Recurrent High Order Neural Network Research Articles

Neural Sliding Mode Control of a Buck-Boost Converter Applied to a Regenerative Braking System for Electric Vehicles

This paper presents the design and simulation of a neural sliding mode controller (NSMC) for a regenerative braking system in an electric vehicle (EV). The NSMC regulates the required current and voltage of the bidirectional DC-DC buck–boost converter, an element of the auxiliary energy system (AES), to improve the state of charge (SOC) of the battery of the EV. The controller is based on a recurrent high-order neural network (RHONN) trained using the extended Kalman filter (EKF) and the unscented Kalman filter (UKF) as the tools to train the neural networks to obtain a higher SOC in the battery. The performance of the controller with the two training algorithms is compared with a proportional integral (PI) controller illustrating the differences and improvements obtained with the EKF and the UKF. Furthermore, robustness tests considering Gaussian noise and varying of parameters have demonstrated the outcome of the NSMC over a PI controller. The proposed controller is a new strategy with better results than the PI controller applied to the same buck–boost converter circuit, which can be used for the main energy system (MES) efficiency in an EV architecture.

Neural Network Inverse Optimal Control of Ground Vehicles

In this paper an active controller for ground vehicles stability is presented. The objective of this controller is to force the vehicle to track a desired reference, ensuring safe driving conditions in the case of adhesion loss during hazardous maneuvers. To this aim, a nonlinear discrete-time inverse optimal control based on a neural network identification is designed, using a recurrent high order neural network (RHONN) trained by an Extended Kalman Filter. The RHONN ensures stability of the identification error, while the controller ensures the stability of the tracking errors. Moreover, a discrete-time reduced order state observer is utilized to reconstruct the lateral vehicle dynamic not usually available. For the control problem, the references of the lateral velocity and yaw rate are given by a dynamic system mimicking an ideal vehicle having not-decreasing tire lateral characteristics. The proposed approach avoids the identification of the Pacejka’s lateral parameters of the tires, so simplifying the input control determination. Moreover, an optimal control is proposed to optimize the actuator effort and power, usually bounded. Control gains are determined using optimal “nature-inspired" algorithms such as particle swarm optimization. Test maneuvers, performed through the full vehicle simulator CarSim®, have been used to test correctness, quality and performances of the observer, the neural identifier and the inverse optimal controller. Robustness of the reduced order discrete-time state observer is also discussed for different sample times. Finally, a fair comparison between optimal and non-optimal control schemes is presented, highlighting the numerical results obtained in simulation.

Online Learning-Informed Feedforward-Feedback Controller Synthesis for Path Tracking of Autonomous Vehicles

High-performance path tracking is a key technology for autonomous vehicles. Feedforward-feedback control architectures are suitable for accurate path tracking with adequate margins of stability. For system modelling in the feedforward component, the learning-based method has been proven to be a promising approach owing to its model-free framework. However, the offline-learned data model trained with collection data is confined by its feature space, resulting in insufficient generalization. As a solution, in this study, we introduce an online learning network – the recurrent high-order neural network (RHONN) – to characterize vehicle behaviors. The RHONN is used to feature vehicle behaviors in a timely manner with a high fidelity and flexible form. The equilibrium at the preview point on the desired path is found based on the online-identified RHONN model, and its induced steering angle is taken as the feedforward command. For the feedback steering controller, the preview point position-based control law incorporating the steady vehicle sideslip angle is adopted to enhance the stability performance. Finally, in the CarSim/Simulink environment, the performance of the designed RHONN-informed feedforward and feedback controller is validated in two typical scenarios – double-lane change and single-turn. The validation results reveal that the proposed approach offers better tracking accuracy in linear and nonlinear regions than other techniques. More notably, the average execution time (3.55 ms) is less than the sampling frequency of the controller (50 ms), which further confirms the applicability and efficiency of the proposed approach.

