Research on Path Planning Algorithm of Driverless Ferry Vehicles Combining Improved A* and DWA.

In view of the fact that the global planning algorithm cannot avoid unknown dynamic and static obstacles and the local planning algorithm easily falls into local optimization in large-scale environments, an improved path planning algorithm based on the integration of A* and DWA is proposed and applied to driverless ferry vehicles. Aiming at the traditional A* algorithm, the vector angle cosine value is introduced to improve the heuristic function to enhance the search direction; the search neighborhood is expanded and optimized to improve the search efficiency; aiming at the problem that there are many turning points in the A* algorithm, a cubic quasi-uniform B-spline curve is used to smooth the path. At the same time, fuzzy control theory is introduced to improve the traditional DWA so that the weight coefficient of the evaluation function can be dynamically adjusted in different environments, effectively avoiding the problem of a local optimal solution. Through the fusion of the improved DWA and the improved A* algorithm, the key nodes in global planning are used as sub-target punctuation to guide the DWA for local planning, so as to ensure that the ferry vehicle avoids obstacles in real time. Simulation results show that the fusion algorithm can avoid unknown dynamic and static obstacles efficiently and in real time on the basis of obtaining the global optimal path. In different environment maps, the effectiveness and adaptability of the fusion algorithm are verified.

Improved RRT* Algorithm for Disinfecting Robot Path Planning.

In this paper, an improved APF-GFARRT* (artificial potential field-guided fuzzy adaptive rapidly exploring random trees) algorithm based on APF (artificial potential field) guided sampling and fuzzy adaptive expansion is proposed to solve the problems of weak orientation and low search success rate when randomly expanding nodes using the RRT (rapidly exploring random trees) algorithm for disinfecting robots in the dense environment of disinfection operation. Considering the inherent randomness of tree growth in the RRT* algorithm, a combination of APF with RRT* is introduced to enhance the purposefulness of the sampling process. In addition, in the context of RRT* facing dense and restricted environments such as narrow passages, adaptive step-size adjustment is implemented using fuzzy control. It accelerates the algorithm's convergence and improves search efficiency in a specific area. The proposed algorithm is validated and analyzed in a specialized environment designed in MATLAB, and comparisons are made with existing path planning algorithms, including RRT, RRT*, and APF-RRT*. Experimental results show the excellent exploration speed of the improved algorithm, reducing the average initial path search time by about 46.52% compared to the other three algorithms. In addition, the improved algorithm exhibits faster convergence, significantly reducing the average iteration count and the average final path cost by about 10.01%. The algorithm's enhanced adaptability in unique environments is particularly noteworthy, increasing the chances of successfully finding paths and generating more rational and smoother paths than other algorithms. Experimental results validate the proposed algorithm as a practical and feasible solution for similar problems.

Hyperparameter Control Using Fuzzy Logic: Evolving Policies for Adaptive Fuzzy Particle Swarm Optimization Algorithm.

Heuristic optimization methods such as Particle Swarm Optimization depend on their parameters to achieve optimal performance on a given class of problems. Some modifications of heuristic algorithms aim at adapting those parameters during the optimization process. We present a novel approach to design such adaptation strategies using continuous fuzzy feedback control. Fuzzy feedback provides a simple interface where probes are sampled in the optimization process and parameters are fed back to the optimizer. The probes are turned into parameters by a fuzzy process optimized beforehand to maximize performance on a training benchmark. Utilizing this framework, we systematically established 127 different Fuzzy Particle Swarm Optimization algorithms featuring a maximum of 7 parameters under fuzzy control. These newly devised algorithms exhibit superior performance compared to both traditional PSO and some of its best parameter control variants. The performance is reported in the single-objective bound-constrained numerical optimization competition of CEC 2020. Additionally, two specific controls, highlighted for their efficacy and dependability, demonstrated commendable performance in real-world scenarios from CEC 2011.

Fuzzy Control Application to an Irrigation System of Hydroponic Crops under Greenhouse: Case Cultivation of Strawberries (Fragaria Vesca).

