Search Results (23)

In order to improve mathematical model accuracy of electromechanical actuator (EMA) and solve the problems of low-frequency response and large overshoot for nonlinear systems by using traditional proportional integral derivative (PID) algorithm, a fuzzy single neuron (FSN)-PID algorithm is proposed. Firstly, a complete multi-nonlinear dynamic model of EMA is constructed, which introduces internal friction and current limiter of brushless direct current motors (BLDCMs), dead zone backlash of gear trains, and LuGre friction between output shaft and fin. Secondly, a FSN-PID controller is introduced into the automatic position regulator (APR) of EMA control system, where the gain coefficient K of SN algorithm is adjusted by fuzzy control, and the stability of the controller is proved. In addition, simulations are conducted on the response effect of different fin positions under different algorithms for the analysis of the 6° fin position response; it can be concluded that the rise time with FSN-PID algorithm can be reduced by about 4.561% compared to PID, about 1.954% compared to fuzzy (F)-PID, about 0.875% compared to single neuron (SN)-PID, and about 0.380% compared to back propagation (BP)-PID. For the 4°-2 Hz sine position tracking analysis, it can be concluded that the minimum phase error of FSN-PID algorithm is about 0.4705 ms, which is about 74.44% smaller than PID, about 73.43% smaller than F-PID, about 17.24% smaller than SN-PID, and about 10.81% smaller than BP-PID. Finally, wind tunnel experiments investigate the actual high dynamic flight environment and verify the excellent position tracking ability of FSN-PID algorithm. Full article

This paper presents a microcontroller-controlled closed-loop fluxgate current sensor utilizing digital proportional–integral–derivative (PID) control with a single-neuron-based self-pre-optimization algorithm. The digital PID controller within the microcontroller (MCU) regulates the drive circuit to generate a feedback current in the feedback winding based on the zero-flux principle in a closed-loop system. This feedback current is proportional to the measured external current, thereby achieving magnetic compensation. Although PID parameters can be determined using heuristic approaches, empirical formulas, or model-based methods, these techniques are often labor-intensive and time-consuming. To address this challenge, this study implements a single-neuron-based self-pre-optimization algorithm for PID parameters, which autonomously identifies the optimal values for the closed-loop system. Once the PID parameters are optimized, a conventional positional PID algorithm is employed for the closed-loop control of the fluxgate current sensor. The experimental results show that the developed digital closed-loop fluxgate sensor has a non-linearity within 0.1% at the full scale in the measuring ranges of 0–1 A and 0–10 A DC current, with an effective response time of approximately 120 ms. The limitation of the sensors’ response time is found to be ascribed to its open-loop measuring circuit. Full article

In the present study, to address the issue of flow rate instability in the flow boiling experimental system, a flow rate adaptive control system is developed using a single-neuron PID adaptive algorithm, enhanced with the whale optimization algorithm (WOA) for parameter tuning. A recursive least-squares online identification method is integrated to adapt to varying operating conditions. The simulation results demonstrate that in step response the WOA-improved single-neuron PID significantly mitigates the overshoot, with a mere 0.31% overshoot observed, marking a reduction of 98.27% compared to the traditional PID control. The output curve of the WOA-improved single-neuron PID closely aligns with the sinusoidal signal, exhibiting an average absolute error of 0.120, which is lower than that of the traditional PID (0.209) and fuzzy PID (0.296). The WOA-improved single-neuron PID (1.01 s) exhibited a faster return to a stable state compared to the traditional PID (2.46 s) and fuzzy PID (1.28 s). Finally, the effectiveness of the algorithm is validated through practical application. The results demonstrate that, compared to traditional PID and single-neuron PID algorithms, the WOA-improved single-neuron PID algorithm achieves an average flow stability of 9.9848 with a standard error of 0.0914394. It exhibits superior performance, including faster rise and settling times, and higher stability. Full article

To tackle the challenges of poor stability during real-time random gait switching and precise trajectory control for hexapod robots under limited stride and steering conditions, a novel real-time replanning gait switching control strategy based on an omnidirectional gait and fuzzy inference is proposed, along with an attitude control method based on the single-neuron adaptive proportional–integral–derivative (PID). To start, a kinematic model of a hexapod robot was developed through the Denavit–Hartenberg (D-H) kinematics analysis, linking joint movement parameters to the end foot’s endpoint pose, which formed the foundation for designing various gaits, including omnidirectional and compound gaits. Incorporating an omnidirectional gait could effectively resolve the challenge of precise trajectory control for the hexapod robot under limited stride and steering conditions. Next, a real-time replanning gait switching strategy based on an omnidirectional gait and fuzzy inference was introduced to tackle the issue of significant impacts and low stability encountered during gait transitions. Finally, in view of further enhancing the stability of the hexapod robot, an attitude adjustment algorithm based on the single-neuron adaptive PID was presented. Extensive experiments confirmed the effectiveness of this approach. The results show that our approach enabled the robot to switch gaits seamlessly in real time, effectively addressing the challenge of precise trajectory control under limited stride and steering conditions; moreover, it significantly improved the hexapod robot’s dynamic stability during its motion, enabling it to adapt to complex and changing environments. Full article

