Performance of the Boost Chopper, Comparative Study between PI Control and Neural Control to Regulate Its Output Voltage

In this study, we investigate the performance of a boost converter regulating its output voltage using two control methods: Proportional-Integral (PI) control and neural control. Both methods are implemented on a simulation platform (Matlab/Simulink) and evaluated in terms of accuracy, response speed, and robustness to disturbances. Indeed, the output voltage of converters exhibits imperfections that require a control method to optimize efficiency when applying a variable load. Results show that neural control offers superior performance in terms of accuracy and response time, with faster and more precise regulation of the output voltage. On the other hand, PI control proves to be more robust against disturbances. These findings can help guide the selection of the appropriate control method for a boost converter based on the specific requirements of each application.

Time-Varying Asymmetrical BLFs Based Adaptive Finite-Time Neural Control of Nonlinear Systems With Full State Constraints

This paper concentrates on asymmetric barrier Lyapunov functions(ABLFs)based on finite-time adaptive neural network(NN)control methods for a class of nonlinear strict feedback systems with time-varying full state constraints.During the process of backstepping recursion,the approximation properties of NNs are exploited to address the problem of unknown internal dynamics.The ABLFs are constructed to make sure that the time-varying asymmetrical full state constraints are always satisfied.According to the Lyapunov stability and finitetime stability theory,it is proven that all the signals in the closedloop systems are uniformly ultimately bounded(UUB)and the system output is driven to track the desired signal as quickly as possible near the origin.In the meantime,in the scope of finitetime,all states are guaranteed to stay in the pre-given range.Finally,a simulation example is proposed to verify the feasibility of the developed finite time control algorithm.

Typical adaptive neural control for hypersonic vehicle based on higher-order filters

A typical adaptive neural control methodology is used for the rigid body model of the hypersonic vehicle. The rigid body model is divided into the altitude subsystem and the velocity subsystem. The proportional integral differential(PID) controller is introduced to control the velocity track. The backstepping design is applied for constructing the controllers for the altitude subsystem.To avoid the explosion of differentiation from backstepping, the higher-order filter dynamic is used for replacing the virtual controller in the backstepping design steps. In the design procedure,the radial basis function(RBF) neural network is investigated to approximate the unknown nonlinear functions in the system dynamic of the hypersonic vehicle. The simulations show the effectiveness of the design method.

An Adaptive Single Neural Control for Variable Step-Size P&O MPPT of Marine Current Turbine System

Marine current energy has been increasingly used because of its predictable higher power potential.Owing to the external disturbances of various flow velocity and the high nonlinear effects on the marine current turbine(MCT)system,the nonlinear controllers which rely on precise mathematical models show poor performance under a high level of parameters’uncertainties.This paper proposes an adaptive single neural control(ASNC)strategy for variable step-size perturb and observe(P&O)maximum power point tracking(MPPT)control.Firstly,to automatically update the neuron weights of SNC for the nonlinear systems,an adaptive mechanism is proposed to adaptively adjust the weighting and learning coefficients.Secondly,aiming to generate the exact reference speed for ASNC to extract the maximum power,a variable step-size law based on speed increment is designed to strike a balance between tracking speed and accuracy of P&O MPPT.The robust stability of the MCT control system is guaranteed by the Lyapunov theorem.Comparative simulation results show that this strategy has favorable adaptive performance under variable velocity conditions,and the MCT system operates at maximum power point steadily.

Adaptive Neural Control for a Class of Low-triangular-structured Nonlinear Systems with H-∞ Performance Analysis

In this paper,a neural-network-based variable structure control scheme is presented for a class of nonlinear systems with a general low triangular structure.The proposed variable structure controller is proved to be C1,thus can be applied for backstepping design,which has extended the scope of previous nonlinear systems in the form of strict-feedback and pure-feedback.With the help of neural network approximator,H-∞ performance analysis of stability is given.The effectiveness of proposed control law is verified via simulation.

Stable Adaptive Neural Control of a Robot Arm

In this paper, stable indirect adaptive control with recurrent neural networks (RNN) is presented for square multivariable non-linear plants with unknown dynamics. The control scheme is made of an adaptive instantaneous neural model, a neural controller based on fully connected “Real-Time Recurrent Learning” (RTRL) networks and an online parameters updating law. Closed-loop performances as well as sufficient conditions for asymptotic stability are derived from the Lyapunov approach according to the adaptive updating rate parameter. Robustness is also considered in terms of sensor noise and model uncertainties. This control scheme is applied to the manipulator robot process in order to illustrate the efficiency of the proposed method for real-world control problems.

