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.

https://www.mdpi.com/1424-8220/20/18/5323/pdf

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

In order to improve the recognition rate of the biometric identification system, the features of each unimodal biometric are often combined in a certain way. However, there are some mutually exclusive redundant features in those combined features, which will degrade the identification performance. To solve this problem, this paper proposes a novel multimodal biometric identification system for face-iris recognition.It is based on binary particle swarm optimization. The face features are extracted by 2D Log-Gabor and Curvelet transform, while iris features are extracted by Curvelet transform. In order to reduce the complexity of the feature-level fusion, we propose a modified chaotic binary particle swarm optimization (MCBPSO) algorithm to select features. It uses kernel extreme learning machine (KELM) as a fitness function and chaotic binary sequences to initialize particle swarms. After the global optimal position (Gbest) is generated in each iteration, the position of Gbest is varied by using chaotic binary sequences, which is useful to realize chaotic local search and avoid falling into the local optimal position. The experiments are conducted on CASIA multimodal iris and face dataset from Chinese Academy of Sciences.The experimental results demonstrate that the proposed system can not only reduce the number of features to one tenth of its original size, but also improve the recognition rate up to 99.78%. Compared with the unimodal iris and face system, the recognition rate of the proposed system are improved by 11.56% and 2% respectively. The experimental results reveal its performance in the verification mode compared with the existing state-of-the-art systems. The proposed system is satisfactory in addressing face-iris multimodal biometric identification.

https://www.mdpi.com/2079-9292/10/2/217/pdf

A Modified Chaotic Binary Particle Swarm Optimization Scheme and Its Application in Face-Iris Multimodal Biometric Identification

A novel computationally efficient machine learning (ML) framework has been developed for constructing the turbulent flow field of single-phase or two-phase particle-liquid flow in a mechanically agitated vessel by feeding a very short-term experimental Lagrangian trajectory. Using a supervised k-nearest neighbours regressor learning algorithm coupled with a Gaussian process, the framework predicts the mean flow and turbulent fluctuations by sharing the statistical features learned from experimental data. The capability of the ML framework is evaluated by comparing the flow dynamics of predicted trajectories to extensive Lagrangian particle tracking measurements under various flow conditions. Local velocity distributions, Lagrangian statistical analysis, solid concentration distributions and phase flow numbers show very good agreement between ML-predictions and experiments. Being accurate, efficient and robust, the ML framework is a powerful tool for analysing and modelling multiphase flow systems using a minimal amount of driver data input which can equally be provided from any reliable numerical simulation, thus avoiding costly experimental measurements.

https://aip.scitation.org/doi/pdf/10.1063/5.0136830

A Data-driven Machine Learning Framework for Modelling of Turbulent Mixing Flows

Orchard spraying can effectively control pests and diseases. Over-spraying commonly results in excessive pesticide residues on agricultural products and environmental pollution. To avoid these problems, variable spraying technology uses target canopy detection to evaluate the leaf area in a canopy and adjust the application rate accordingly. In this study, a mobile LiDAR detection platform was set up to automatically measure point cloud data for a thick canopy in an apple orchard. A test platform was built, and manual measurements of the canopy leaf area were taken. Then, polynomial regression, back propagation (BP) neural network regression, and partial least squares regression (PLSR) algorithms were used to study the relationship between the orchard tree canopy point clouds and leaf areas. The BP neural network algorithm (86.1% and 73.6% accuracies for the test and verification data, respectively) and the PLSR algorithm (78.46% and 60.3%, respectively) performed better than the Fourier function of the polynomial regression (59.73% accuracy). The leaf area model obtained using PLSR was intuitive and simple, while the BP neural network algorithm was more accurate and could meet the requirements for high-precision variable spraying.

/pdf/innovative-leaf-area-detection-models-for-orchard-tree-thick-2fznpo7c.pdf

Innovative Leaf Area Detection Models for Orchard Tree Thick Canopy Based on LiDAR Point Cloud Data

