Neural Control Research Articles

An Implementation of Fuzzy to Minimize Electrical Losses in Induction Machine Vector Control

An induction motor is a motor that is difficult to control so it requires synchronous speed due to dynamic load changes which are controlled by the torque current while the field current is kept constant. Thus, in this paper, we implement an adaptive PID controller method with an induction motor using MATLAB which coordinates the speed of two decoupled loads with a three-phase induction motor, where an adaptive Self Tuning Regulator (STR) PID controller is designed and realized to regulate the speed of a three-phase induction motor. using MATLAB software and AVR Atmega16 microcontroller as input and output from the plant with communication using serial RS232. The results show that after coordinating the system with each motor which is given a neural controller and FOC, the motor speed results are obtained which gradually provide synchronous speed so it can be concluded that this system can work well to slow down the plant response. Keywords: Induction Motor, Motor Speed, neural method, PID controller

Stride width and postural stability in frontal gait disorders and Parkinson's disease.

Older adults, as well as those with certain neurological disorders, may compensate for poor neural control of postural stability by widening their base of foot support while walking. However, the extent to which this wide-based gait improves postural stability or affects postural control strategies has not been explored. People with idiopathic Parkinson's disease (iPD, n = 72), frontal gait disorders (FGD, n = 16), and healthy older adults (n = 32) performed walking trials at their preferred speed over an 8-m-long, instrumented walkway. People with iPD were tested in their OFF medication state. Analyses of covariance were performed to determine the associations between stride width and measures of lateral stability control. People with FGD exhibited a wide-based gait compared to both healthy older adults and iPD. An increased stride width was associated with an increase in lateral margin of stability in FGD. Unlike healthy older adults or iPD, people with FGD did not externally rotate their feet (toe-out angle) or shift their center of pressure laterally to aid lateral dynamic stability during walking but slowed their gait instead to increase stability. By adopting a slow, wide-based gait, people with FGD take advantage of the passive, pendular mechanics of walking.

Neural Control of Naturalistic Behavior Choices

In the natural world, animals make decisions on an ongoing basis, continuously selecting which action to undertake next. In the lab, however, the neural bases of decision processes have mostly been studied using artificial trial structures. New experimental tools based on the genetic toolkit of model organisms now make it experimentally feasible to monitor and manipulate neural activity in small subsets of neurons during naturalistic behaviors. We thus propose a new approach to investigating decision processes, termed reverse neuroethology. In this approach, experimenters select animal models based on experimental accessibility and then utilize cutting-edge tools such as connectomes and genetically encoded reagents to analyze the flow of information through an animal's nervous system during naturalistic choice behaviors. We describe how the reverse neuroethology strategy has been applied to understand the neural underpinnings of innate, rapid decision making, with a focus on defensive behavioral choices in the vinegar fly Drosophila melanogaster.

Neural Network Sliding Control for Three-Axis Gimbal Orientation with Camera on an Unmanned Vehicle

Neural Network Sliding Control for Three-Axis Gimbal Orientation with Camera on an Unmanned Vehicle

Muscle synergies and muscle networks in multiple frequency components in post-stroke patients

As a neural control strategy employed by the central nervous system to control movements, the extraction of muscle synergies from a broad range of electromyographic signals has been the focus. Nevertheless, recent coherence analysis demonstrates distinct roles played in low-frequency components in motor control. This suggests that muscle synergies extracted from the low-frequency components can capture movement characteristics as done in the full-band. To investigate this issue, muscle synergies were extracted from three distinct frequency components (20–450 Hz, 20–30 Hz, 30–450 Hz) during spatial reaching movements in a group of healthy and stroke subjects. Synergy similarities were computed both within frequencies and between groups, and complex network analysis was conducted based on the group-averaged synergies. Results showed that the number and structure of muscle synergies, as well as network metrics, identified from the full-band (20–450 Hz) and low-frequency (20–30 Hz) exhibited high similarity between groups. These patterns diverged from those observed in high-frequency (30–450 Hz). The stroke did not induce significant alterations in muscle synergies. However, higher network metrics were identified in the full-band for one synergy. This study demonstrated the capacity of low-frequency components to perform muscle synergy analysis. These findings can help better understand the neural drive and motor control, potentially advancing the assessment of motor function and rehabilitation strategies.