Hydroponics refers to a modern set of agricultural techniques that do not require the use of natural soil for plant germination and development. These types of crops use artificial irrigation systems that, together with fuzzy control methods, allow plants to be provided with the exact amount of nutrients for optimal growth. The diffuse control begins with the sensorization of the agricultural variables that intervene in the hydroponic ecosystem, such as the environmental temperature, electrical conductivity of the nutrient solution and the temperature, humidity, and pH of the substrate. Based on this knowledge, these variables can be controlled to be within the ranges required for optimal plant growth, reducing the risk of a negative impact on the crop. This research takes, as a case study, the application of fuzzy control methods to hydroponic strawberry crops (Fragaria vesca). It is shown that, under this scheme, a greater foliage of the plants and a larger size of the fruits are obtained in comparison with natural cultivation systems in which irrigation and fertilization are carried out by default, without considering the alterations in the aforementioned variables. It is concluded that the combination of modern agricultural techniques such as hydroponics and diffuse control allow us to improve the quality of the crops and the optimization of the required resources.

Fuzzy Controller Implemented for Movement of a Tendon-Driven 3D Robotic Lumbar Spine Mechanism.

Notable efforts have been devoted to the development of biomechanical models of the spine, so the development of a motion system to control the spine becomes expressively relevant. This paper presents a fuzzy controller to manipulate the movement of a 3D robotic mechanism of the lumbar spine, which is driven by tendons. The controller was implemented in Matlab/Simulink R2023a software, MathWorks (Brazil), considering mathematical modeling based on the Lagrangian methodology for simulating the behavior of the lumbar spine dynamic movement. The fuzzy controller was implemented to perform movements of two joints of the 3D robotic mechanism, which consists of five vertebrae grouped into two sets, G1 and G2. The mechanism's movements are carried out by four servomotors which are driven by readings from two sensors. For control, the linguistic variables of position, velocity and acceleration were used as controller inputs and the torque variables were used for the controller output. The experimental tests were carried out by running the fuzzy controller directly on the 3D physical model (external to the simulation environment) to represent flexion and extension movements analogous to human movements.

Local Path Planning for Mobile Robots Based on Fuzzy Dynamic Window Algorithm.

Due to the increased employment of robots in modern society, path planning methods based on human-robot collaborative mobile robots have been the subject of research in both academia and industry. The dynamic window approach used in the research of the robot local path planning problem involves a mixture of fixed weight coefficients, which makes it hard to deal with the changing dynamic environment and the issue of the sub-optimal global planning paths that arise after local obstacle avoidance. By dynamically modifying the combination of weight coefficients, we propose, in this research, the use of fuzzy control logic to optimize the evaluation function's sub-functions and enhance the algorithm's performance through the safe and dynamic avoidance of obstacles. The global path is introduced to enhance the dynamic window technique's ability to plan globally, and important points on the global path are selected as key sub-target sites for the local motion planning phase of the dynamic window technique. The motion position changes after local obstacle avoidance to keep the mobile robot on the intended global path. According to the simulation results, the enhanced dynamic window algorithm cuts planning time and path length by 16% and 5%, respectively, while maintaining good obstacle avoidance and considering a better global path in the face of various dynamic environments. It is difficult to achieve a local optimum using this algorithm.

Fuzzy Synchronization of Chaotic Systems with Hidden Attractors.

Chaotic systems are hard to synchronize, and no general solution exists. The presence of hidden attractors makes finding a solution particularly elusive. Successful synchronization critically depends on the control strategy, which must be carefully chosen considering system features such as the presence of hidden attractors. We studied the feasibility of fuzzy control for synchronizing chaotic systems with hidden attractors and employed a special numerical integration method that takes advantage of the oscillatory characteristic of chaotic systems. We hypothesized that fuzzy synchronization and the chosen numerical integration method can successfully deal with this case of synchronization. We tested two synchronization schemes: complete synchronization, which leverages linearization, and projective synchronization, capitalizing on parallel distributed compensation (PDC). We applied the proposal to a set of known chaotic systems of integer order with hidden attractors. Our results indicated that fuzzy control strategies combined with the special numerical integration method are effective tools to synchronize chaotic systems with hidden attractors. In addition, for projective synchronization, we propose a new strategy to optimize error convergence. Furthermore, we tested and compared different Takagi-Sugeno (T-S) fuzzy models obtained by tensor product (TP) model transformation. We found an effect of the fuzzy model of the chaotic system on the synchronization performance.