Adaptive neural control for a class of uncertain stochastic nonlinear systems with dead-zone

The problem of adaptive stabilization is addressed for a class of uncertain stochastic nonlinear strict-feedback systems with both unknown dead-zone and unknown gain functions.By using the backstepping method and neural network（NN） parameterization,a novel adaptive neural control scheme which contains fewer learning parameters is developed to solve the stabilization problem of such systems.Meanwhile,stability analysis is presented to guarantee that all the error variables are semi-globally uniformly ultimately bounded with desired probability in a compact set.The effectiveness of the proposed design is illustrated by simulation results.

Neural control of the soleus H-reflex correlates tothe laterality pattern in limbs

BACKGROUND： Previous studies have demonstrated the relationship of lower limb dominance with left- and right-handedness, supporting findings suggest that there is a role for peripheral factors in the neural control of movement. OBJECTIVE： To investigate the effect of laterality pattern on the neural mechanisms of motor control at the peripheral level. DESIGN, TIME AND SETTING： A controlled observation experiment was performed at the Motor Diagnostics Laboratory of the Academy of Physical Education in Katowice, Poland, in June 2009. PARTICIPANTS： Twenty young male adults aged 21-23 years and presenting two laterality patterns in hand-foot combination （right handed-right footed and left handed-left footed groups） took part in the experiment. All participants were carefully screened to eliminate any neurological or muscle disease or trauma. METHODS： The experiment included a laterality evaluation and the motor evoked potentials of dominant and non-dominant limbs. Measures were done through the use of the Hoffmann-reflex （H-reflex） circuitry. The soleus H-reflex parameters elicited at rest in lower extremities were compared. The soleus H-reflex and the direct motor response were elicited in lower extremities of each participant in the same laboratory session. MAIN OUTCOME MEASURES： Onset latencies and min-max amplitudes of the direct motor response and the H-reflex; the motor and sensory conduction velocities; and symmetry coefficients of response parameters. RESULTS： The analysis of symmetry coefficients of direct and late motor responses confirmed differences between two laterality patterns in amplitude and latency of the H-reflex as well as in a sensory conduction velocity （P 〈 0.05）, but not in direct motor response parameters. The amplitude of the H-reflex and the calculated sensory la afferent conduction velocity in the dominant lower extremity were significantly depressed in the right-sided group in comparison to the left-sided group （P = 0.001）. The right-sided group presented significantly higher motor fiber conduction velocity in the dominant leg than in the non-dominant leg （P = 0.006）, with no similar effect in the left-sided group. CONCLUSION： The neural control of the H-reflex elicited at rest is related to the laterality pattern in hand-foot combination in healthy adults. This result strongly suggests the possible existence of intrinsic control mechanisms of afferent feedback related to functional dominance in human limbs.

Dissipative-based adaptive neural control for nonlinear systems

A dissipative-based adaptive neural control scheme was developed for a class of nonlinear uncertain systems with unknown nonlinearities that might not be linearly parameterized. The major advantage of the present work was to relax the requirement of matching condition, i.e., the unknown nonlinearities appear on the same equation as the control input in a state-space representation, which was required in most of the available neural network controllers. By synthesizing a state-feedback neural controller to make the closed-loop system dissipative with respect to a quadratic supply rate, the developed control scheme guarantees that the L2-gain of controlled system was less than or equal to a prescribed level. And then, it is shown that the output tracking error is uniformly ultimate bounded. The design scheme is illustrated using a numerical simulation.

Novel Adaptive Neural Controller Design Based on HVDC Transmission System to Damp Low Frequency Oscillations and Sub Synchronous Resonance

This paper presents the effect of the high voltage direct current (HVDC) transmission system based on voltage source converter (VSC) on the sub synchronous resonance (SSR) and low frequency oscillations (LFO) in power system. Also, a novel adaptive neural controller based on neural identifier is proposed for the HVDC which is capable of damping out LFO and sub synchronous oscillations (SSO). For comparison purposes, results of system based damping neural controller are compared with a lead-lag controller based on quantum particle swarm optimization (QPSO). It is shown that implementing adaptive damping controller not only improves the stability of power system but also can overcome drawbacks of conventional compensators with fixed parameters. In order to determine the most effective input of HVDC system to apply supplementary controller signal, analysis based on singular value decomposition is performed. To evaluate the performance of the proposed controller, transient simulations of detailed nonlinear system are considered.

Prescribed performance neural control to guarantee tracking quality for near space kinetic kill vehicle

A prescribed performance neural controller to guarantee tracking quality is addressed for the near space kinetic kill vehicle (NSKKV) to meet the state constraints caused by side window detection. Different from the traditional prescribed performance control in which the shape of the performance function is constant, this paper exploits new performance functions which can change the shape of their function according to different symbols of initial errors and can ensure the error convergence with a small overshoot. The neural backstepping control and the minimal learning parameters (MLP) technology are employed for exploring a prescribed performance controller (PPC) that provides robust tracking attitude reference trajectories. The highlight is that the transient performance of tracking errors is satisfactory and the computational load of neural approximation is low. The pseudo rate (PSR) modulator is used to shape the continuous control command to pulse or on-off signals to meet the requirements of the thruster. Numerical simulations show that the proposed method can achieve state constraints, pseudo-linear operation and high accuracy.