This paper presents the constitution of a computationally intelligent self-adaptive steering controller for a lawn-mowing robot to yield robust trajectory tracking and disturbance rejection behavior. The conventional fixed-gain proportional–integral–derivative (PID) control procedure lacks the flexibility to deal with the environmental indeterminacies, coupling issues, and intrinsic nonlinear dynamics associated with the aforementioned nonholonomic system. Hence, this article contributes to formulating a self-adaptive single-neuron PID control system that is driven by an extended Kalman filter (EKF) to ensure efficient learning and faster convergence speeds. The neural adaptive PID control formulation improves the controller’s design flexibility, which allows it to effectively attenuate the tracking errors and improve the system’s trajectory tracking accuracy. To supplement the controller’s robustness to exogenous disturbances, the adaptive PID control signal is modulated with an auxiliary fuzzy-immune system. The fuzzy-immune system imitates the automatic self-learning and self-tuning characteristics of the biological immune system to suppress bounded disturbances and parametric variations. The propositions above are verified by performing the tailored hardware in the loop experiments on a differentially driven lawn-mowing robot. The results of these experiments confirm the enhanced trajectory tracking precision and disturbance compensation ability of the prescribed control method. Full article

In order to meet the demands of autonomy and control optimization in solar greenhouse control systems, this paper developed an intelligent temperature and humidity control system for greenhouses based on the Single Neuron Proportional Integral Derivative (SNPID) algorithm. The system is centered around the Huada HC32F460 Micro-Controller Unit (MCU) and the RT-Thread operating system, integrated with the SNPID control algorithm. Through comprehensive simulation, model construction, and comparative experiments, this system was thoroughly evaluated in comparison with traditional PID control systems (cPID) that rely on overseas software and hardwsbuare. Simulation results show that our new system significantly outperforms traditional PID (Proportional Integral Derivative) systems in terms of temperature control stability and accuracy. Experimental data further confirm that, while ensuring cost-effectiveness, the new system achieves a remarkable 50.2% improvement in temperature and humidity control precision compared to traditional systems. The temperature Root Mean Square Error (RMSE) in the experimental greenhouse is 0.734 compared to 1.594 in the comparison greenhouse, indicating better stable temperature control capability. The vents in the experimental greenhouse have a maximum opening of 67 cm and a minimum of 5 cm, showing a quick response property to high temperatures. In contrast, the control greenhouse has a maximum vent opening of 55 cm, remaining unchanged during the test period, which reflects its slower response to temperature fluctuations. These results demonstrate the significant advantages of the designed solar greenhouse temperature and humidity control system in terms of autonomy and control optimization, providing an efficient and economical solution for solar greenhouse environmental management. This system shows significant practical application perspective in promoting intelligent agriculture and sustainable agricultural production, highlighting its broad impact and potential significance. Full article

This paper proposes an electro-hydraulic vibration control system based on the single-neuron PID algorithm, which improves the operating frequency of the electro-hydraulic fatigue testing machine and the control accuracy of the load force. Through mathematical modeling of the electro-hydraulic vibration system (EVS), a MATLAB/Simulink simulation, and experimental testing, this study systematically analyzes the output waveform of the EVS as well as the closed-loop situation of load force amplitude and offset under the action of the single-neuron PID algorithm. The results show that: the EVS with a 2D vibration valve as the core, which can control the movement of the spool in the two-degrees-of-freedom direction, can realize the output of an approximate sinusoidal load force waveform from 0 to 800 Hz. The system controlled by the single-neuron PID algorithm is less complex to operate than the traditional PID algorithm. It also has a short rise time for the output load force amplitude curve and a maximum control error of only 1.2%. Furthermore, it exhibits a rapid closed-loop response to the load force offset. The range variability of the load force is measured to be 1.43%. A new scheme for the design of EVS is provided in this study, which broadens the application range of electro-hydraulic fatigue testing machines. Full article