Research on Clamping Force Control of CVT for Electric Vehicles Based on Slip Characteristics.

The low mechanical efficiency of metal belt's continuously variable transmission (CVT) limits its application in new energy vehicles. To further improve CVT efficiency and reduce the energy consumption of electric vehicles (EVs) with CVT, this paper proposes a pure electric CVT configuration and a clamping force control strategy. The slip characteristics of CVT are obtained through a bench test, the dynamic model of CVT slip is established, and a clamping force fuzzy control strategy is designed. The strategy is studied by simulation under extreme conditions and standard driving cycles. The simulation results show that the proposed clamping force control strategy has good adaptability. Under extreme conditions, this strategy can ensure that CVT does not undergo macro slip, while reducing the clamping force by 12.86-21.65%. Energy consumption per 100 km is 14.90 kWh in NEDC, which is 6.67% lower compared with the traditional strategy. CVT average efficiency and average transmission efficiency increased by 3.71% and 6.40%. The research results demonstrate that adjusting the CVT clamping force through fuzzy control based on the slip rate can improve the CVT efficiency and energy economy of EVs, which provides a certain reference for CVT clamping force control strategy development and the application of CVT on EVs.

Research on Shipborne Helicopter Electric Rapid Secure Device: System Design, Modeling, and Simulation.

The research objective of this paper is to propose a new type of ERSD to solve the problem of the uncontrollable velocity of the claw in the current RSD. Firstly, the working characteristics of the RSD in ASIST are analyzed, and the design scheme of the transmission system of the ERSD is provided. The control system is designed by combining the vector control algorithm with the fuzzy adaptive PID control algorithm. On this basis, the trajectory planning of claw capture velocity is completed. Finally, the dynamics model of the transmission system of the ERSD is built by power bond graph theory, and the system simulation is carried out. The results show that the maximum capture time, velocity, and force were reduced by 47%, 53%, and 80%. In addition, when the ERSD is towing the helicopter, the mechanical claw can still provide good velocity tracking performance under a maximum interference load of 34,000 N.

A Novel Fuzzy Controller for Visible-Light Camera Using RBF-ANN: Enhanced Positioning and Autofocusing.

To obtain high-precision for focal length fitting and improve the visible-light camera autofocusing speed, simultaneously, the backlash caused by gear gaps is eliminated. We propose an improved RBF (Radical Basis Function) adaptive neural network (ANN) FUZZY PID (Proportional Integral Derivative) position closed-loop control algorithm to achieve the precise positioning of zoom and focus lens groups. Thus, the Levenberg-Marquardt iterative algorithm is used to fit the focal length, and the improved area search algorithm is applied to achieve autofocusing and eliminate backlash. In this paper, we initially adopt an improved RBF ANN fuzzy PID control algorithm in the position closed-loop in the visible-light camera position and velocity double closed-loop control system. Second, a similar triangle method is used to calibrate the focal length of the visible-light camera system, and the Levenberg-Marquardt iterative algorithm is used to fit the relation of the zoom potentiometer code values and the focal length to achieve the zoom position closed-loop control. Finally, the improved area search algorithm is used to achieve fast autofocusing and acquire clear images. The experimental results show that the ITAE (integrated time and absolute error) performance index of the improved RBF ANN fuzzy PID control algorithm is improved by more than two orders of magnitude as compared with the traditional fuzzy PID control algorithm, and the settling time is 6.4 s faster than that of the traditional fuzzy PID control. Then, the Levenberg-Marquardt iterative algorithm has a fast convergence speed, and the fitting precision is high. The quintic polynomial fitting results are basically consistent with the sixth-degree polynomial. The fitting accuracy is much better than that of the quadratic polynomial and exponential. Autofocusing requires less than 2 s and is improved by more than double that of the traditional method. The improved area search algorithm can quickly obtain clear images and solve the backlash problem.

