Neural Inverse Optimal Control Research Articles

Event-triggered adaptive neural inverse optimal output-feedback control of steer-by-wire vehicle systems with prescribed performance

In this article, an event-triggered adaptive neural network (NN) output-feedback inverse optimal control issue is investigated for the steer-by-wire vehicle (SBWV) systems. Firstly, NNs are utilized to approximate the unknown nonlinear dynamics and the auxiliary system of SBWV systems is established. Then, a NN state observer is constructed to estimate the unmeasured states. To obtain better tracking performance, the prescribed performance technique is introduced to constrain the tracking error. An event-triggered mechanism (ETM) is established to decrease the numbers of controller execution times. Subsequently, an event-triggered adaptive NN inverse optimal output-feedback control algorithm is proposed by employing the backstepping control theory. It is proved that the developed control method can not only ensure the stability of the SBWV systems, but also guarantee the tracking error does not exceed the prescribed performance bound and converges to a small neighborhood of zero. Finally, simulation results are given to verify the validity of the proposed control method.

Adaptive neural inverse optimal control for uncertain switched nonlinear systems: an improved predefined-time method

For the first time, this paper introduces an improved predefined-time inverse optimal method to address the adaptive tracking control problem of a class of uncertain switched nonlinear systems. The proposed approach enables the system to achieve optimal performance without resorting to the HJB equation. Firstly, we design two-time constants to improve the traditional predefined-time lemma. The improved predefined-time lemma exhibits faster convergence. Subsequently, to realise the design of a predefined-time inverse optimal controller, a specific form of the auxiliary controller is designed using Sontag functions and nonsingular functions. Furthermore, the stability of the system and the optimality of the inverse are proven through the use of a common Lyapunov function method, ensuring that tracking errors converge to the neighbourhood of zero within the predefined time. Finally, the rationality of the proposed control structure is demonstrated through the example results.

Adaptive neural inverse optimal control with predetermined tracking accuracy for nonlinear MIMO systems

Adaptive neural inverse optimal control with predetermined tracking accuracy for nonlinear MIMO systems

Decentralized Adaptive Neural Inverse Optimal Control of Nonlinear Interconnected Systems.

Existing methods on decentralized optimal control of continuous-time nonlinear interconnected systems require a complicated and time-consuming iteration on finding the solution of Hamilton-Jacobi-Bellman (HJB) equations. In order to overcome this limitation, in this article, a decentralized adaptive neural inverse approach is proposed, which ensures the optimized performance but avoids solving HJB equations. Specifically, a new criterion of inverse optimal practical stabilization is proposed, based on which a new direct adaptive neural strategy and a modified tuning functions method are proposed to design a decentralized inverse optimal controller. It is proven that all the closed-loop signals are bounded and the goal of inverse optimality with respect to the cost functional is achieved. Illustrative examples validate the performance of the methods presented.

Adaptive Neural Inverse Optimal Tracking Control for Uncertain Nonlinear System With Actuator Faults

Most optimal control method aim at finding a control law to minimize the corresponding cost function, and the key problem is to solve the Hamilton–Jacobi–Bellman equation (HJBE). However, the solution of HJBE is hard and even unavailable to obtain for nonlinear system and, thus, the optimal control method can not be developed. To address this problem, an adaptive neural inverse optimal control method is proposed for nonlinear systems with external disturbances and actuator failures in this article. The cost function in this article is defined based on the controller and, therefore, once the controller is obtained, the corresponding cost function is minimized. Accordingly, the directly solution of HJBE is avoided. In more detail, the auxiliary system is constructed to develop the auxiliary controller, based on which the actual optimal fault-tolerant controller can be further obtained. The uncertainties in system dynamics are compensated by neural networks, and the unknown actuator failures are handled by adaptive control gain. It is proved that nonlinear system is input-to-state stabilizable (ISS) and the proposed controller can minimize the cost function that puts penalty on tracking error and system input. Two simulation examples are provided to demonstrate the effectiveness of the proposed control method.

