Robust Adaptive Neural Control Research Articles

Design of a optimal robust adaptive neural network-based fractional-order PID controller for H-bridge single-phase inverter

Adaptive Neural Network- based Fractional-order PID Control (NN-FO-PID) approach is designed for H-bridge inverter. This inverter has an LC filter to decrease the level of Total Harmonic Distortion (THD) that can affect the efficiency of the system. In addition, the reduction of THD and stability insurance of the filter are challenging performances. To fulfill this function, a fractional order proportional integrated derivative (FO-PID) controller is developed for the inverter. A few merits of the Fractional-Order notion as a useful technique include reduced sensitivity to noise and parametric fluctuation; however, for a wider range of disturbances, such as noise, this approach shows an unsuitable practical application based on its fixed gain values. Moreover, having parameters uncertainties including parametric variations, load uncertainty, supply voltage variation uplifts this challenging condition severely, and the parameters need to be adjusted once more for more dependable operations. As a result, the control parameters must be optimized once more to provide ideal operations. Here, an adaptive mechanism is proposed based on neural network structure to optimize the gains of the FO-PID controller for better performances. In real applications, this approach has some benefits, since it uses the Black-box technique, which does not necessitate a precise mathematical model of the system, resulting in a reduced computational burden, simple implementation, and reduced dependence on the model’s states. Even under extremely difficult circumstances, the artificial neural network structure effectively optimizes the FO-PID gains in real-time. This benefit can reduce the amount of THD-level for the inverter, properly. Additionally, to have a proper response in the first step condition, and trying to reduce the level of dangerous conditions, based on benefits of the ant lion optimization method, it is considered to select the initial values of the FO-PID controller gains. Here to verify the superiority of the proposed controller, PID and FO-PID controllers are offered to drive a comparison with this method, which are optimized using the PSO optimization algorithm. The analysis of simulation data shows that the suggested control strategy is appropriate for maintaining stability as well as adequately compensating for disturbances and uncertainties in the inverter. Furthermore, with the help of this controller, THD level decreased lower than 2 percent.

Robust adaptive control for dynamic positioning vessels with actuator faults via the multiport event-triggered mechanism

This paper presents a robust adaptive neural control algorithm for dynamic positioning tasks of full-actuated vessels using a novel multiport event-triggered mechanism. The proposed mechanism only updates control signals and feedback error signals upon violation of the multiport event-triggered rule. Therefore, the communication loads between the sensors and controller, as well as between the controller and actuators, are significantly mitigated. Furthermore, a fault-adaptive method is adopted by estimating the uncertain actuator faults, based on the proposed event-triggered mechanism, the fault-adaptive parameters will be updated at the triggering instant, eliminating the destabilizing effects of the closed-loop system. Besides, the dynamic surface control (DSC) and the robust neural damping technique are employed to address the problems of “complexity explosion” and system uncertainties. Based on Lyapunov stability theory, the closed-loop system is proved to satisfy semi-globally uniform ultimate bounded (SGUUB). Finally, a simulation experiment validates the effectiveness of the proposed algorithm.

Neural Network-Based Adaptive Height Tracking Control of Active Air Suspension System with Magnetorheological Fluid Damper Subject to Uncertain Mass and Input Delay.

In this paper, we present a novel robust adaptive neural network-based control framework to address the ride height tracking control problem of active air suspension systems with magnetorheological fluid damper (MRD-AAS) subject to uncertain mass and time-varying input delay. First, a radial basis function neural network (RBFNN) approximator is designed to compensate for unmodeled dynamics of the MRD. Then, a projector-based estimator is developed to estimate uncertain parameter variation (sprung mass). Additionally, to deal with the effect of input delay, a time-delay compensator is integrated in the adaptive control law to enhance the transient response of MRD-AAS system. By introducing a Lyapunov-Krasovskii (LK) functional, both ride height tracking and estimator errors can robustly converge towards the neighborhood of the desired values, achieving uniform ultimate boundness. Finally, comparative simulation results based on a dynamic co-simulator built in AMESim 2021.2 and Matlab/Simulink 2019(b) are given to illustrate the validity of the proposed control framework, showing its effectiveness to operate ride height regulation with MRD-AAS systems accurately and reliably under random road excitations.