Fuzzy Control of Pressure in a Water Supply Network Based on Neural Network System Modeling and IoT Measurements.

As hydroenergetic losses are inherent to water supply systems, they are a frequent issue which water utilities deal with every day. The control of network pressure is essential to reducing these losses, providing a quality supply to consumers, saving electricity and preserving piping from excess pressure. However, to obtain these benefits, it is necessary to overcome some difficulties such as sensing the pressure of geographically distant consumer units and developing a control logic that is capable of making use of the data from these sensors and, at the same time, a good solution in terms of cost benefit. Therefore, this work has the purpose of developing a pressure monitoring and control system for water supply networks, using the ESP8266 microcontroller to collect data from pressure sensors for the integrated ScadaLTS supervisory system via the REST API. The modeling of the plant was developed using artificial neural networks together with fuzzy pressure control, both designed using the Python language. The proposed method was tested by considering a pumping station and two reference units located in the city of João Pessoa, Brazil, in which there was an excess of pressure in the supply network and low performance from the old controls, during the night period from 12:00 a.m. to 6:00 a.m. The field results estimated 2.9% energy saving in relation to the previous form of control and a guarantee that the pressure in the network was at a healthy level.

Design of a Real-Time Salinity Detection System for Water Injection Wells Based on Fuzzy Control.

Salinity is an important index of water quality in oilfield water injection engineering. To address the need for real-time measurement of salinity in water flooding solutions during oilfield water injection, a salinity measurement system that can withstand a high temperature environment was designed. In terms of the polarization and capacitance effects, the system uses an integrator circuit to collect information and fuzzy control to switch gears to expand the range. Experimental results show that the system can operate stably in a high-temperature environment, with an accuracy of 0.6% and an uncertainty of 0.2% in the measurement range of 1-10 g/L.

Design and Implementation of a Position, Speed and Orientation Fuzzy Controller Using a Motion Capture System to Operate a Wheelchair Prototype.

The design and implementation of an electronic system that involves head movements to operate a prototype that can simulate future movements of a wheelchair was developed here. The controller design collects head-movements data through a MEMS sensor-based motion capture system. The research was divided into four stages: First, the instrumentation of the system using hardware and software; second, the mathematical modeling using the theory of dynamic systems; third, the automatic control of position, speed, and orientation with constant and variable speed; finally, system verification using both an electronic controller test protocol and user experience. The system involved a graphical interface for the user to interact with it by executing all the controllers in real time. Through the System Usability Scale (SUS), a score of 78 out of 100 points was obtained from the qualification of 10 users who validated the system, giving a connotation of "very good". Users accepted the system with the recommendation to improve safety by using laser sensors instead of ultrasonic range modules to enhance obstacle detection.

Reliable Control Applications with Wireless Communication Technologies: Application to Robotic Systems.

The nature of wireless propagation may reduce the QoS of the applications, such that some packages can be delayed or lost. For this reason, the design of wireless control applications must be faced in a holistic way to avoid degrading the performance of the control algorithms. This paper is aimed at improving the reliability of wireless control applications in the event of communication degradation or temporary loss at the wireless links. Two controller levels are used: sophisticated algorithms providing better performance are executed in a central node, whereas local independent controllers, implemented as back-up controllers, are executed next to the process in case of QoS degradation. This work presents a reliable strategy for switching between central and local controllers avoiding that plants may become uncontrolled. For validation purposes, the presented approach was used to control a planar robot. A Fuzzy Logic control algorithm was implemented as a main controller at a high performance computing platform. A back-up controller was implemented on an edge device. This approach avoids the robot becoming uncontrolled in case of communication failure. Although a planar robot was chosen in this work, the presented approach may be extended to other processes. XBee 900 MHz communication technology was selected for control tasks, leaving the 2.4 GHz band for integration with cloud services. Several experiments are presented to analyze the behavior of the control application under different circumstances. The results proved that our approach allows the use of wireless communications, even in critical control applications.