Prediction and Analysis of Elevator Traffic Flow under the LSTM Neural Network

Elevators are essential components of contemporary buildings, enabling efficient vertical mobility for occupants. However, the proliferation of tall buildings has exacerbated challenges such as traffic congestion within elevator systems. Many passengers experience dissatisfaction with prolonged wait times, leading to impatience and frustration among building occupants. The widespread adoption of neural networks and deep learning technologies across various fields and industries represents a significant paradigm shift, and unlocking new avenues for innovation and advancement. These cutting-edge technologies offer unprecedented opportunities to address complex challenges and optimize processes in diverse domains. In this study, LSTM (Long Short-Term Memory) network technology is leveraged to analyze elevator traffic flow within a typical office building. By harnessing the predictive capabilities of LSTM, the research aims to contribute to advancements in elevator group control design, ultimately enhancing the functionality and efficiency of vertical transportation systems in built environments. The findings of this research have the potential to reference the development of intelligent elevator management systems, capable of dynamically adapting to fluctuating passenger demand and optimizing elevator usage in real-time. By enhancing the efficiency and functionality of vertical transportation systems, the research contributes to creating more sustainable, accessible, and user-friendly living environments for individuals across diverse demographics.

Novel adaptive neural control of flexible air-breathing hypersonic vehicles based on sliding mode differentiator

A novel adaptive neural control strategy is exploited for the longitudinal dynamics of a generic flexible air-breathing hypersonic vehicle（FAHV）.By utilizing functional decomposition method, the dynamics of FAHV is decomposed into the velocity subsystem and the altitude subsystem.For each subsystem, only one neural network is employed for the unknown function approximation.To further reduce the computational burden, minimal-learning parameter（MLP）technology is used to estimate the norm of ideal weight vectors rather than their elements.By introducing sliding mode differentiator（SMD） to estimate the newly defined variables, there is no need for the strict-feedback form and virtual controller.Hence the developed control law is considerably simpler than the ones derived from back-stepping scheme.Finally, simulation studies are made to illustrate the effectiveness of the proposed control approach in spite of the flexible effects, system uncertainties and varying disturbances.

Adaptive neural control for pure-feedback nonlinear time-delay systems with unknown dead-zone: a Lyapunov-Razumikhin method

This paper addresses the problem of adaptive neural control for a class of uncertain pure-feedback nonlinear systems with multiple unknown state time-varying delays and unknown dead-zone. Based on a novel combination of the Razumikhin functional method, the backstepping technique and the neural network parameterization, an adaptive neural control scheme is developed for such systems. All closed-loop signals are shown to be semiglobally uniformly ultimately bounded, and the tracking error remains in a small neighborhood of the origin. Finally, a simulation example is given to demonstrate the effectiveness of the proposed control schemes.

Adaptive neural control of nonlinear periodic time-varying parameterized mixed-order multi-agent systems with unknown control coefficients

In this paper, we first consider the adaptive leader-following consensus problem for a class of nonlinear parameterized mixedorder multi-agent systems with unknown control coefficients and time-varying disturbance parameters of the same period. Neural networks and Fourier series expansions are used to describe the unknown nonlinear periodic time-varying parameterized function.A distributed control protocol is designed based on adaptive control, matrix theory, and Nussbaum function. The robustness of the distributed control protocol is analyzed by combining the stability analysis theory of closed-loop systems. On this basis, this paper discusses the case of time-varying disturbance parameters with non-identical periods, expanding the application scope of this control protocol. Finally, the effectiveness of the algorithm is verified by a simulation example.

Intelligent vehicle lateral control based on radial basis function neural network sliding mode controller

Based on the predigestion of the dynamic model of the intelligent firefighting vehicle,a linear 2-DOF lateral dynamic model and a preview error model are established.To solve the problems of a highly non-linear vehicle model,time-varying parameters,output chattering,and poor robustness,the Radial Basis Function neural network sliding mode controller is designed.Then,different driving speeds are used to conduct simulation tests under standard double-shifting and smooth curve road conditions,and the simulation results are used to analyse the tracking effect of the lateral motion controller on the desired path.The simulation results reveal that the controller designed has high accuracy in tracking the desired path and has good robustness to the disturbance of intelligent firefighting vehicle speed changes.