Robust adaptive backstepping neural networks fault tolerant control for mobile manipulator UAV with multiple uncertainties

The present study outlines the development of an Adaptive Backstepping Radial Basis Function Neural Networks Fault Tolerant Control (ABRBFNNFTC) methodology. The aforementioned methodology is employed to address the challenge of achieving trajectory following in the context of a Mobile Manipulator Unmanned Aerial Vehicle (MMUAV) when subjected to the effects of actuator faults and parametric uncertainties. The utilization of an adaptive radial basis function neural networks (RBFNNs) controller is employed for the purpose of approximating an unidentified nonlinear backstepping controller that relies on the precise model of the MMUAV. The Lyapunov direct method is utilized to establish the stability analysis of the entire system. The closed-loop system guarantees the Uniformly Ultimately Bounded (UUB) stability of all signals. The control methodology put forth ensures the achievement of a prescribed trajectory, and mitigates the impact of uncertainties and actuator faults. The efficiency of the proposed ABRBFNNFTC scheme is demonstrated through the presentation of extensive simulation studies.

Disturbance observer-based adaptive neural control for underactuated surface vehicle with constraint of input saturation

In this paper, a robust adaptive neural control algorithm is provided to solve the problem of path-following control for underactuated surface vehicle (USV) subject to input saturation, in the presence of model uncertainties and unknown marine environmental disturbance.In the algorithm, through the application of neural network (NN), an adaptive disturbance observer (ADO) is proposed to observe the unknown disturbance, which does not need the precise information of the upper bound of the disturbance, and the model uncertainty can be appropriated simultaneously. Specially, since the ADO and control law share the same set of NN, the number of adaptive parameters of the controller can be greatly reduced. After that, dynamic surface control (DSC) method is used to avoid repeated derivative in the process of back-stepping, which solve the “calculation explosion” problem. An auxiliary system is designed to compensate errors caused by input saturation, which solve the input saturation problem of actuators. Through Lyapunov stability analysis, it is proved that all signals of the closed-loop system are semi-globally ultimately uniformly bounded (SGUUB). Finally, some experiments are conducted to demonstrate the effectiveness of the algorithm.

Neural adaptive robust control for MEMS gyroscope with output constraints

Neural adaptive robust control for MEMS gyroscope with output constraints

Robust Adaptive Neural Control for Wing-Sail-Assisted Vehicle via the Multiport Event-Triggered Approach.

This article presents a robust adaptive neural control algorithm for the wing-sail-assisted vehicle to track the desired waypoint-based route, where the event-triggered mechanism is with the multiport form. The main features of the proposed algorithm are three-fold: 1) the communication burden, in the channel from the sensor to the controller as well as the actuator, has been reduced for the merits of the multiport event-triggered approach. The feedback error signals and the control input will be updated only on the event-triggered time point; 2) for the wing-sail-assisted vehicle, the thrust force is provided by devices with the propeller and the sail. From this consideration, the proper sail force compensation is derived on the basis of information about the current heading angle and the wind direction. The corresponding control law can guarantee the energy-saving for the propeller; and 3) in the algorithm, the system uncertainties are remodeled by the neural-network approximator. Furthermore, by fusion of the robust neural damping and dynamic surface control (DSC) techniques, the corresponding gain-related adaptive law is developed to address constraints of the gain uncertainty and the environmental disturbances. Through the Lyapunov theorem, all signals of the closed-loop control system have been proved to be with the semiglobal uniform ultimate bounded (SGUUB) stability, including the triggered time point and the intermediate triggered interval. Finally, the numerical simulation and the practical experiment are illustrated to verify the effectiveness of the proposed strategy.