In vitro evaluation of multi-objective physiological control of the centrifugal blood pump.

Left ventricular assist devices (LVADs) have been used as a bridge to transplantation or as destination therapy to treat patients with heart failure (HF). The inability of control strategy to respond automatically to changes in hemodynamic conditions can impact the patients' quality of life. The developed control system/algorithm consists of a control system that harmoniously adjusts pump speed without additional sensors, considering the patient's clinical condition and his physical activity. The control system consists of three layers: (a) Actuator speed control; (b) LVAD flow control (FwC); and (c) Fuzzy control system (FzC), with the input variables: heart rate (HR), mean arterial pressure (MAP), minimum pump flow, level of physical activity (data from patient), and clinical condition (data from physician, INTERMACS profile). FzC output is the set point for the second LVAD control schemer (FwC) which in turn adjusts the speed. Pump flow, MAP, and HR are estimated from actuator drive parameters (speed and power). Evaluation of control was performed using a centrifugal blood pump in a hybrid cardiovascular simulator, where the left heart function is the mechanical model and right heart function is the computational model. The control system was able to maintain MAP and cardiac output in the physiological level, even under variation of EF. Apart from this, also the rotational pump speed is adjusted following the simulated clinical condition. No backflow from the aorta in the ventricle occurred through LVAD during tests. The control algorithm results were considered satisfactory for simulations, but it still should be confirmed during in vivo tests.

A PID-Type Fuzzy Logic Controller-Based Approach for Motion Control Applications.

Motion control is widely used in industrial applications since machinery, robots, conveyor bands use smooth movements in order to reach a desired position decreasing the steady error and energy consumption. In this paper, a new Proportional-Integral-Derivative (PID) -type fuzzy logic controller (FLC) tuning strategy that is based on direct fuzzy relations is proposed in order to compute the PID constants. The motion control algorithm is composed by PID-type FLC and S-curve velocity profile, which is developed in C/C++ programming language; therefore, a license is not required to reproduce the code among embedded systems. The self-tuning controller is carried out online, it depends on error and change in error to adapt according to the system variations. The experimental results were obtained in a linear platform integrated by a direct current (DC) motor connected to an encoder to measure the position. The shaft of the motor is connected to an endless screw; a cart is placed on the screw to control its position. The rise time, overshoot, and settling time values measured in the experimentation are 0.124 s, 8.985% and 0.248 s, respectively. These results presented in part 6 demonstrate the performance of the controller, since the rise time and settling time are improved according to the state of the art. Besides, these parameters are compared with different control architectures reported in the literature. This comparison is made after applying a step input signal to the DC motor.

An Intelligent Multi-Sensor Variable Spray System with Chaotic Optimization and Adaptive Fuzzy Control.

During the variable spray process, the micro-flow control is often held back by such problems as low initial sensitivity, large inertia, large hysteresis, nonlinearity as well as the inevitable difficulties in controlling the size of the variable spray droplets. In this paper, a novel intelligent double closed-loop control with chaotic optimization and adaptive fuzzy logic is developed for a multi-sensor based variable spray system, where a Bang-Bang relay controller is used to speed up the system operation, and adaptive fuzzy nonlinear PID is employed to improve the accuracy and stability of the system. With the chaotic optimization of controller parameters, the system is globally optimized in the whole solution space. By applying the proposed double closed-loop control, the variable pressure control system includes the pressure system as the inner closed-loop and the spray volume system as the outer closed-loop. Thus, the maximum amount of spray droplets deposited on the plant surface may be achieved with the minimum medicine usage for plants. Multiple sensors (for example: three pressure sensors and two flow rate sensors) are employed to measure the system states. Simulation results show that the chaotic optimized controller has a rise time of 0.9 s, along with an adjustment time of 1.5 s and a maximum overshoot of 2.67% (in comparison using PID, the rise time is 2.2 s, the adjustment time is 5 s, and the maximum overshoot is 6.0%). The optimized controller parameters are programmed into the hardware to control the established variable spray system. The experimental results show that the optimal spray pressure of the spray system is approximately 0.3 MPa, and the flow rate is approximately 0.08 m3/h. The effective droplet rate is 89.4%, in comparison to 81.3% using the conventional PID control. The proposed chaotically optimized composite controller significantly improved the dynamic performance of the control system, and satisfactory control results are achieved.