Adaptive Neural Control for a Class of Random Fractional-Order Multi-Agent Systems with Markov Jump Parameters and Full State Constraints

Based on an adaptive neural control scheme, this paper investigates the consensus problem of random Markov jump multi-agent systems with full state constraints. Each agent is described by the fractional-order random nonlinear uncertain system driven by random differential equations, where the random noise is the second-order stationary stochastic process. First, in order to deal with the unknown functions with Markov jump parameters, a radial basis function neural network (RBFNN) structure is introduced to achieve approximation. Second, for the purpose of keeping the agents’ states from violating the constraint boundary, the tan-type barrier Lyapunov function is employed. By using the stochastic stability theory and adopting the backstepping technique, a novel adaptive neural control design method is presented. Furthermore, to cope with the differential explosion problem in the design course, the extended state observer (ESO) is developed instead of neural network (NN) approximation or command filtering techniques. Finally, the exponentially noise-to-state stability in the mean square is analyzed rigorously by the Lyapunov method, which guarantees the consensus of the considered multi-agent systems and all the agents’ outputs are bounded in probability. Two simulation examples are provided to verify the effectiveness of the suggested control strategy.

Anthropomorphic Tendon-Based Hands Controlled by Agonist-Antagonist Corticospinal Neural Network.

This article presents a study on the neurobiological control of voluntary movements for anthropomorphic robotic systems. A corticospinal neural network model has been developed to control joint trajectories in multi-fingered robotic hands. The proposed neural network simulates cortical and spinal areas, as well as the connectivity between them, during the execution of voluntary movements similar to those performed by humans or monkeys. Furthermore, this neural connection allows for the interpretation of functional roles in the motor areas of the brain. The proposed neural control system is tested on the fingers of a robotic hand, which is driven by agonist-antagonist tendons and actuators designed to accurately emulate complex muscular functionality. The experimental results show that the corticospinal controller produces key properties of biological movement control, such as bell-shaped asymmetric velocity profiles and the ability to compensate for disturbances. Movements are dynamically compensated for through sensory feedback. Based on the experimental results, it is concluded that the proposed biologically inspired adaptive neural control system is robust, reliable, and adaptable to robotic platforms with diverse biomechanics and degrees of freedom. The corticospinal network successfully integrates biological concepts with engineering control theory for the generation of functional movement. This research significantly contributes to improving our understanding of neuromotor control in both animals and humans, thus paving the way towards a new frontier in the field of neurobiological control of anthropomorphic robotic systems.

Application of Fuzzy Control and Neural Network Control in the Commercial Development of Sustainable Energy System

Sustainable energy systems (SESs) occupy a prominent position in the modern global energy landscape. The purpose of this study is to explore the application of fuzzy control and neural network control in photovoltaic systems to improve the power generation efficiency and stability of the system. By establishing the mathematical model of a photovoltaic system, the nonlinear and uncertain characteristics of photovoltaic system are considered. Fuzzy control and neural network control are used to control the system, and their performance is verified by experiments. The experimental results show that under the conditions of low light and moderate temperature, the fuzzy neural network control achieves a 3.33% improvement in power generation efficiency compared with the single control strategy. Meanwhile, the system can still maintain relatively stable operation under different environmental conditions under this comprehensive control. This shows that fuzzy neural network control has significant advantages in improving power generation efficiency and provides beneficial technical support and guidance for the commercial development of SESs.

Fuzzy wavelet neural adaptive finite-time self-triggered fault-tolerant control for a quadrotor unmanned aerial vehicle with scheduled performance

This paper focuses on fuzzy wavelet neural networks-based adaptive finite-time self-triggered fault-tolerant control design with assured tracking performance for a quadrotor unmanned aerial vehicle subject to unknown actuator faults. First, an improved finite-time performance function is integrated with the command-filtered backstepping control framework to assure the scheduled tracking performance, which can effectively constrain the transient fluctuations of the tracking error at fault occurrence. Then, two compensation mechanisms are developed to weaken the adverse impact induced by actuator faults and filter errors. Further, a fuzzy wavelet neural adaptive self-triggered fault-tolerant controller with guaranteed performance is designed to improve the fault insensitivity and tracking accuracy of the controlled vehicle, where the fuzzy wavelet neural networks are employed to approximate the unknown nonlinearities and the control signals are allowed to achieve irregular updating only when the pre-specified trigger protocol is violated. Stability analysis proves that the designed controller guarantees the attitude and position subsystems are practical finite-time stable, and the tracking errors are strictly confined to a preassigned region and never cross its allowed bounds. Finally, the validity of the developed control scheme is confirmed by the simulation results.