RHONN-Modeling-Based Predictive Safety Assessment and Torque Vectoring for Holistic Stabilization of Electrified Vehicles

Improving vehicle safety and reducing traffic accidents have always been of cardinal importance in vehicle dynamics control fields. A reasonable and comprehensive safety index that characterizes the vehicle's safe region is the most challenging aspect of research. With the linear dynamics model as the benchmark, this article uses the deviation of yaw rate and vehicle sideslip angle from the corresponding linear response as the lateral stability indices. Meanwhile, the maximum slip ratio of the driven wheels is selected as the longitudinal stability index. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">safety indicator</i> , a quantitative index featuring the safety degree of vehicle planar motions, is then inferred by a fuzzy inference system using the lateral and longitudinal stability indices. As such, the recurrent high-order neural network model predicts the vehicle states. Based on the predicted states, a safety indicator is then derived by using fuzzy inference system, which can assess the safety of a driver's control commands. In the case of an improper driving torque demand given by the driver, the torque correction process is immediately conducted to maintain the vehicle in a safe region. Finally, two typical scenarios—slippery curves and double lane changes in low friction roads—are simulated on the MATLAB/Simulink-CarSim cosimulation platform. The hardware-in-the-loop experiments are also conducted on a driving simulator test rig, validating the performance of the developed algorithms. The holistic stability performance of the in-wheel motor driven vehicle is thoroughly analyzed and compared using three existing methods. The simulation and experimental results validate the effectiveness and feasibility of the proposed method.

Retraction Note: Lung cancer prediction using higher-order recurrent neural network based on glowworm swarm optimization

Retraction Note: Lung cancer prediction using higher-order recurrent neural network based on glowworm swarm optimization

Impulsive Neural Control to Schedule Antivirals and Immunomodulators for COVID-19.

New SARS-CoV-2 variants escaping the effect of vaccines are an eminent threat. The use of antivirals to inhibit the viral replication cycle or immunomodulators to regulate host immune responses can help to tackle the viral infection at the host level. To evaluate the potential use of these therapies, we propose the application of an inverse optimal neural controller to a mathematical model that represents SARS-CoV-2 dynamics in the host. Antiviral effects and immune responses are considered as the control actions. The variability between infected hosts can be large, thus, the host infection dynamics are identified based on a Recurrent High-Order Neural Network (RHONN) trained with the Extended Kalman Filter (EKF). The performance of the control strategies is tested by employing a Monte Carlo analysis. Simulation results present different scenarios where potential antivirals and immunomodulators could reduce the viral load.

Sensor fault-tolerant control for a doubly fed induction generator in a smart grid

This study proposes a sensor fault-tolerant controller for a doubly fed induction generator connected to a smart grid. Mathematical models are the main tools for the synthesis of modern control systems; however, an accurate model for complex systems is not always available. Therefore, in this study, a recurrent high-order neural network trained with an extended Kalman filter is proposed to develop a mathematical model for a wind turbine with a doubly fed induction generator connected to a smart grid. The neural model is combined with a modified discrete-time sliding mode controller, which compensates for the presence of sensor faults on each of the state variables on both sides of the back-to-back converter. Real-time results are included to illustrate the effectiveness of the proposed scheme under five sensor faults.

Hybrid physics-data-driven online modelling: Framework, methodology and application to electric vehicles

This paper proposes a novel hybrid physics-data-driven framework for system modelling by integrating a physical model and an online learning data model to improve model accuracy, interpretability, and generalization. Taking an in-wheel Motor Driven Vehicle (IMDV) as an example, two hybrid representations, i.e. the Dynamic Linearization Data Model (DLDM) and Recurrent High-Order Neural Network (RHONN) are introduced for the planar dynamics modelling of the electric vehicle. However, it is difficult to obtain the statistical information of the operation process and measurement noise when the weight vectors of the data-driven model is updated online. To address this issue, a H∞-based learning algorithm is adopted. The stability and convergence rate are elaborated and compared with an existing Extended Kalman Filter (EKF)-based method. Finally, we compare four methods, including the physics-based, data-based and two hybrid models, to evaluate their performances of modelling the IMDV’s dynamics. The feasibility test and comparison studies are conducted in simulations and on a Hardware-in-the-Loop (HiL) test rig. The results demonstrated that the proposed H∞-based hybrid method, which does not make any assumption on measurement noise, has better generalization ability and robustness in practical implementations, compared to other baseline methods.