Event-Triggered Adaptive Neural Control for Multiagent Systems with Deferred State Constraints

This paper focuses on the leader-following consensus control problem for nonlinear multiagent systems subject to deferred asymmetric time-varying state constraints.A distributed eventtriggered adaptive neural control approach is advanced.By virtue of a distributed sliding-mode estimator,the leader-following consensus control problem is converted into multiple simplified tracking control problems.Afterwards,a shifting function is utilized to transform the error variables such that the initial tracking condition can be totally unknown and the state constraints can be imposed at a specified time instant.Meanwhile,the deferred asymmetric time-varying full state constraints are addressed by a class of asymmetric barrier Lyapunov function.In order to reduce the burden of communication,a relative threshold event-triggered mechanism is incorporated into controller and Zeno behavior is excluded.Based on Lyapunov stability theorem,all closed-loop signals are proved to be semi-globally uniformly ultimately bounded.Finally,a practical simulation example is given to verify the presented control scheme.

Application of Improved Neural Adaptive PSD Algorithm in Temperature Control of INS

A neural adaptive proportion sum differential (PSD) algorithm with errors prediction is researched. It is applied in inertial navigation system(INS) temperature control. The algorithm do not need the process's precise mathematical model and can adapt to the process parameters changing, and can deal with the process with nonlinearity. According to the Smith predictor, author developed a method that takes the predicted process error and error change as neural adaptive PSD algorithm's input. The method is effective to the system with long dead time. The results of compute simulation show that this system has characters of quickly reaction, low overshoot and good stability. It can meet the requirements of temperature control of INS.

Design,analysis,and neural control of a bionic parallel mechanism

Although the torso plays an important role in the movement coordination and versatile locomotion of mammals,the structural design and neuromechanical control of a bionic torso have not been fully addressed.In this paper,a parallel mechanism is designed as a bionic torso to improve the agility,coordination,and diversity of robot locomotion.The mechanism consists of 6-degree of freedom actuated parallel joints and can perfectly simulate the bending and stretching of an animal’s torso during walking and running.The overall spatial motion performance of the parallel mechanism is improved by optimizing the structural parameters.Based on this structure,the rhythmic motion of the parallel mechanism is obtained by supporting state analysis.The neural control of the parallel mechanism is realized by constructing a neuromechanical network,which merges the rhythmic signals of the legs and generates the locomotion of the bionic parallel mechanism for different motion patterns.Experimental results show that the complete integrated system can be controlled in real time to achieve proper limb-torso coordination.This coordination enables several different motions with effectiveness and good performance.

Neural control of pressure support ventilation improved patient-ventilator synchrony in patients with different respiratory system mechanical properties:a prospective,crossover trial

Background:Conventional pressure support ventilation(PSP)is triggered and cycled off by pneumatic signals such as flow.Patient-ventilator asynchrony is common during pressure support ventilation,thereby contributing to an increased inspiratory effort.Using diaphragm electrical activity,neurally controlled pressure support(PSN)could hypothetically eliminate the asynchrony and reduce inspiratory effort.The purpose of this study was to compare the differences between PSN and PSP in terms of patient-ventilator synchrony,inspiratory effort,and breathing pattern.Methods:Eight post-operative patients without respiratory system comorbidity,eight patients with acute respiratory distress syndrome(ARDS)and obvious restrictive acute respiratory failure(ARF),and eight patients with chronic obstructive pulmonary disease(COPD)and mixed restrictive and obstructive ARF were enrolled.Patient-ventilator interactions were analyzed with macro asynchronies(ineffective,double,and auto triggering),micro asynchronies(inspiratory trigger delay,premature,and late cycling),and the total asynchrony index(AI).Inspiratory efforts for triggering and total inspiration were analyzed.Results:Total AI of PSN was consistently lower than that of PSP in COPD(3%vs.93%,P=0.012 for 100%support level;8%vs.104%,P=0.012 for 150%support level),ARDS(8%vs.29%,P=0.012 for 100%support level;16%vs.41%,P=0.017 for 150%support level),and post-operative patients(21%vs.35%,P=0.012 for 100%support level;15%vs.50%,P=0.017 for 150%support level).Improved support levels from 100%to 150%statistically increased total AI during PSP but not during PSN in patients with COPD or ARDS.Patients’inspiratory efforts for triggering and total inspiration were significantly lower during PSN than during PSP in patients with COPD or ARDS under both support levels(P<0.05).There was no difference in breathing patterns between PSN and PSP.Conclusions:PSN improves patient-ventilator synchrony and generates a respiratory pattern similar to PSP independently of any level of support in patients with different respiratory system mechanical properties.PSN,which reduces the trigger and total patient’s inspiratory effort in patients with COPD or ARDS,might be an alternative mode for PSP.Trial Registration:ClinicalTrials.gov,NCT01979627;https://clinicaltrials.gov/ct2/show/record/NCT01979627.