Adaptive neural control for nonlinear switched systems with improved MDADT and its applications

In this paper, a new framework of the robust adaptive neural control for nonlinear switched stochastic systems is established in the presence of external disturbances and system uncertainties. In the existing works, the design of robust adaptive control laws for nonlinear switched systems mainly relies on the average dwell time method, while the design and analysis based on the model-dependent average dwell time (MDADT) method remains a challenge. An improved MDADT method is developed for the first time, which greatly relaxes the requirements of Lyapunov functions of any two subsystems. Benefiting from the improved MDADT, a switched disturbance observer for discontinuous disturbances is proposed, which realizes the real-time gain adjustment. For known and unknown piecewise continuous nonlinear functions, a processing method based on the tracking differentiator and the neural network is proposed, which skillfully guarantees the continuity of the control law. The theoretical proof shows that the semiglobal uniform ultimate boundedness of all closed-loop signals can be guaranteed under switching signals with MDADT property, and simulation results of the longitudinal maneuvering control at high angle of attack are given to further illustrate the effectiveness of the proposed framework.

Robust Adaptive Neural Control of the Blood Glucose for Type 1 Diabetic Patients in Presence of Meals

Robust Adaptive Neural Control of the Blood Glucose for Type 1 Diabetic Patients in Presence of Meals

Robust adaptive neural control for a class of perturbed nonlinear systems with unmodeled dynamics and output disturbances

AbstractFor a nonlinear system with unmodeled dynamics and output disturbances, an adaptive neural network based control strategy is designed. For the unknown output interference, this article introduces a new module between the system output and the tracking signal for the first time. By constructing a new state, system becomes a new system without unknown output interference. In order to alleviate the major influence caused by the system dynamic disturbance and unknown time delay, appropriate Lyapunov–Krasovskii functions, and backstepping technology are employed. In designing some adaptive methods, the unmodeled dynamics, disturbances, and unknown time‐varying virtual coefficients are processed. Unknown function is approximated by neural network. All signals are semi‐global uniform ultimate boundedness. Simulation effectively verifies the control strategy.

Robust Adaptive Neural Cooperative Control for the USV-UAV Based on the LVS-LVA Guidance Principle

Around the cooperative path-following control for the underactuated surface vessel (USV) and the unmanned aerial vehicle (UAV), a logic virtual ship-logic virtual aircraft (LVS-LVA) guidance principle is developed to generate the reference heading signals for the USV-UAV system by using the “virtual ship” and the “virtual aircraft”, which is critical to establish an effective correlation between the USV and the UAV. Taking the steerable variables (the main engine speed and the rudder angle of the USV, and the rotor angular velocities of the UAV) as the control input, a robust adaptive neural cooperative control algorithm was designed by employing the dynamic surface control (DSC), radial basic function neural networks (RBF-NNs) and the event-triggered technique. In the proposed algorithm, the reference roll angle and pitch angle for the UAV can be calculated from the position control loop by virtue of the nonlinear decouple technique. In addition, the system uncertainties were approximated through the RBF-NNs and the transmission burden from the controller to the actuators was reduced for merits of the event-triggered technique. Thus, the derived control law is superior in terms of the concise form, low transmission burden and robustness. Furthermore, the tracking errors of the USV-UAV cooperative control system can converge to a small compact set through adjusting the designed control parameters appropriately, and it can be also guaranteed that all the signals are the semi-global uniformly ultimately bounded (SGUUB). Finally, the effectiveness of the proposed algorithm has been verified via numerical simulations in the presence of the time-varying disturbances.

Robust adaptive neural network event-triggered compensation control for continuous stirred tank reactors with prescribed performance and actuator failures

This paper studies the event-triggered compensation tracking control problem of the Continuous Stirred Tank Reactor (CSTR) with actuator failures. By actuator redundancies, a novel adaptive neural network (NN) event-triggered controller (ETC) design scheme is proposed based on the switching threshold event-triggering mechanism (SWT-ETM). To constrain the maximum overshoot and the convergence rate within given specifications, a prescribed performance function (PPF) error transformation is employed. It is shown that the tracking error will exponentially converge to an adjustable neighborhood of zero with prescribed transient performance, despite the presence of failures, system nonlinearities, parametric uncertainties and external disturbances. Besides, the system’s burden is significantly alleviated requiring less system resources for signal transmission and actuator execution to decrease the occurrence rate of the actuator failures; and the minimum inter-event interval is guaranteed to be positive to avoid the Zeno phenomenon. Simulation results illustrate the effectiveness of the proposed adaptive NN ETC scheme.

