Nonlinear Control of Synaptic Plasticity Model for Constraining Bursting Activity in Epileptic Seizures

Clinical Neurophysiology: EEG–Video Monitoring

A global state feedback output regulating control for uncertain systems in strict feedback form

This paper presents a novel method for designing an adaptive control system using radial basis function neural network. The method is capable of dealing with nonlinear stochastic systems in strict-feedback form with any unknown dynamics. The proposed neural network allows the method not only to approximate any unknown dynamic of stochastic nonlinear systems, but also to compensate actuator nonlinearity. By employing dynamic surface control method, a common problem that intrinsically exists in the back-stepping design, called “explosion of complexity”, is resolved. The proposed method is applied to the control systems comprising various types of the actuator nonlinearities such as Prandtl–Ishlinskii (PI) hysteresis, and dead-zone nonlinearity. The performance of the proposed method is compared to two different baseline methods: a direct form of backstepping method, and an adaptation of the proposed method, named APIC-DSC, in which the neural network is not contributed in compensating the actuator nonlinearity. It is observed that the proposed method improves the failure-free tracking performance in terms of the Integrated Mean Square Error (IMSE) by 25%/11% as compared to the backstepping/APIC-DSC method. This depression in IMSE is further improved by 76%/38% and 32%/49%, when it comes with the actuator nonlinearity of PI hysteresis and dead-zone, respectively. The proposed method also demands shorter adaptation period compared with the baseline methods.

Finite-time stabilization of stochastic nonlinear systems in strict-feedback form

In this paper, by incorporating this dynamic structure control technique into a neural network based adaptive control design framework, we have developed a backstepping based control design for a class of nonlinear systems in strict-feedback form with arbitrary uncertainty. Our development is able to eliminate the problem of explosion of complexity inherent in the existing method. Taking the neural network as a model of the system, control signals are directly obtained by minimizing the cumulative differences between a setpoint and output of the model. The applicability in nonlinear system is demonstrated by simulation experiments.

This paper investigates adaptive fuzzy output feedback fault-tolerant optimal control problem for a class of single-input and single-output nonlinear systems in strict feedback form. The considered nonlinear systems contain unknown nonaffine nonlinear faults and unmeasured states. Fuzzy logic systems are used to approximate cost function and unknown nonlinear functions, respectively. It is assumed that the states of the systems to be controlled are unmeasurable, thus an adaptive state observer is developed. To solve the nonaffine nonlinear fault control design problem, filtered signals are introduced into the adaptive backstepping control design procedures, and in the framework of adaptive critic technique and fault-tolerant control technique, a novel adaptive fuzzy fault-tolerant optimal control scheme is developed. The stability of the closed-loop system is proved by using Lyapunov stability theory. The simulation results verify the effectiveness of the proposed control strategy.

In this paper, the problem of adaptive fuzzy decentralized optimal control is investigated for a class of nonlinear large-scale systems in strict-feedback form. The considered nonlinear large-scale systems contain the unknown nonlinear functions and unmeasured states. By utilizing the fuzzy logic systems to approximate the unknown nonlinear functions and cost functions, a fuzzy state observer is established to estimate the unmeasured states. The control design is divided into two phases. First, by using the state observer and the backstepping design technique, a feedforward decentralized controller with parameters adaptive laws is designed, by which the original controlled strict-feedback nonlinear large-scale system is transformed into an equivalent affine nonlinear large-scale system. Second, by using adaptive dynamic programming theory, a feedback decentralized optimal controller is developed for the equivalent affine nonlinear system. The whole adaptive fuzzy decentralized optimal control scheme consists of a feedforward decentralized controller and a feedback decentralized optimal controller. It is shown that the proposed adaptive fuzzy decentralized optimal control approach can guarantee that all the signals in the closed-loop system are bounded, and the tracking errors converge to a small neighborhood of zero. In addition, the proposed control approach can guarantee that the cost functions are minimized. Simulation results are given to demonstrate the effectiveness of the proposed control approach.

Objective To assess the value of amplitude-integrated electroencephalogram(aEEG) in diagnose of epileptic seizure and status epilepticus in pediatric intensive care unit(PICU). Methods Thirty-five children admitted to Children's Hospital of Fudan University from January to December 2014 with severe neurologia situation in PICU were investigated, and bedside video-EEG(VEEG) findings were recorded for more than 4 hours.VEEG signals were transformed into aEEG signals by Galileo NT PMS software: one-channel aEEG (C3-C4), mutichannel aEEG(F3-F4; C3-C4; P3-P4; T3-T4) plus original EEG.Electrical seizure activity on VEEG was signed out with respect to its occurrence, duration and localization of seizure onset; while aEEG seizure was recorded only during its occurrence.The relationship between aEEG and VEEG was analyzed by Spearman analysis. Results A total of 61 traces in 35 cases were suitable for analysis.(1) gender: 24 male and 11 female; years of age: 12 cases of 2 months-1 year old, 13 cases >1-6 years old, 10 cases >6-12 years old; etiological factors: 15 cases associated with severe viral encephalitis, 6 cases associated with epilepsy, 6 cases associated with intracranial hemorrhage, 5 cases associated with hypoxic-ischemic encephalopathy(HIE), 3 cases with other factors.(2)The results were divided into 2 groups: status epilepticus (SE) group and epileptic seizures group.There were 37 traces in SE group, 19 traces of them were convulsive SE, and severe viral encephalitis and epilepsy were the most common causes.The other 18 traces were non-convulsive SE, the most common causes were severe viral encephalitis, HIE and intracranial hemorrhage.There were 24 traces in non-SE group, and 118 frequencies of epileptic seizures were monitored.(3)The diagnostic value of aEEG for SE and epileptic seizures: 37 traces with status epilepticus on VEEG were all diagnosed as SE on aEEG.For non-SE electrical seizures, the sensitivity of aEEG for detection of electrical seizures was as follows: 79.66%(94/118 traces)for one-channel aEEG, 91.53%(108/118 traces) for multichannel aEEG.Findings from one-channel aEEG and multichannel aEEG were all related to VEEG(ρ=0.837, 0.901, all P<0.01). Conclusions The aEEG is a simple and effective method for monitoring epileptic seizures and SE in PICU.One-channel aEEG is the best method to find SE.It is helpful to increase the numbers of channel in order to find epileptic seizures. Key words: Child; Severe neurology situation; Amplitude-integrated electroencephalogram; Status epilepti-cus; Seizure