Observer-Based adaptive neural inverse optimal consensus control of nonlinear multiagent systems

The objective of this article is to present an adaptive neural inverse optimal consensus tracking control for nonlinear multi-agent systems (MASs) with unmeasurable states. In the control process, firstly, to approximate the unknown state, a new observer is created which includes the outputs of other agents and their estimated information. The neural network is used to reckon the uncertain nonlinear dynamic systems. Based on a new inverse optimal method and the construction of tuning functions, an adaptive neural inverse optimal consensus tracking controller is proposed, which does not depend on the auxiliary system, thus greatly reducing the computational load. The developed scheme not only insures that all signals of the system are cooperatively semiglobally uniformly ultimately bounded (CSUUB), but also realizes optimal control of all signals. Eventually, two simulations provide the effectiveness of the proposed scheme.

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.

Neural Inverse Optimal Control of a Regenerative Braking System for Electric Vehicles

This paper presents the development of a neural inverse optimal control (NIOC) for a regenerative braking system installed in electric vehicles (EVs), which is composed of a main energy system (MES) including a storage system and an auxiliary energy system (AES). This last one is composed of a supercapacitor and a buck–boost converter. The AES aims to recover the energy generated during braking that the MES is incapable of saving and using later during the speed increase. To build up the NIOC, a neural identifier has been trained with an extended Kalman filter (EKF) to estimate the real dynamics of the buck–boost converter. The NIOC is implemented to regulate the voltage and current dynamics in the AES. For testing the drive system of the EV, a DC motor is considered where the speed is controlled using a PID controller to regulate the tracking source in the regenerative braking. Simulation results illustrate the efficiency of the proposed control scheme to track time-varying references of the AES voltage and current dynamics measured at the buck–boost converter and to guarantee the charging and discharging operation modes of the supercapacitor. In addition, it is demonstrated that the proposed control scheme enhances the EV storage system’s efficacy and performance when the regenerative braking system is working. Furthermore, the mean squared error is calculated to prove and compare the proposed control scheme with the mean squared error for a PID controller.

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.

Adaptive neural inverse optimal tracking control for uncertain multi-agent systems

An adaptive neural inverse optimal consensus control method is presented for a class of multi-agent systems (MASs) with uncertain dynamics and time-varying disturbance. The auxiliary system is constructed for every agent, and then, based on backstepping technique, neural networks are utilized to approximate the unknown function and develop the distributed controllers. Additionally, there is only one unknown parameter need to be learned for each agent. It is proved that the proposed control scheme can ensure that all states of MASs are bounded and each agent is Input-to-state stabilizable (ISS). In the meantime, the inverse optimal controller also minimizes a meaningful cost function given in advance, which contains system input and disturbance. That is, the control scheme also reduces the cost of input. Simulation experiments illustrate the feasibility of the proposed method.

Treatment for T1DM patients by a neuro-fuzzy inverse optimal controller including multi-step prediction

Diabetes Mellitus is a serious metabolic condition for global health associations. Recently, the number of adults, adolescents and children who have developed Type 1 Diabetes Mellitus (T1DM) has increased as well as the mortality statistics related to this disease. For this reason, the scientific community has directed research in developing technologies to reduce T1DM complications. This contribution is related to a feedback control strategy for blood glucose management in population samples of ten virtual adult subjects, adolescents and children. This scheme focuses on the development of an inverse optimal control (IOC) proposal which is integrated by neural identification, a multi-step prediction (MSP) strategy, and Takagi–Sugeno (T–S) fuzzy inference to shape the convenient insulin infusion in the treatment of T1DM patients. The MSP makes it possible to estimate the glucose dynamics 15 min in advance; therefore, this estimation allows the Neuro-Fuzzy-IOC (NF-IOC) controller to react in advance to prevent hypoglycemic and hyperglycemic events. The T–S fuzzy membership functions are defined in such a way that the respective inferences change basal infusion rates for each patient’s condition. The results achieved for scenarios simulated in Uva/Padova virtual software illustrate that this proposal is suitable to maintain blood glucose levels within normoglycemic values (70–115 mg/dL); furthermore, this level remains less than 250 mg/dL during the postprandial event. A comparison between a simple neural IOC (NIOC) and the proposed NF-IOC is carried out using the analysis for control variability named CVGA chart included in the Uva/Padova software. This analysis highlights the improvement of the NF-IOC treatment, proposed in this article, on the NIOC approach because each subject is located inside safe zones for the entire duration of the simulation.