Output feedback synchronization control for supply vessels during underway replenishment with unknown dynamics and disturbances under input saturation

ABSTRACT This paper investigates the synchronization control problem for a supply vessel during underway replenishment with unknown dynamics, unknown disturbances, input saturation and unavailable velocities. A virtual trajectory shifted by a distance at an angle relative to the trajectory of the supplied vessel is planned. A high-gain observer observer is employed to estimate unavailable vessel velocities and an auxiliary design system is constructed to address the effect of input saturation. Then, the adaptive radial basis function neural networks are employed to approximate unknown dynamics and the adaptive laws are derived to estimate bounds of unknown disturbances. Incorporating the above into the dynamic surface control method, an output feedback robust adaptive neural synchronization control law is developed to force the supply vessel to track the virtual trajectory such that the synchronization navigation between the supply and the supplied vessels is realized. The theoretical analysis and simulation results validate our developed control scheme.

Robust Adaptive Neural Control of Nonminimum Phase Hypersonic Vehicle Model

This paper investigates the robust adaptive neural control of nonminimum phase hypersonic flight vehicle using composite learning. To overcome the nonminimum phase behavior, the output redefinition is employed and the attitude subsystem is transformed to the internal subsystem and the input-output subsystem. For the input-output subsystem, the adaptive neural control works together with the robust control to follow the reference command of pitch angle derived from the internal subsystem. Furthermore, the sliding mode control is constructed in a similar way. For the update of the neural weights, the composite learning is constructed using the prediction error. The stability of the closed-loop system is analyzed via the Lyapunov approach and the ultimately uniform boundedness of the tracking errors can be guaranteed. The effectiveness of the methodology is illustrated by the simulation results.

Trajectory tracking of a quadrotor using a robust adaptive type-2 fuzzy neural controller optimized by cuckoo algorithm

This paper proposes an adaptive and robust adaptive control strategy based on type-2 fuzzy neural network (T2FNN) for tracking the desired trajectories of a quadrotor. The designed methods can control both the position and the orientation of a quadrotor. Contrary to common sliding mode controllers (SMCs), the robust adaptive trajectory tracking scheme presented here is based on SMC with exponential reaching law; which helps reduce the phenomenon of chattering. Moreover, parameters including the gains of sliding surfaces, are optimized by cuckoo optimization algorithm (COA), and the results are compared with those obtained by genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO). The designed methods in this study are called adaptive T2FNN controller and the exponential SMC (ESMC)-T2FNN. The law for updating the T2FNN is obtained online by using the Lyapunov stability theory. Considering undesired factors such as uncertainties, external disturbances and control signal saturation, the results of our controllers are compared with those of the adaptive type-1 fuzzy neural network controller (T1FNN) and ESMC-T1FNN. The extensive simulations demonstrate the effectiveness of the proposed COA-based ESMC-AT2FNN approach compared to the other tested techniques (i.e. GA, PSO and ACO) in terms of the improved transient and steady-state trajectory-tracking performance. The mean and standard deviation values concerning the COA are obtained through statistical analyses at 0.00006173 and 0.000092, respectively. This paper also examines the complexity of COA in optimizing the trajectory tracking control of quadrotor and investigates the effects of COA parameters on optimization results. The stable performance of the cuckoo algorithm is demonstrated by varying its parameters and analyzing the obtained results. These results also show the convergence of COA for the considered problem.

Robust adaptive neural network-based compensation control of a class of quadrotor aircrafts

In this paper, the position and attitude trajectory tracking problem of a class of quadrotor aircrafts with bounded external disturbances and state-dependent internal uncertainties is addressed. Neural network (NN)-based methods are adopted to approximate the unknown uncertainties, while adaptive technique is used to estimate the unknown bounds of disturbances. Then, an adaptive compensation control scheme based on neural networks is proposed to compensate for the effects of disturbances and uncertainties. On the basis of Lyapunov stability theorem, bounded trajectory tracking of a position subsystem and asymptotic trajectory tracking of an attitude subsystem can be achieved by using the NN-based adaptive compensation control scheme in the presence of internal uncertainties and external disturbances. A numerical simulation is carried out to verify the effectiveness of the designed control method of quadrotor aircrafts.

