Neural Network Control Scheme Research Articles

A study on a vehicle semi-active suspension control system based on road elevation identification.

A semi-active suspension system can effectively improve vehicle ride comfort and handling stability, and the active detection of road information is key to achieving semi-active suspension. To improve the road elevation perception ability of vehicles, this study proposes a continuous multiple scanning recursive matching algorithm based on a single-line LIDAR sensor. Radar recursive scanning is used to obtain the multiple superposition data of echo signals, and coordinate matching is realized between historical scanning data and current scanning data. Simultaneously, the sensor height deviation and pitch angle deviation of the sensors are regressed to obtain an accurate pavement elevation. Considering the control effect of the active vehicle suspension, a vehicle suspension model with seven degrees of freedom is established. The semi-active suspension controller is constructed using a diagonal recursive neural network algorithm, and the neural network weight is trained using a genetic algorithm. In addition, a preview diagonal recursive neural network control strategy for semi-active suspension, based on the combination of road elevation information, is proposed. The results of a hardware-in-the-loop co-simulation, which was conducted based on the Simulink control model and dSPACE real-time simulation, revealed that the ride comfort and stability of the vehicle were improved owing to a preview of the elevation information of the road ahead and the active adjustment of the shock absorber of the suspension system.

Switching Neural Network Control for Underactuated Spacecraft Formation Reconfiguration in Elliptic Orbits

A switching neural network control scheme, consisting of the adaptive neural network controller and sliding mode controller, is proposed for underactuated formation reconfiguration in elliptic orbits with the loss of either the radial or in-track thrust. By using the inherent coupling of system states, the switching neural network technique is then adopted to estimate the unmatched disturbances and design the underactuated controller to achieve underactuated formation reconfiguration with high precision. The adaptive neural network controller works in the active region, and the disturbances composed of linearization errors and external perturbations are approximated by radial basis function neural networks. The adaptive sliding mode controller works outside the active region, and the upper bound of the approximation errors is estimated by the adaptation law. The stability of the closed-loop control system is proved via the Lyapunov-based approach. The numerical simulation results have demonstrated the rapid, high-precision and robust performance of the proposed controller compared with the linear sliding mode controller.

Dynamic Stabilization of DC Microgrids Using ANN-Based Model Predictive Control

Over the past decade, the high penetration of renewable-based distributed generation (DG) units has witnessed a considerable rise in electrical networks. In this context, direct current (DC) microgrids based on DGs are being preferred due to having less complexity for the establishment and control. At the same time, they offer higher efficiency and reliability compared to their alternating current (AC) counterparts. This paper proposes a new model predictive control (MPC)-trained artificial neural network (ANN) control strategy being an ANN-MPC instead of conventional cascaded-proportional-integral (PI)-trained ANN control for dynamic damping of photovoltaic (PV)-battery-based grid-connected DC microgrids. Unlike traditional controllers, the proposed control approach more rapidly attains generation-load power balancing under variable climate input (meteorological sensor data) and output (load demand), hence achieving quick DC-bus voltage damping. The proposed ANN-MPC scheme is examined under different operating conditions, and the results are compared with the ANN-based conventional PI controller. The results show the proposed control strategy's efficacy to lessen the instability issues and achieve effective attenuation of oscillations in DC microgrids.

Adaptive Fixed-Time Neural Networks Control for Pure-Feedback Non-Affine Nonlinear Systems with State Constraints.

A new fixed-time adaptive neural network control strategy is designed for pure-feedback non-affine nonlinear systems with state constraints according to the feedback signal of the error system. Based on the adaptive backstepping technology, the Lyapunov function is designed for each subsystem. The neural network is used to identify the unknown parameters of the system in a fixed-time, and the designed control strategy makes the output signal of the system track the expected signal in a fixed-time. Through the stability analysis, it is proved that the tracking error converges in a fixed-time, and the design of the upper bound of the setting time of the error system only needs to modify the parameters and adaptive law of the controlled system controller, which does not depend on the initial conditions.

Adaptive NN Control of Electro-Hydraulic System with Full State Constraints

This paper presents an adaptive neural network (NN) control approach for an electro-hydraulic system. The friction and internal leakage are nonlinear uncertainties, and the states in the considered electro-hydraulic system are fully constrained. In the control design, the NNs are utilized to approximate the nonlinear uncertainties. Then, by constructing barrier Lyapunov functions and based on the adaptive backstepping control design technique, a novel adaptive NN control scheme is formulated. It has been proven that the developed adaptive NN control scheme can sustain the controlled electro-hydraulic system to be stable and make the system output track the desired reference signal. Furthermore, the system states do not surpass the given bounds. The computer simulation results verify the effectiveness of the proposed controller.

Command filtering‐based adaptive neural network control for uncertain switched nonlinear systems using event‐triggered communication

AbstractIn this article, a command filtering‐based adaptive event‐triggered neural network control scheme is proposed for a class of uncertain switched nonlinear systems with unknown control coefficient and input saturation. First, radial basis function neural networks are used as function approximators to estimate unknown nonlinear functions. Then, an event‐triggering mechanism based on the tracking error is introduced to avoid the over‐consumption of communication resources. Furthermore, command filters are employed to solve the problem of complexity explosion that exists in conventional backstepping control design, and the error compensation signals are designed to reduce the errors caused by the filters. Considering that the unknown control gain and input saturation exist in many practical applications, a Nussbaum‐type function is thus introduced into the controller design to address these challenging issues. Finally, stability of the closed‐loop system is strictly proven under a standard Lyapunov stability analysis framework. The effectiveness of the proposed control scheme is illustrated by a simulation example.