Precision spraying relies on the response of the spraying equipment to the features of the targeted canopy. PWM technology manages the flow rate using a set of electronically actuated solenoid valves to regulate flow rate at the nozzle level. Previous studies have found that PWM systems may deliver incorrect flow rates. The objective of the present study was to characterize the performance of a commercial blast sprayer modified with pulse-width-modulated nozzles under laboratory conditions, as a preliminary step before its further field validation. Four different duty cycles (25 percent, 50 percent, 75 percent and 100 percent) and four different pressures (400 kPa, 500 kPa, 600 kPa and 700 kPa) were combined to experimentally measure the flow rate of each nozzle. Results showed that the PWM nozzles mounted in the commercial blast sprayer, under static conditions, were capable of modulating flow rate according to the duty cycle. However, the reduction of flow rates for the tested duty cycles according to pressure was lower than the percentage expected. A good linear relation was found between the pressure registered by the control system feedback sensor and the pressure measured by a reference conventional manometer located after the pump. High-speed video recordings confirmed the accurate opening and closing of the nozzles according to the duty cycle; however, substantial pressure variations were found at nozzle level. Further research to establish the general suitability of PWM systems for regulating nozzle flow rates in blast sprayers without modifying the system pressure still remains to be addressed.

/pdf/preliminary-evaluation-of-a-blast-sprayer-controlled-by-1ohppff9.pdf

Preliminary Evaluation of a Blast Sprayer Controlled by Pulse-Width-Modulated Nozzles

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.

/pdf/an-intelligent-multi-sensor-variable-spray-system-with-2x827723ni.pdf

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

The invention discloses an automatic walnut sorting system and a control method. The system comprises a feeding hopper and three stages of screening meshes which are arranged sequentially in accordance with the pore diameter from small to large; a receiving hopper is arranged below each screening mesh; a first weight collector is arranged below each receiving hopper, and walnuts from the output ends of the first weight collector are conveyed to a second weight collector through a first conveying belt; and walnuts from the output end of the second weight collector are conveyed to a classifier via a second conveyor belt, and the classifier compares mean weight collected by the first weight collector and the second weight collector with preset weight of walnuts screened by the corresponding screening mesh with the primary pore diameter and judges whether the walnuts belong to qualified products. The system has the advantages of reduced labor cost, high automation degree and improved sorting efficiency; and sorting accuracy is further improved by the three stages of screening meshes and dual weight collection.

Automatic walnut sorting system and control method

The invention discloses walnut sorting equipment and method and a walnut dividing device thereof. Walnuts can be strictly sorted and can be conveniently utilized separately. The walnut sorting equipment comprises a dividing frame, wherein a walnut inlet channel is arranged at the inlet end of the dividing frame; a low-quality walnut channel, a medium-quality walnut channel and a high-quality walnut channel are sequentially and adjacently arranged at the outlet end of the dividing frame; a first partition plate is arranged between the medium-quality walnut channel and the high-quality walnut channel; a second partition plate is arranged between the low-quality walnut channel and the medium-quality walnut channel; the inlet ends of the low-quality walnut channel and the medium-quality walnutchannel communicate with the walnut inlet channel through a connecting channel; a first sealing door is arranged at the inlet end of the first partition plate and is used for sealing the inlet end ofthe connecting channel or the high-quality walnut channel; and a second sealing door is arranged at the inlet end of the second partition plate and is used for sealing the outlet end of the connecting channel or the medium-quality walnut channel. The walnut sorting equipment comprises a feeding hopper, a material distributing device and the walnut dividing device which are sequentially arranged in the feeding direction. The invention also discloses a method for realizing walnut division.

Walnut sorting equipment and method and walnut dividing device thereof

The invention provides a control system of a constant-temperature and constant-humidity air supply device for pharmaceutical equipment. With the control system of the invention adopted, temperature and humidity control precision can be improved; and the performance of the system in tracking outlet air temperature and humidity control is improved. The control system comprises a control unit, a high-power rear heater, a low-power rear heater, an incoming air temperature and humidity sensor (2), a differential pressure sensor (3), a front surface cold temperature and humidity sensor (6), a rear surface cold temperature sensor (9) and an outgoing air dew point and temperature and humidity sensor (13). According to the control system of the invention, the control quantity of an outlet air temperature control loop acts on the high-power rear heater and the low-power rear heater through a split-ranging control algorithm, so that the precision of temperature control over outlet air is improved, and the response time of the temperature control is shortened; existing humidity open-loop control is changed into closed-loop control, and the humidity control effect of a regeneration heater is realized, and therefore, humidity control precision is jointly improved, and the performance of the system in tracking outlet air humidity control is improved.

Huang Jinpen

Papers

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

Automatic walnut sorting system and control method

Walnut sorting equipment and method and walnut dividing device thereof

Control system of constant-temperature and constant-humidity air supply device for pharmaceutical equipment