Semi-empirical dynamics modeling of a bicycle robot based on feature selection and RHONN

Aiming at the difference between a bicycle robot dynamics model based on prior knowledge and a real robot, the robot modeling problem is studied. A set of features is extracted from the dynamics model of a bicycle robot. The trigonometric functions in the features are replaced with a linear combination of several sigmoid functions to form a new set of features. An index describing the importance of features is designed based on the maximal information coefficient (MIC). The features are ranked using the feature importance index. The index of system identification fitness (FIT) is regarded as the likelihood function term in Bayesian information criterion (BIC). The penalty term of BIC is constructed with the number of both the features and weight parameters in the recurrent high order neural network (RHONN) model. A semi-empirical dynamics modeling method based on feature selection is proposed. An inverse optimal controller is designed to stabilize the bicycle robot. The effectiveness of the modeling method is verified through simulation and real robot experiments.

RHONN Modelling-Enabled Nonlinear Predictive Control for Lateral Dynamics Stabilization of an In-Wheel Motor Driven Vehicle

Featuring the fast response and flexibility in control allocation, an electric vehicle with in-wheel motors is a good platform for implementing advanced vehicle dynamics control. Among many active safety functions of an in-wheel motor driven vehicle (IMDV), lateral stability control is a key technology, which can be realized through torque vectoring. To further advance the lateral stabilization performance of the IMDV, in this article a novel data-driven nonlinear model predictive control (NMPC) is proposed based the recurrent high-order neural network (RHONN) modelling method. First, the new RHONN model is developed to represent vehicle’s nonlinear dynamic behaviors. Different from the conventional physics-based modelling method, the RHONN model forms high-order polynomials by neuron states to feature nonlinear dynamics. Based on the RHONN model, the steady-state responses of vehicle’s yaw rate and sideslip angle are iteratively optimized and set as the control objectives for low-level controller, aiming to improve the system robustness. Besides, a nonlinear model predictive controller is designed based on the RHONN, which is expected to improve the prediction accuracy during the receding horizon control. Further, a constrained optimization problem is formulated to derive the required yaw moment for vehicle lateral dynamics stabilization. Finally, the performance of the developed RHONN-based nonlinear MPC is validated on an IMDV in the CarSim/Simulink simulation environment. The validation results show that the developed approach outperforms the conventional method, and further improves the stable margin of the system. It is able to enhance the lateral stabilization performance of the IMDV under various driving scenarios, demonstrating the feasibility and effectiveness of the proposed approach.

Distributed Cooperative Neural Inverse Optimal Control of Microgrids for Island and Grid-Connected Operations

In this paper, a novel distributed secondary cooperative control based on neural inverse optimal control scheme is synthesized for microgrids considering island and grid-connected modes. This approach is developed for each distributed generator to track frequency and voltage predefined values for island mode, and to track active and reactive power references for grid-connected mode, and for smooth switching between these two modes. To achieve these goals, a secondary control layer is proposed to define the required currents trajectories, which then are controlled using the primary controller. Both control layers are synthesized using recurrent high order neural network on-line trained using an extended Kalman filter, which builds up neural models and is to be used to implement the respective inverse optimal controllers. In addition, a pinning technique is also used for achieving synchronization of all distributed generators only requiring neighborhood information. Thus, a large reduction in the communication network complexity is obtained and the control system reliability is improved. Simulations are performed to evaluate the effectiveness of the proposed scheme with a microgrid benchmark. The obtained results illustrate adequate performances of the proposed scheme to operate the microgrids in island mode, grid-connected mode, and to achieve seamless switching between these two modes.

Optimal Operation for Reduced Energy Consumption of an Air Conditioning System Using Neural Inverse Optimal Control

For a comfortable thermal environment, the main parameters are indoor air humidity and temperature. These parameters are strongly coupled, causing the need to search for multivariable control alternatives that allow efficient results. Therefore, in order to control both the indoor air humidity and temperature for direct expansion (DX) air conditioning (A/C) systems, different controllers have been designed. In this paper, a discrete-time neural inverse optimal control scheme for trajectories tracking and reduced energy consumption of a DX A/C system is presented. The dynamic model of the plant is approximated by a recurrent high-order neural network (RHONN) identifier. Using this model, a discrete-time neural inverse optimal controller is designed. Unscented Kalman filter (UKF) is used online for the neural network learning. Via simulation the scheme is tested. The proposed approach effectiveness is illustrated with the obtained results and the control proposal performance against disturbances is validated.