The importance of long-term well management in geothermal power systems using fuzzy control: A Western Anatolia (Turkey) case study.

Effective geothermal power generation depends on two main elements: geothermal reservoir management and maintenance of the power plant. Reservoir management consists of both the fluid production and reinjection of brine to the underground. The management of wells is important to ensure the sustainability of the reservoir. Thus, the flow rate control systems are essential to protect geothermal reservoirs under long-term power production. The second issue is the daily change in electricity prices and the load change process is complex because geothermal well controls are not flexible operations. The well management thus requires control approaches, and fuzzy control can be one effective solution. In this study, a fuzzy control system has been developed to control flow rates of the wells in Kizildere geothermal field and its performance has been compared with the real data taken from the Kizildere Power Plant. The results of comparison show that the fuzzy controllers achieved the target energy production in 2 h instead of 5 h, compared to the real data. Based on the real data, the reinjection was only able to stabilize at the end of the fourth hour and the process took only 2 h when using the fuzzy controllers.

Estimating the Orientation of Objects from Tactile Sensing Data Using Machine Learning Methods and Visual Frames of Reference.

Underactuated hands are useful tools for robotic in-hand manipulation tasks due to their capability to seamlessly adapt to unknown objects. To enable robots using such hands to achieve and maintain stable grasping conditions even under external disturbances while keeping track of an in-hand object's state requires learning object-tactile sensing data relationships. The human somatosensory system combines visual and tactile sensing information in their "What and Where" subsystem to achieve high levels of manipulation skills. The present paper proposes an approach for estimating the pose of in-hand objects combining tactile sensing data and visual frames of reference like the human "What and Where" subsystem. The system proposed here uses machine learning methods to estimate the orientation of in-hand objects from the data gathered by tactile sensors mounted on the phalanges of underactuated fingers. While tactile sensing provides local information about objects during in-hand manipulation, a vision system generates egocentric and allocentric frames of reference. A dual fuzzy logic controller was developed to achieve and sustain stable grasping conditions autonomously while forces were applied to in-hand objects to expose the system to different object configurations. Two sets of experiments were used to explore the system capabilities. On the first set, external forces changed the orientation of objects while the fuzzy controller kept objects in-hand for tactile and visual data collection for five machine learning estimators. Among these estimators, the ridge regressor achieved an average mean squared error of 0.077 ∘ . On the second set of experiments, one of the underactuated fingers performed open-loop object rotations and data recorded were supplied to the same set of estimators. In this scenario, the Multilayer perceptron (MLP) neural network achieved the lowest mean squared error of 0.067 ∘ .

Multilevel Fuzzy Control Based on Force Information in Robot-Assisted Decompressive Laminectomy.

The lumbar spinal stenosis (LSS) is a kind of orthopedic disease which causes a series of neurological symptom. Vertebral lamina grinding operation is a key procedure in decompressive laminectomy for LSS treatment. With the help of image-guided navigation system, the robot-assisted technology is applied to reduce the burdens on surgeon and improve the accuracy of the operation. This paper proposes a multilevel fuzzy control based on force information in the robot-assisted decompressive laminectomy to improve the quality and the robotic dynamic performance in surgical operation. The controlled grinding path is planned in the medical images after 3D reconstruction, and the mapping between robot and images is realized by navigation registration. Multilevel fuzzy controller is used to adjust the feed rate to keep the grinding force stable. As the vertebral lamina contains different components according to the anatomy, it has different mechanical properties as the main reason causing the fluctuation of force. A feature extraction method for texture recognition of bone is introduced to improve the accuracy of component classification. When the inner cortical bone is reached, the feeding operation needs to stop to avoid penetration into spinal cord and damage to the spinal nerves. Experiments are conducted to evaluate the dynamic stabilities of the control system and state recognition.