Optimal Neural Tracking Control with Metaheuristic Parameter Identification for Uncertain Nonlinear Systems with Disturbances

In this paper, we propose an inverse optimal neural control strategy for uncertain nonlinear systems subject to external disturbances. This control strategy is developed based on a neural observer for the estimation of unmeasured states and inverse optimal control theory for trajectory tracking. The stabilization of states along the desired trajectory is ensured via a control Lyapunov function. The optimal parameters of the control law are identified by different nature-inspired metaheuristic algorithms, namely: Ant Lion Optimizer, Grey Wolf Optimizer, Harris Hawks Optimization, and Whale Optimization Algorithm. Finally, a highly nonlinear biological system subject to parameter uncertainties and external disturbances (Activated Sludge Model) is proposed to validate the control strategy. Simulation results demonstrate that the control law with Ant Lion Optimizer outperforms the other optimization methods in terms of trajectory tracking in the presence of disturbances. The control law with Harris Hawks Optimization shows a better convergence of the neural states in presence of parameter uncertainty.

Real-time Neural Inverse Optimal Control for Low-Voltage Rid-Through enhancement of Double Fed Induction Generator based Wind Turbines

The Low-Voltage Ride-Through (LVRT) capacity of the Doubly Fed Induction Generator (DFIG) is one of the important requirements to ensure power systems stability, incorporating wind energy. While traditional control schemes present inappropriate performances under disturbances, this paper introduces a novel Neural Inverse Optimal Control (N-IOC) scheme for LVRT capacity enhancing. The developed controller is synthesized using recurrent high order neural network, which is utilized to build-up the DFIG and the DC-link dynamics. Based on such identifier, the proposed N-IOC is synthesized. This controller is experimentally validated on 1∕4 HP DFIG prototype considering various grid disturbances. Results illustrate the proposed controller effectiveness for LVRT enhancement without required decomposition process and/or any additional device.

Real‐time implementation of battery bank charge–discharge based on neural inverse optimal control

In a renewable energy generation system, the batteries are one of the main components for energy storage. To maximise the useful life of batteries, it is important to ensure a safe and effective rate in the process of battery charging and discharging. To control these processes, different electronic circuits can be used, of which, the most commonly implemented is the DC–DC buck–boost converter. Two different topologies with their corresponding controllers are needed because the energy transfer is bidirectional. This work develops a unique neural inverse optimal controller with online identification for both charge and discharge processes of the battery bank. The main feature of the proposed controller is that it does not present dependence on the converter parameter variations; for this reason, it can be applied for systems with different power requirements without considerable changes in its application. This study discusses the development and real-time operation of a neural controller based on the inverse optimal control algorithm for charge–discharge of a battery bank.

Neural Inverse Optimal Control Implementation for Induction Motors via Rapid Control Prototyping

This paper presents a discrete-time neural inverse optimal control for induction motors, which is implemented on a rapid control prototyping (RCP) system using a C2000 Microcontroller-Simulink platform. Such controller addresses the solution of three issues: system identification, trajectory tracking, and state estimation, which are solved independently. The neural controller is based on a recurrent high order neural network (RHONN), which is trained with an extended Kalman filter. The RHONN is an identifier to obtain an accurate motor model, which is robust to external disturbances and parameter variations. The inverse optimal controller is used to force the system to track a desired trajectory and to reject undesired disturbances. Moreover, the controller is based on a neural model and does not need the a-priori knowledge of motor parameters. A supertwisting observer is implemented to estimate the rotor magnetic fluxes. The hub of the RCP system is a TMS320f28069M MCU, which is an embedded combination of a 32-bit C28x DSP core and a real-time control accelerator. This Microcontroller is fully programmable from the Simulink environment. Simulation and experimental results illustrate the performance of the proposed controller and the RCP system, and a comparison with a control algorithm without the neural identifier is also included.