Adaptive disturbance rejection neural output feedback control of hydraulic manipulator systems

This paper proposes an adaptive disturbance rejection neural output feedback control (ADRNC) scheme for multi-degree-of-freedom (n-DOF) hydraulic manipulator systems, subjected to unknown nonlinearities, external disturbances and unmeasured system states. The controller design is formulated by integrating Radial Basis Function Neural Networks (RBFNNs) with state and disturbance observers using the backstepping method. The RBFNNs are synthesized to handle unknown nonlinear functions and the residual estimate error, coupled with external disturbances, is estimated through the combination of state observer and disturbance observer. The unique features of the proposed controller lies in its capability to estimate both matched and unmatched lumped disturbances. The auxiliary disturbance estimation law is guided by the neural learning weights and estimated system states provided by state observers. By effectively utilizing neural networks to approximate and mitigate most nonlinear uncertainties, the workload of the disturbance observer is substantially reduced. High-gain feedback is therefore avoided and improved tracking performance can be expected. Moreover, to avoid the tedious analysis and the problem of “explosion of complexity” in the conventional backstepping method, we employ a first-order sliding-mode differentiator. Rigorous analysis via Lyapunov methods establishes the stability of the entire closed-loop system, ensuring guaranteed and satisfactory tracking performance under the integrated influence of unknown nonlinearities, unmeasured states, and external disturbances. Extensive simulations are conducted to verify the effectiveness of the nested control strategy.

Effects of passive ankle exoskeletons on neuromuscular function during exaggerated standing sway

Effects of passive ankle exoskeletons on neuromuscular function during exaggerated standing sway

Intelligent control of the power generation system using DSPACE

AbstractWind power systems (WPs) are complex non‐linear systems with varying parameters affected by environmental changes, including wind speed fluctuations. Extracting maximum power from WPs poses a significant challenge due to these factors. Direct power control (DPC) is a highly effective technique known for its simplicity and ease of implementation. However, it suffers from power ripples caused by the use of hysteresis comparators and switching tables that operate at variable frequencies. To address this issue, this paper presents the robust neural controller (NC) based on DPC, which replaces the switching tables. The Double‐Fed Induction Generator (DFIG) is the chosen generator for the studied WP system due to its advantageous features. The NC‐DPC effectively regulates the exchange of active and reactive powers between the DFIG and the system, maximizing power extraction from the WP system while reducing Total Harmonic Distortion and enhancing overall system quality. The effectiveness of the NC‐DPC is evaluated through MATLAB simulations and further supported by experimental data obtained using the Real‐Time Interface of the dSPACE‐DS1104 Controller card.

Differential neural network robust constrained controller using approximate dynamic programming

This study focuses on developing a continuous differential neural network (DNN) approximating min–max robust control by applying dynamic neural programming. The suggested controller is applied to a class of nonlinear perturbed systems providing satisfactory dynamics for a given cost function depending on both the trajectories of the perturbed system and the designed constrained control actions. The min–max formulation for dynamic programming offers reliable control for restricted modeling uncertainties and perturbations. The suggested design considers control norm restrictions in the optimization problem. DNN’s approximation of the worst (with respect to the admissible class of perturbations and uncertainties) value function of the Hamilton–Jacobi-Bellman (HJB) equation enables the estimation of the closed-loop formulation of the controller. The robust version of the HJB partial differential equation is studied to create the learning law class for the time-varying weights in the DNN. The controller employs a time-varying Lyapunov-like differential equation and the solution of the corresponding learning laws. A recurrent algorithm based on the Kiefer-Wolfowitz technique can be used by modifying the weights’ initial conditions to fulfill the specified cost function’s end requirements. A numerical example tests the robust control proposed in this study, validating the robust optimal solution based on the DNN approximation for Bellman’s value function.

