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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.

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.

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.

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 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.

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.

It is difficult to establish an exact mathematical model for the induction motor and the robustness is poor of the vector control system using PI regulator. This paper adopts the linear active disturbance rejection controller (LADRC) to control inductor motor. LADRC doesn't need the exact mathematical model of motor and it can not only estimate but also compensate the general disturbance that includes the coupling items in model of motor and parameters perturbations by linear extended state observer (LESO), so the rotor flux and torque fully decouple. As a result, the performance is improved. To prove the above control scheme, the proposed control system has been simulated in MATLAB/SIMULINK, and the comparison was made with PID. Simulation results show that LADRC' has better performance and robustness than PID.