Neural High Order Sliding Mode Control for Doubly Fed Induction Generator based Wind Turbines

Wind energy has many advantages because it does not pollute and is an inexhaustible source of energy. In this paper Neural High Order Sliding Mode (NHOSM) control is developed for Doubly Fed Induction Generator (DFIG) based Wind Turbine (WT). The stator winding is directly coupled with the main network, whereas a Back-to-Back converter is installed to connect its rotor to the grid. The proposed control scheme is composed of Recurrent High Order Neural Network (RHONN) trained with the Extended Kalman Filter (EKF), which is used to build-up the DFIG models. Based on such identifier, the High Order Sliding Mode (HOSM) using Super-Twisting (ST) algorithm is synthesized. To show the potential of the selected scheme, a comparison study considering the NHOSM, Conventional Sliding mode (CSM), and the HOSM control is done. To ensure maximum power extractions and to protect the system, the Maximum Point Power Tracking (MPPT) algorithm and the h control are also implemented. Simulation results demonstrate the effectiveness of the proposed scheme for enhancing robustness, reducing chattering, and improving quality and quantity of the generated power.

UKF-Based Neural Training for Nonlinear Systems Identification and Control Improvement

Numerous academic works have addressed the identification and control problem for complex dynamic systems. In recent decades, the use of control algorithms based on neural networks (NNs) has been highlighted, which have shown satisfactory results in the trajectory tracking control for a class of discrete-time nonlinear systems. The present work proposes an efficient learning law for discrete-time recurrent high order neural networks (RHONNs), using a training algorithm based on an unscented Kalman filter (UKF). This learning law is applied through a decentralized neural block control using UKF (DNBC-UKF), for trajectory tracking control of a class of discrete-time nonlinear systems. The proposed controller is experimentally evaluated by means of real-time tests in a two degrees of freedom (DOF) vertical direct-drive robotic manipulator against a decentralized neural block control using EKF (DNBC-EKF). The Lyapunov stability theory is used to show that the identification errors of RHONNs are semi-globally uniformly ultimately bounded (SGUUB), the RHONN weights remains bounded, and the closed-loop tracking errors go to zero.

Physics-Informed Tensor-Train ConvLSTM for Volumetric Velocity Forecasting of the Loop Current

According to the National Academies, a week long forecast of velocity, vertical structure, and duration of the Loop Current (LC) and its eddies at a given location is a critical step toward understanding their effects on the gulf ecosystems as well as toward anticipating and mitigating the outcomes of anthropogenic and natural disasters in the Gulf of Mexico (GoM). However, creating such a forecast has remained a challenging problem since LC behavior is dominated by dynamic processes across multiple time and spatial scales not resolved at once by conventional numerical models. In this paper, building on the foundation of spatiotemporal predictive learning in video prediction, we develop a physics informed deep learning based prediction model called—Physics-informed Tensor-train ConvLSTM (PITT-ConvLSTM)—for forecasting 3D geo-spatiotemporal sequences. Specifically, we propose (1) a novel 4D higher-order recurrent neural network with empirical orthogonal function analysis to capture the hidden uncorrelated patterns of each hierarchy, (2) a convolutional tensor-train decomposition to capture higher-order space-time correlations, and (3) a mechanism that incorporates prior physics from domain experts by informing the learning in latent space. The advantage of our proposed approach is clear: constrained by the law of physics, the prediction model simultaneously learns good representations for frame dependencies (both short-term and long-term high-level dependency) and inter-hierarchical relations within each time frame. Experiments on geo-spatiotemporal data collected from the GoM demonstrate that the PITT-ConvLSTM model can successfully forecast the volumetric velocity of the LC and its eddies for a period greater than 1 week.

Dynamical behavior of recurrent neural networks with different external inputs

The main aim of this paper is to study the dynamics of a recurrent neural networks with different input currents in terms of asymptotic point. Under certain circumstances, we studied the existence, the uniqueness of bounded solutions and their homoclinic and heteroclinic motions of the considered system with rectangular currents input. Moreover, we studied the unpredictable behavior of the continuous high-order recurrent neural networks and the discrete high-order recurrent neural networks. Our method was primarily based on Banach’s fixed-point theorem, topology of uniform convergence on compact sets and Gronwall inequality. For the demonstration of theoretical results, we give examples and their numerical simulations.