Adaptive neural quantized control for full-state constrained Markov jumping nonlinear systems with incomplete transition probabilities and unknown control directions

This article devises an adaptive quantized control scheme for full-state constrained Markov jumping nonlinear systems with incomplete transition probabilities and unknown control directions. First, the barrier Lyapunov functions were utilized to achieve full-state constraints on the investigated system, while the Nussbaum function has been adopted to overcome the issue of unknown control directions. Then, by means of command filtered backstepping control technique, an adaptive quantized controller is developed, where an improved error compensation mechanism was established to eliminate the effect of filter error, and a hysteresis quantizer was employed to diminish the transmission rate. Furthermore, it is demonstrated that the designed controller assures that all signals of the closed-loop system are bounded in the mean square sense. Finally, two illustrative examples were provided to validate the effectiveness of the proposed control method.

A body-brain circuit that regulates body inflammatory responses.

The body-brain axis is emerging as a principal conductor of organismal physiology. It senses and controls organ function1,2, metabolism3 and nutritional state4-6. Here, we show that a peripheral immune insult powerfully activates the body-brain axis to regulate immune responses. We demonstrate that pro- and anti-inflammatory cytokines communicate with distinct populations of vagal neurons to inform the brain of an emerging inflammatory response. In turn, the brain tightly modulates the course of the peripheral immune response. Genetic silencing of this body-to-brain circuit produced unregulated and out-of-control inflammatory responses. By contrast, activating, rather than silencing, this circuit affords exceptional neural control of immune responses. We used single-cell RNA sequencing, combined with functional imaging, to identify the circuit components of this neuro-immune axis, and showed that its selective manipulation can effectively suppress the pro-inflammatory response while enhancing an anti-inflammatory state. The brain-evoked transformation of the course of an immune response offers new possibilities in the modulation of a wide range of immune disorders, from autoimmune diseases to cytokine storm and shock.

Adaptive RBF Neural Network Tracking Control of Stochastic Nonlinear Systems with Actuators and State Constraints

This paper investigates the adaptive neural network (NN) tracking control problem for stochastic nonlinear systems with multiple actuator constraints and full-state constraints. The issue of system full-state constraints is tackled by a generalized barrier Lyapunov function (GBLF), and the output constraints of the system are considered to be in the form of time-varying functions, which are more in line with the needs of real physical systems. The NN approximation technique is utilized to overcome the influence of the uncertainty term on controller design due to randomness. Based on the backstepping technique, a neural adaptive fixed-time tracking control strategy is designed. Under the designed control strategy, the tracking accuracy of the controlled system can reach the expectation in a fixed time. The multi-actuator constraints are converted into a generalized mathematical model to simplify the controller design process. Using the characteristics of the hyperbolic tangent function, a new function called practical virtual control signal is designed using the virtual control signal as the input. Due to the saturation constraint property of the hyperbolic tangent function, it is theoretically ensured that no state of the system exceeds the constraints through to the new form of the virtual controller. Using the adaptive controller constructed in this paper, the controlled system is semi-global fixed-time stabilized in probability (SGFSP). Finally, the effectiveness of the proposed control strategy is further verified by simulation examples.

Fixed‐time adaptive neural network tracking control for output‐constrained high‐order systems using command filtered strategy

SummaryThis article proposes a fixed‐time adaptive neural command filtered controller for a category of high‐order systems based on adding a power integrator technique. Different from existing research, the presented controller has the following distinguishing advantages: (i) a fixed‐time control framework is extended to the tracking control problem of high‐order systems. (ii) The error compensation mechanism eliminates filter errors that arise from dynamic controllers. (iii) Growth assumptions about unknown functions are relaxed with the help of adaptive neural networks. (iv) More general systems: the developed controller can apply to high‐order systems subject to uncertain dynamics, unknown gain functions and asymmetric constraints. Stability analysis shows that all states are semi‐globally uniformly ultimately bounded, and the convergence rate of tracking error is independent of initial conditions. Finally, simulation results validate the advantages and efficacy of the developed control scheme.

Electrophysiology and 3D-imaging reveal properties of human intracardiac neurons and increased excitability with atrial fibrillation.