Activation of mammalian target of rapamycin (mTOR) has been known as one of the contributing factors in nociceptive sensitization after peripheral injury. Its activation followed by the phosphorylation of downstream effectors causes hyperexcitability of primary sensory neurons in the dorsal root ganglion. We investigated whether a single injection of rAAV-shmTOR would effectively downregulate both complexes of mTOR in the long-term and glial activation as well. Male SD rats were categorized into shmTOR (n = 29), shCON (n = 23), SNI (n = 13), and Normal (n = 8) groups. Treatment groups were injected with rAAV-shmTOR or rAAV-shCON, respectively. DRG tissues and sciatic nerve were harvested for Western blot and immunohistochemical analyses. Peripheral sensitization was gradually attenuated in the shmTOR group, and it reached a peak on PID 21. Western blot analysis showed that both p-mTORC1 and p-mTORC2 were downregulated in the DRG compared to shCON and SNI groups. We also found decreased expression of phosphorylated p38 and microglial activation in the DRG. We first attempted a therapeutic strategy for neuropathic pain with a low dose of AAV injection by interfering with the mTOR signaling pathway, suggesting its potential application in pain treatment. Full article

The oxygen regulator is the core component of the aircraft life support system, which adjusts the flow and pressure of the breathing gas according to the pilot’s breathing needs. In response to the problem that structural parameters are difficult to adjust and prone to jitter when the indirect oxygen regulator system is stable, a direct oxygen regulator is designed using a stepper motor to drive a lung-type flapper, replacing the diaphragm lever-type structure of the indirect oxygen regulator. Due to the nonlinearity and time-varying nature of the dynamic characteristics of oxygen regulators, a single-neuron PID control strategy based on online identification of RBF neural networks is proposed to improve the PID control performance. The RBF neural network is used to identify the Jacobian information of the controlled object, and the single-neuron PID controller completes the online adjustment of the controller parameters to realize the intelligent control of the system. Simulation experimental studies are conducted to verify the performance of the direct oxygen regulator. The result analysis verifies the excellence of the single-neuron PID control strategy based on online recognition of the RBF neural network to improve the system performance. Full article

Accurate large-displacement magnetic levitation actuation and its stability remain difficult in non-liquid environments. A magnetic levitation actuation and motion control system with active levitation mode is proposed in this paper. The actuating force of the system is generated by the external magnetic field. A neural network proportion-integration-differentiation (PID) controller is designed for active actuation, and a force imbalance principle is built for the step motion mode. Dual electromagnetic actuators are configured to generate a superimposed magnetic field, ensuring that the electromagnetic force on the ball is more uniform and stable than single actuators. Dual-hall-structure sensors are used to measure displacement, thereby reducing overshoot and ensuring stability whilst motivating the ball. Due to the high adaptability of the neural network to complex systems with nonlinear and ambiguous models, the PID controller composed of neurons has stronger adaptability through tuning the PID controller parameters automatically. Furthermore, the proposed controller can solve the shortcoming that the deviation between the controlled object and the steady-state operating point increases and the tracking performance deteriorates rapidly. The strong robustness and stability in active levitation and motion control is achieved during both ascending and descending processes. Full article

A load frequency controller (LFC) is a crucial part in the distribution of a power system network (PSN) to restore its frequency response when the load demand is changed rapidly. In this paper, an artificial neural network (ANN) technique is utilised to design the optimal LFC. However, the training of the optimal ANN model for a multi-area PSN is a major challenge due to its variations in the load demand. To address this challenge, a particle swarm optimization is used to distribute the nodes of a hidden layer and to optimise the initial neurons of the ANN model, resulting in obtaining the lower mean square error of the ANN model. Hence, the mean square error and the number of epochs of the ANN model are minimised to about 9.3886 × 10⁻⁸ and 25, respectively. To assess this proposal, a MATLAB/Simulink model of the PSN is developed for the single-area PSN and multi-area PSN. The results show that the LFC based on the optimal ANN is more effective for adjusting the frequency level and improves the power delivery of the multi-area PSN comparison with the single-area PSN. Moreover, it is the most reliable for avoiding the fault condition whilst achieving the lowest time multiplied absolute error about 3.45 s when compared with the conventional ANN and PID methods. Full article

Agricultural unmanned aerial vehicles (UAVs), which are a new type of fertilizer application technology, have been rapidly developed internationally. This study combines the agronomic characteristics of rice fertilization with weighted coefficient learning-modified single-neuron adaptive proportional–integral–differential (PID) control technology to study and design an aerial real-time variable fertilizer application control system that is suitable for rice field operations in northern China. The nitrogen deficiency at the target plot is obtained from a map based on a fertilizer prescription map, and the amount of fertilizer is calculated by a variable fertilizer application algorithm. The advantages and disadvantages of the two control algorithms are analyzed by a MATLAB simulation in an indoor test, which is integrated into the spreading system to test the effect of actual spreading. A three-factor, three-level orthogonal test of fertilizer-spreading performance is designed for an outdoor test, and the coefficient of variation of particle distribution Cv (a) as well as the relative error of fertilizer application λ (b) are the evaluation indices. The spreading performance of the spreading system is the best and can effectively achieve accurate variable fertilizer application when the baffle opening is 4%, spreading disc speed is 600 r/min, and flight height is 2 m, with a and b of evaluation indexes of 11.98% and 7.02%, respectively. The control error of the spreading volume is 7.30%, and the monitoring error of the speed measurement module is less than 30 r/min. The results show that the centrifugal variable fertilizer spreader improves the uniformity of fertilizer spreading and the accuracy of fertilizer application, which enhances the spreading performance of the centrifugal variable fertilizer spreader. Full article