Sliding mode learning algorithm based adaptive neural observer strategy for fault estimation, detection and neural controller of an aircraft

In this paper, two different adaptive strategies are presented for continuous time uncertain nonlinear systems with unknown disturbances and faults. In first strategy, a sliding mode control based adaptive neural observer approach is anticipated for estimation of unknown disturbances and faults by using the multi-layer perceptron, the weight parameters are updated by using the sliding mode online learning strategy. Conventionally, gradient descent back-propagation adaptation methods are used for neural networks training, within these adaptation methods a new theory of sliding mode control is added to conventional gradient descent back-propagation procedure. In this nonlinear control concept, the Sliding Mode Control is employed as a learning strategy, in which the neural network is considered as a control process and computes the stable and dynamic learning rates of neural network. By considering the unknown faults approximation and reconstruction, this online learning strategy shows a rapid sensor fault detection, approximation, and reconstruction with high preciseness and rapidness compared to conventional strategy and algorithms presented in literature. Approaches used in literature do not have much higher preciseness and fast response to fault occurrence compared to the strategy proposed in this study. In second strategy, the neural network controller strategy is proposed with concept of filtered error scheme. Online weight updating strategy comprise of additional term to back-propagation, plus an additional robustifying term, assures the stability and rapid convergence of the faulty system. The stability analysis of the proposed fault tolerance control is also provided. While considering stability of system, this robust online adaptive fault tolerance control shows a fast convergence in the presence of unknown disturbances and faults. The robust adaptive neural controller is compared with the conventional gradient descent based controller in the existence of various sensor faults and failures. The proposed strategies are validated on Boeing 747 100/200 aircraft, results show the efficiency, preciseness and robustness of strategies compared to the algorithm presented in literature.

Robust adaptive neural trajectory tracking control of surface vessels under input and output constraints

In this paper, a novel robust adaptive control scheme is developed for the trajectory tracking of surface vessels in the presence of dynamic uncertainties, unknown time-varying disturbances and input and output constraints. Firstly, the output constraint problem is transformed into the constraint problem of trajectory tracking error by coordinate transformations. Then, a nonlinear transformation is introduced to transform the tracking error into the transformed variable, with the aid of which the output constraint problem of surface vessel trajectory tracking is transformed into the boundedness problem of transformed variable, such that the various types of output constraint boundaries including constant, time-varying, symmetry and asymmetry ones can be handled in a unified framework. An auxiliary dynamic system (ADS) is applied to handle the input saturation effect. Incorporating the nonlinear transformation, the radial basis function neural networks and the ADS into dynamic surface control technique, a novel robust adaptive neural trajectory tracking control law is designed. It is theoretically proved that all signals in the closed-loop trajectory tracking control system of surface vessels are locally uniformly ultimately bounded and the actual positions and heading of surface vessels are guaranteed to lie in the constrained region. Simulation results on a scale model vessel illustrate the effectiveness of the proposed control scheme.

Robust adaptive neural practical fixed-time tracking control for uncertain Euler-Lagrange systems under input saturations

This paper develops a robust adaptive neural practical fixed-time tracking control scheme for Euler-Lagrange systems (ELSs) with unknown dynamics and external disturbances under input saturations. A novel auxiliary dynamic system governed by a smooth piecewise continuous function is constructed to handle the input saturation effect, while promoting to achieve the fixed-time convergence of tracking errors. Moreover, the unknown dynamics of ELSs and the bound vector of unknown external disturbances are synthesized into a compounded uncertain vector in this paper. Here, adaptive neural networks with the ∊-modification updating laws are employed to only approximate the compounded uncertain vector, rather than each dynamic matrix of ELSs, such that the computational burden of the developed control scheme is significantly reduced. It is theoretically proven that the trajectory tracking is able to be achieved in a fixed time under the developed adaptive neural tracking control scheme, while all signals in the Euler-Lagrange closed-loop tracking control system are bounded. The simulation results on a 2-link robotic manipulator are included to illuminate the effectiveness of our developed tracking control scheme and superiority to a finite-time control scheme.

Coordinated Control of a Tractor-Trailer and a Combine Harvester by Neural Adaptive Robust Control

Coordinated Control of a Tractor-Trailer and a Combine Harvester by Neural Adaptive Robust Control