Cascade control for a class of uncertain nonlinear systems: a backstepping approach

In this paper, output feedback direct adaptive robust NN control is investigated for a class of nonlinear discrete-time systems in strict-feedback form. To construct output feedback control, the original strict-feedback system is transformed into a cascade form, which the output feedback of the nonlinear discrete-time system can be carried out. Then with employment of the inputs and outputs, the output feedback direct adaptive robust NN control is developed. The HONNs is exploited to approximate unknown function, and a stable adaptive NN controller is synthesized. The proposed algorithm improves the rubostness of the discrete-time nonlinear systems. It is proven that all the signals in closed-loop system are uniformly ultimately bounded (UUB). A simulation example is presented to illustrate the effectiveness of the proposed algorithm.Keywordsoutput feedbackrubostnessdirect adaptive NN controldiscrete-time nonlinear system

To investigate the network dynamics mechanisms underlying differential initiation of epileptic interictal spikes and seizures. We performed combined in vivo 2-photon calcium imaging from different targeted neuronal subpopulations and extracellular electrophysiological recordings during 4-aminopyridine-induced neocortical spikes and seizures. Both spikes and seizures were associated with intense synchronized activation of excitatory layer 2/3 pyramidal neurons (PNs) and to a lesser degree layer 4 neurons, as well as inhibitory parvalbumin-expressing interneurons (INs). In sharp contrast, layer 5 PNs and somatostatin-expressing INs were gradually and asynchronously recruited into the ictal activity during the course of seizures. Within layer 2/3, the main difference between onset of spikes and seizures lay in the relative recruitment dynamics of excitatory PNs compared to parvalbumin- and somatostatin-expressing inhibitory INs. Whereas spikes exhibited balanced recruitment of PNs and parvalbumin-expressing INs, during seizures IN responses were reduced and less synchronized than in layer 2/3 PNs. Similar imbalance was not observed in layers 4 or 5 of the neocortex. Machine learning-based algorithms we developed were able to distinguish spikes from seizures based solely on activation dynamics of layer 2/3 PNs at discharge onset. During onset of seizures, the recruitment dynamics markedly differed between neuronal subpopulations, with rapid synchronous recruitment of layer 2/3 PNs, layer 4 neurons, and parvalbumin-expressing INs and gradual asynchronous recruitment of layer 5 PNs and somatostatin-expressing INs. Seizures initiated in layer 2/3 due to a dynamic mismatch between local PNs and inhibitory INs, and only later spread to layer 5 by gradually and asynchronously recruiting PNs in this layer. ANN NEUROL 2020;87:97-115.

In this paper, switched non-linear systems in parametric strict feedback form with uncertainty, disturbance and unknown time delay are considered. At first, by using adaptive backstepping approach a common Lyapunov function (CLF) is found and a robust adaptive control is designed to stabilize the system against disturbance and unknown time delays. In continuance a modified adaptive backstepping approach is introduced, which makes it easier to find a CLF. At the end, simulation results are provided to show the effectiveness of the proposed approaches.

This work is concerned with the design of feedback control laws that guarantee satisfaction of hard output constraints. A recursive procedure is used to derive sufficient conditions for robust constraint satisfaction in input affine nonlinear systems with bounded disturbances or parametric uncertainties. The control objective is achieved by a suitable switching between two control modes, one responsible for stabilization and one for constraint satisfaction.

Online lifelong optimal tracking control of uncertain nonlinear continuous-time strict-feedback systems using deep neural networks.

This paper considers the optimization problem for dynamic multiple surface control of nonlinear systems in strict feedback form with additive uncertainties. Back-stepping combined with multiple surfaces sliding mode control is the control design method. Integral filters are used to estimate the derivative of the composite reference state at each step to avoid explosion of the number of terms. The problem can be described as H/sub /spl infin// sub-optimization in s- space where s is the coordinates determined by sliding functions s = (s/sub 1/, ..., s/sub n/). Here the sub-optimization is treated as a whole with respect to s instead of each state respectively. These sliding gains can be determined by solving a set of triangularly coupled algebraic inequalities, which is easy to implement. This is a partial optimization in the sense that optimization is not with respect to integral filter gains. A pertinent third order example is given.