while the entire framework was validated through a variety of simulation flights conducted in Gazebo and PX4. The experiments’ results showed the versatility of the developed system to many complex missions where the targets are added or removed.

Cerebellar Model Articulation Controller (CMAC) is a kind of adaptive ANN based on local learning, which has the characters of fast convergence speed, strong generation ability, simple structure. While its advantages over conventional control techniques have been recognized in the literature, it has primarily been in system identification and pattern recognition, but not in control application. A novel adaptive learning algorithm and concurrent control based on CMAC and PID is proposed in this paper. The proposed algorithm and controller are applied to two-order nonlinear system. The experimental and simulated results show that performance of the CMAC based controller using the proposed learning algorithm is more stable and effective than that of the conventional controllers.

Due to its varying time delay and parameters, brazing temperature control is the main difficulty in brazing operations. In order to optimize the brazing temperature, the brazing temperature model must first be established. Parameter identification and optimization are effective ways to obtain the precise temperature model of the brazing furnace. In this investigation, the online parameter identification on first order delay temperature model was proposed. Through identifying and calculating for the first order delay temperature model, the mode parameters and delay time were obtained. Next, PID parameters optimization for the traditional objective function was implemented using a genetic algorithm. The overshoot, static error index and rising time in the third stage of the heating requirements can then be controlled well, with the help of the modified objective function.

This paper introduces the PID controller based on the human simulation intelligent control algorithm and analyzes the human simulation intelligent PID control algorithm in the MATLAB simulation environment for comparison with the traditional PID control algorithm. An intelligent PID controller is designed through the simplification of the intelligent PID algorithm into a program run in single-ship. It is has improved control effects and is adaptable to standard serial communication protocol, with characteristics of cheap production and simple configuration.

Against such characteristics as uncertainty of controlled object in the main steam temperature control of thermal power plants, a novel PID control strategy with radial basis function (RBF) network tuning based on quantum-behaved particle swarm optimization (QPSO) algorithm is proposed. The QPSO algorithm is applied to optimize the initial parameters of RBF network, thus achieving dynamic control of the main steam temperature. The proposed controller has a self-learning ability, which can strengthen the system of uncertainties adaptability. Simulation results show that the control system performance is obviously better than the conventional cascade control.

Intelligent UAV(Unmanned Aerial Vehicle) as a new field of artificial intelligence and robotics technology, which has widely received attention by the domestic and foreign industries. In view of the problem of the single function and the poor stability of flight that is widely known, this paper researches on the flight attitude of UAV based on IDE Arduino development platform, and the flight stability of UAV which is optimized through the PID control algorithm and fuzzy control method. At the same time, combined with the open source of Arduino, all kinds of sensors are installed on the UAV to carry out the underlying call, and finally, to realize the intelligent and stable flight control.

A fuzzy-PID control strategy based on T-S fuzzy model is proposed by combining fuzzy control and Conventional PID control. PID controller is developed for each subsystem of T-S fuzzy model. In terms of fuzzy-control theory, each subsystem’s control law and membership function can be used to synthetically calculate the whole control system’s control law, which control the whole system. The fuzzy-PID control strategy is also applied to three-tank water level nonlinear system. The control results show the effectiveness and feasibility of the proposed method.

In order to solve the problem that the steel wire gets loose or broken in Multi-Wire-Saw when it works in a high-speed, this paper introduces a control method based on PLC and servo motor, uses the separated integral PID to control the speed of the steel wire when it’s taken on and off accurately. This method improves the stability of the system, even operating at high-speed multi-wire saw. The tension control system can also be used in papermaking, printing and packaging.

For the need of anti-wind and anti-vibration in transmission line construction and maintenance work, the design of anti-galloping fitting based on PID automatic controlling theory was introduced in this paper. Performance of this PID closed loop adjustment control was revealed as simulation results in the computer-aided software. Receiving displacement in the line from sensors in line as input signal, PID controller would compare the dynamic displacement and static one, and give position signal to the motor. Two kinds of the anti-galloping fitting had been introduced in this paper, which could make the PID principles described earlier more practical and trusted.

After more than ten years' development, the neural network PID control technology, combining intelligent control technology and traditional control theories, has got remarkable achievements both in theory and use. This paper designs the whole production line temperature computer control system; production line crystallization process temperature control adopts multilayer network approximate PID controller based on BP algorithm, which verifies the validity of the control method after simulation comparison; finally, this paper discusses the feasibility of using VB language to realize the control process in RSView32.

The Fusion Advanced Studies Torus (FAST) conceptual has been proposed as possible European ITER Satellite facility with the aim of preparing ITER operation scenarios and helping DEMO design and R&D. Insights into ITER regimes of operation in Deuterium plasmas can be obtained from investigations of nonlinear dynamics that are relevant for the understanding of alpha particle behaviours in burning plasmas by using fast ions accelerated by heating and current drive systems. In this paper the plasma scenarios that can be studied on FAST are presented, with emphasis on the aspect of its flexibility in terms of both performance and physics that can be investigated. Plasma position and shape control studies are also discussed.

The Proper Forcing Axiom is a powerful extension of the Baire Category Theorem which has proved highly effective in settling mathematical statements which are independent of ZFC. In contrast to the Continuum Hypothesis, it eliminates a large number of the pathological constructions which can be carried out using additional axioms of set theory.

The ALICE experiment is devoted to the study of heavy-ion collisions at LHC energies. ALICE is also studying proton-proton collisions both as a comparison with lead-lead collisions and in physics areas where ALICE is competitive with other LHC experiments. The ALICE-HMPID detector performs charged particle track-by-track identification (π and K in the momentum range of 1 < p < 3 GeV/c and p in 1.5 < p < 5 GeV/c) by means of measurement of Cherenkov angle and momentum information provided by the tracking devices. The current results on hadron transverse momentum spectra measured by the HMPID detector in pp collisions at , are shown.