Inverse optimal neural control via passivity approach for nonlinear anaerobic bioprocesses with biofuels production

SummaryThis paper proposes an inverse optimal neural control method of a nonlinear anaerobic bioprocesses model for simultaneous hydrogen and methane production in presence of disturbances. Based on the fundamental properties of the system, a passivity approach is designed such that asymptotic stability is guaranteed. A recurrent high‐order neural network for unknown nonlinear systems in presence of unknown bounded disturbances and parameter uncertainties is proposed to identify nonmeasurable state variables of the system, which are directly related to biofuels production. Optimal control laws based on the neural model are proposed so that the passivation of the entire plant is preserved. The neural control strategy performance for trajectory tracking in presence of disturbances is proven. Results via simulation show the optimal control methodology efficiency to stabilize the H2 and CH4 productions along desired trajectories even in presence of disturbances.

Neural inverse optimal control for discrete-time impulsive systems

Abstract Impulsive systems describe processes with at least one state variable is impulsively changeable. The design of optimal control policies in impulsive systems is a complex task. In order to relax the solution for the Hamilton-Jacobi-Bellman equation, a meaningful cost functional can be proposed a posteriori in the inverse optimal problem. The main contribution of this paper is a neural inverse optimal control for discrete-time impulsive systems. Control policies for discrete-time impulsive systems are derived by combining inverse optimal control into a recurrent high order neural network (RHONN) trained with the Extended Kalman filter (EKF). The neural network avoids the development of a mathematical model to represent the studied system. For illustration, we apply the proposed neurocontrol to personalized drug treatment in influenza infection disease, whose nonlinear model is included and described for completeness. The robustness of the proposed framework is tested through Monte Carlo simulations.

Germinal Center Optimization Applied to Neural Inverse Optimal Control for an All-Terrain Tracked Robot

Nowadays, there are several meta-heuristics algorithms which offer solutions for multi-variate optimization problems. These algorithms use a population of candidate solutions which explore the search space, where the leadership plays a big role in the exploration-exploitation equilibrium. In this work, we propose to use a Germinal Center Optimization algorithm (GCO) which implements temporal leadership through modeling a non-uniform competitive-based distribution for particle selection. GCO is used to find an optimal set of parameters for a neural inverse optimal control applied to all-terrain tracked robot. In the Neural Inverse Optimal Control (NIOC) scheme, a neural identifier, based on Recurrent High Orden Neural Network (RHONN) trained with an extended kalman filter algorithm, is used to obtain a model of the system, then, a control law is design using such model with the inverse optimal control approach. The RHONN identifier is developed without knowledge of the plant model or its parameters, on the other hand, the inverse optimal control is designed for tracking velocity references. Applicability of the proposed scheme is illustrated using simulations results as well as real-time experimental results with an all-terrain tracked robot.

Real-time neural inverse optimal control for indoor air temperature and humidity in a direct expansion (DX) air conditioning (A/C) system

A real-time neural inverse optimal control for the simultaneous control of indoor air temperature and humidity using a direct expansion (DX) air conditioning (A/C) system has been developed and the development results are reported in this paper. A recurrent high order neural network (RHONN) was used to identify the plant model of an experimental DX A/C system. Based on this model, a discrete-time inverse optimal control strategy was developed and implemented to an experimental DX A/C system for simultaneously controlling indoor air temperature and humidity. The neural network learning was on-line performed by extended Kalman filtering (EKF). This control scheme was experimentally tested via implementation in real time using an experimental DX A/C system. The obtained results for trajectory tracking illustrated the effectiveness of the proposed control scheme.