The varied cognitive abilities and rich adaptive behaviors enabled by the animal nervous system are often described in terms of information processing. This framing raises the issue of how biological neural circuits actually process information, and some of the most fundamental outstanding questions in neuroscience center on understanding the mechanisms of neural information processing. Classical information theory has long been understood to be a natural framework within which information processing can be understood, and recent advances in the field of multivariate information theory offer new insights into the structure of computation in complex systems. In this review, we provide an introduction to the conceptual and practical issues associated with using multivariate information theory to analyze information processing in neural circuits, as well as discussing recent empirical work in this vein. Specifically, we provide an accessible introduction to the partial information decomposition (PID) framework. PID reveals redundant, unique, and synergistic modes by which neurons integrate information from multiple sources. We focus particularly on the synergistic mode, which quantifies the “higher-order” information carried in the patterns of multiple inputs and is not reducible to input from any single source. Recent work in a variety of model systems has revealed that synergistic dynamics are ubiquitous in neural circuitry and show reliable structure–function relationships, emerging disproportionately in neuronal rich clubs, downstream of recurrent connectivity, and in the convergence of correlated activity. We draw on the existing literature on higher-order information dynamics in neuronal networks to illustrate the insights that have been gained by taking an information decomposition perspective on neural activity. Finally, we briefly discuss future promising directions for information decomposition approaches to neuroscience, such as work on behaving animals, multi-target generalizations of PID, and time-resolved local analyses. Full article

Because of its simple structure, high efficiency, low noise, and high reliability, the brushless direct current motor (BLDCM) has an irreplaceable role compared with other types of motors in many aspects. The traditional proportional integral derivative (PID) control algorithm has been widely used in practical engineering because of its simple structure and convenient adjustment, but it has many shortcomings in control accuracy and other aspects. Therefore, in this paper, a fuzzy single neuron neural network (FSNNN) PID algorithm based on an automatic speed regulator (ASR) is designed and applied to a BLDCM control system. This paper introduces a BLDCM mathematical model and its control system and designs an FSNNN PID algorithm that takes speed deviation e at different sampling times as inputs of a neural network to adjust the PID parameters, and then it uses a fuzzy system to adjust gain K of the neural network. In addition, the frequency domain stability of a double closed loop PID control system is analyzed, and the control effect of traditional PID, fuzzy PID, and FSNNN PID algorithms are compared by setting different reference speeds, as well as the change rules of three-phase current, back electromotive force (EMF), electromagnetic torque, and rotor angle position. Finally, results show that a motor controlled by the FSNNN PID algorithm has certain superiority compared with traditional PID and fuzzy PID algorithms and also has better control effects. Full article

Rotary burying by tractor-hitched rotary tillers is a common practice in southern China for treating rice stubbles. Currently, it is difficult to maintain stable tillage depths due to surface unevenness and the residual stubbles in the field, which leads to unstable tillage quality and nonuniform crop growth in later stages. In this study, an RTK-GNSS was used to measure the real-time height and roll angle of the tractor, and a variable-gain single-neuron PID control algorithm was designed to adjust the coefficients (K_P, K_I, and K_D) and gain K in real-time according to the control effects. An on-board computer sent the angles of the upper swing arm u(t) to an STM32 microcontroller through a CAN bus. Compared with the current angle of the upper swing arm, the microcontroller controlled an electronic-control proportional hydraulic system, so that the height of the rotary tiller could be adjusted to follow the field undulations in real-time. Field experiments showed that when the operation speed of the tractor-rotary tiller system was about 0.61 m/s, the variable-gain single-neuron PID algorithm could effectively improve the stability of the working depth and the stubbles’ burying rate. Compared with a conventional PID controller, the stability coefficient and the stubbles’ burying rate were improved by 5.85% and 4.38%, respectively, and compared with a single-neuron PID controller, the stability coefficient and the stubbles’ burying rate were improved by 4.37% and 3.49%, respectively. This work controlled the working depth of the rotary tiller following the changes in the field surface in real-time and improved the stubbles’ burying rate, which is suitable for the unmanned operation of the rotary burying of stubbles in the future. Full article