Altered autonomic input to the heart plays a major role in atrial fibrillation (AF). Autonomic neurons termed ganglionated plexi (GP) are clustered on the heart surface to provide the last point of neural control of cardiac function. To date the properties of GP neurons in humans are unknown. Here we have addressed this knowledge gap in human GP neuron structure and physiology in patients with and without AF. Human right atrial GP neurons embedded in epicardial adipose tissue were excised during open heart surgery performed on both non-AF and AF patients and then characterised physiologically by whole cell patch clamp techniques. Structural analysis was also performed after fixation at both the single cell and at the entire GP levels via three-dimensional confocal imaging. Human GP neurons were found to exhibit unique properties and structural complexity with branched neurite outgrowth. Significant differences in excitability were revealed between AF and non-AF GP neurons as measured by lower current to induce action potential firing, a reduced occurrence of low action potential firing rates, decreased accommodation and increased synaptic density. Visualisation of entire GPs showed almost all neurons are cholinergic with a small proportion of noradrenergic and dual phenotype neurons. Phenotypic distribution differences occurred with AF including decreased cholinergic and dual phenotype neurons, and increased noradrenergic neurons. These data show both functional and structural differences occur between GP neurons from patients with and without AF, highlighting that cellular plasticity occurs in neural input to the heart that could alter autonomic influence on atrial function. KEY POINTS: The autonomic nervous system plays a critical role in regulating heart rhythm and the initiation of AF; however, the structural and functional properties of human autonomic neurons in the autonomic ganglionated plexi (GP) remain unknown. Here we perform the first whole cell patch clamp electrophysiological and large tissue confocal imaging analysis of these neurons from patients with and without AF. Our data show human GP neurons are functionally and structurally complex. Measurements of action potential kinetics show higher excitability in GP neurons from AF patients as measured by lower current to induce action potential firing, reduced low firing action potential rates, and decreased action potential accommodation. Confocal imaging shows increased synaptic density and noradrenergic phenotypes in patients with AF. Both functional and structural differences occur in GP neurons from patients with AF that could alter autonomic influence on atrial rhythm.

A comprehensive dataset on biomechanics and motor control during human walking with discrete mechanical perturbations.

Humans have a remarkable capability to maintain balance while walking. There is, however, a lack of publicly available research data on reactive responses to destabilizing perturbations during gait. Here, we share a comprehensive dataset collected from 10 participants who experienced random perturbations while walking on an instrumented treadmill. Each participant performed six 5-min walking trials at a rate of 1.2 m/s, during which rapid belt speed perturbations could occur during the participant's stance phase. Each gait cycle had a 17% probability of being perturbed. The perturbations consisted of an increase of belt speed by 0.75 m/s, delivered with equal probability at 10%, 20%, 30%, 40%, 50%, 60%, 70%, or 80% of the stance phase. Data were recorded using motion capture with 25 markers, eight inertial measurement units (IMUs), and electromyography (EMG) from the tibialis anterior (TA), soleus (SOL), lateral gastrocnemius (LG), rectus femoris (RF), vastus lateralis (VL), vastus medialis (VM), biceps femoris (BF), and gluteus maximus (GM). The full protocol is described in detail. We provide marker trajectories, force plate data, EMG data, and belt speed information for all trials and participants. IMU data is provided for most participants. This data can be useful for identifying neural feedback control in human gait, biologically inspired control systems for robots, and the development of clinical applications.

Event-Triggered Adaptive Neural Network Control for State-Constrained Pure-Feedback Fractional-Order Nonlinear Systems with Input Delay and Saturation

In this research, the adaptive event-triggered neural network controller design problem is investigated for a class of state-constrained pure-feedback fractional-order nonlinear systems (FONSs) with external disturbances, unknown actuator saturation, and input delay. An auxiliary compensation function based on the integral function of the input signal is presented to handle input delay. The barrier Lyapunov function (BLF) is utilized to deal with state constraints, and the event-triggered strategy is applied to overcome the communication burden from the limited communication resources. By the utilization of a backstepping scheme and radial basis function neural network, an adaptive event-triggered neural state-feedback stabilization controller is constructed, in which the fractional-order dynamic surface filters are employed to reduce the computational burden from the recursive procedure. It is proven that with the fractional-order Lyapunov analysis, all the solutions of the closed-loop system are bounded, and the tracking error can converge to a small interval around the zero, while the state constraint is satisfied and the Zeno behavior can be strictly ruled out. Two examples are finally given to show the effectiveness of the proposed control strategy.