Learning Impulsive Pinning Control of Complex Networks

In this paper, we present an impulsive pinning control algorithm for discrete-time complex networks with different node dynamics, using a linear algebra approach and a neural network as an identifier, to synthesize a learning control law. The model of the complex network used in the analysis has unknown node self-dynamics, linear connections between nodes, where the impulsive dynamics add feedback control input only to the pinned nodes. The proposed controller consists of the linearization for the node dynamics and a reorder of the resulting quadratic Lyapunov function using the Rayleigh quotient. The learning part of the control is done with a discrete-time recurrent high order neural network used for identification of the pinned nodes, which is trained using an extended Kalman filter algorithm. A numerical simulation is included in order to illustrate the behavior of the system under the developed controller. For this simulation, a 20-node complex network with 5 different node dynamics is used. The node dynamics consists of discretized versions of well-known continuous chaotic attractors.

Neural identification of Type 1 Diabetes Mellitus for care and forecasting of risk events

Glucose–insulin models, testing glucose sensors and support systems for health care decisions play an important role in synthesis of glucose control algorithms. In this work we propose an online glucose–insulin identification using the Recurrent High Order Neural Network (RHONN). Then, the model obtained is used to predict n-steps forward of glucose levels, also by RHONN. The used data for identification is from a Type 1 Diabetes Mellitus (T1DM) patient, it was collected from the Continuous Monitoring Glucose System (CMGS) by MiniMed Inc ® and an insulin pump by Paradigm Real-time Insulin Pump ®. RHONN is trained online by Extended Kalman Filter (EKF). The results suggest that it is possible to make a prediction of up to 35 min in the future, which it would help to prevent risky events (hypoglycemia and hyperglycemia). Also shows that, it could be directly connected to a CGMS to help the patient improve the glucose control and even an automatic glucose control algorithm. The proposed Neural Network shows good performance compared to baseline methods in terms of evaluation criteria.

Neural control of an induction motor with regenerative braking as electric vehicle architecture

This paper presents the synthesis of an induction motor neural controller and a regenerative braking controller for an electric vehicle architecture, based on two energy system, a Main Energy System (MES) and an Auxiliary Energy System (AES). Such controllers are based on system identification, trajectory tracking and state estimation. System identification uses a Recurrent High Order Neural Network (RHONN), trained with an Extended Kalman Filter (EKF). RHONN obtains an accurate motor model which is robust in presence of external disturbances and parameter variations. To force the motor to track a desired torque trajectory and to reject undesired disturbances, an inverse optimal controller based on the identified neural model is proposed. Therefore, the proposed scheme does not need a-priori knowledge of motor parameters. For state estimation a super-twisting observer is implemented to estimate the rotor magnetic fluxes. The regenerative braking controller addresses the issue when the battery is not capable to accept the generated energy due to braking; Therefore, an AES based on a super capacitor and a buck–boost converter is a solution to recover the braking energy and give power to the motor during acceleration. The regenerative braking controller is based on a PI control to regulate voltage and current of the super capacitor. Simulation and experimental results illustrate the performance of the proposed controllers which are implemented using a rapid control prototyping platform integrated by a dSPACE board. Experimental tests are carried out for a topology with and without AES to illustrate the improvement of the proposed EV architecture.

Real‐time model‐free resilient control for discrete nonlinear systems

AbstractMotivated by the increasing complexity of systems to be controlled and different system components that cause uncertainties, threats, and disturbances, among other system stressing phenomena. Resilient control is an important design paradigm that has attracted attention from academics, practitioners, and the industrial sector. Therefore, this paper proposes the design of a model‐free resilient control for unknown discrete‐time nonlinear systems based on a recurrent high order neural network trained with an on‐line extended Kalman filter‐based algorithm for output trajectory tracking in the presence of uncertainties, disturbances, and unmodeled dynamics. This paper also includes the stability proof of the entire proposed scheme; its applicability is shown via simulation and experimental results including a comparative analysis of the proposed controller against well‐known controllers for a three‐phase induction motor.