A Neural Network NARMA-L2 Tracking Control for Electronic Throttle System*

<div class="section abstract"><div class="htmlview paragraph">In this paper, the Artificial Neural Network (ANN) control strategy based on the Nonlinear Auto Regressive Moving Average-Level 2 (<i>NARMA-L2</i>) technique has been used for tracking control of an electronic throttle body. The <i>NARMA-L2</i> nonlinear plant model is first identified offline by training using a set of input-output data pairs measured at different operating conditions. This data was collected from an actual operation of the throttle body running in a closed-loop control system on a prototype vehicle. The identified <i>NARMA-L2</i> plant model was then inverted and used to force the throttle output position to approximately track any reference inputs with multiple set-point changes at different operating conditions. The <i>NARMA-L2</i> model was reconfigured to be an equivalent model of a feed-forward controller that can cancel not only the actual dynamic behavior of the throttle body but also the nonlinearity effects. This type of controller has great potential to overcome the difficulty of developing an inverse dynamic model of the throttle body [<span class="xref">1</span>] and has the ability to compensate any un-modeled nonlinear dynamic changes that may be excited due to environmental changes. These changes can include load, temperature, humidity, and external disturbances with noise effects that may be introduced into the closed-loop control of the throttle body during real time operation in the vehicle. The resulting closed loop control system used the <i>NARMA-L2</i> architecture that have a multilayer perceptron neural network structure with the dynamic back-propagation algorithm becomes an implicit algebraic model that perfectly tracks any multiple set-point changes at different operating conditions. Testing results from real time implementation are provided to illustrate the new controller performance for tracking controls during actual operation on a prototype vehicle. The identified <i>NARMA-L2</i> neuro-controller shows more robustness and better performs as compared to other controllers developed in previous work [<span class="xref">1</span>-<span class="xref">3</span>].</div><div class="htmlview paragraph">* This work was done while the author was at IAV Automotive Engineering Inc, 15620 Technology Drive, Northville, MI 48168.</div><div class="htmlview paragraph">† Currently working with MAHLE Powertrain LLC, 14900 GalleonCourt, Plymouth, MI 48170.</div></div>

Adaptive neural network control for image‐based visual servoing of robot manipulators

This paper presents a novel adaptive neural network control strategy for image-based visual servoing (IBVS) of robotic manipulators with both eye-in-hand and eye-to-hand camera configurations in the presence of unknown dynamics and external disturbances. The IBVS method is combined with the adaptive neural network to construct the proposed adaptive neural network controller to solve the visual servoing control problem of robots. The adaptive neural network based IBVS controller is designed based on the depth-independent interaction matrix, which can be trained on-line to identify the visual servoing robotic system modeling errors. Moreover, the proposed method can approach the unknown nonlinear dynamics for both eye-in-hand and eye-to-hand camera configurations without requiring the robot dynamics to be linearly parameterizable, and the exact knowledge of the robot structure is not needed. On the basis of the nonlinear robot dynamics, the Lyapunov stability analysis is given to prove the asymptotical convergence of the image position and velocity errors. Simulation results for both camera configurations are provided to demonstrate the performance of the proposed adaptive neural network based approach.

Learning‐based robust control methodologies under information constraints

Learning‐based robust control methodologies under information constraints

Adaptive Neural Network Control of Zero-Speed Vessel Fin Stabilizer Based on Command Filter

This paper proposes a zero-speed vessel fin stabilizer adaptive neural network control strategy based on a command filter for the problem of large-angle rolling motion caused by adverse sea conditions when a vessel is at low speed down to zero. In order to avoid the adverse effects of the high-frequency part of the marine environment on the vessel rolling control system, a command filter is introduced in the design of the controller and a command filter backstepping control method is designed. An auxiliary dynamic system (ADS) is constructed to correct the feedback error caused by input saturation. Considering that the system has unknown internal parameters and unmodeled dynamics, and is affected by unknown disturbances from the outside, the neural network technology and nonlinear disturbance observer are fused in the proposed design, which not only combines the advantages of the two but also overcomes the limitations of the single technique itself. Through Lyapunov theoretical analysis, the stability of the control system is proved. Finally, the simulation results also verify the effectiveness of the control method.

Adaptive Neural Network Control of an Uncertain Nuclear Refueling Machine With Input Backlash and Asymmetric Output Constraint

This paper studies the control problem of an uncertain nuclear refueling machine (RM) system in the presence of uncertainty, external disturbance, input backlash and asymmetric output constraint. Taking into account the effects of external disturbance and input backlash, the RM system with fuel rod is modeled as a copling partial differential equation-ordinary differential equation (PDE-ODE). Using the backstepping method, an adaptive neural network control scheme is designed to drive the RM and bridge to the desired positions and simultaneously reduce the vibration of fuel rod. A novel asymmetric tangent-type barrier Lyapunov function (Tan-BLF) is constructed to restrict the position tracking error into the given range. The formulation of backlash nonlinearity is transformed into the expected input and nonlinear error. Then the combination of input backlash error and boundary disturbance is defined as a disturbance-like item. Applying the robust control strategy and adaptive technique, two auxiliary input signals are proposed to offset the impact of the disturbance-like item. The adaptive neural network (NN) is employed to compensate for the system uncertainty. The practical stability of the RM system with the proposed control is demonstrated via the theoretical Lyapunov analysis. Simulation verifies the performance of the designed control scheme.

Observer-based adaptive neural network control for PEMFC air-feed subsystem

Oxygen excess ratio (OER) tracking of polymer electrolyte membrane fuel cell (PEMFC) air-feed subsystem is an interesting topic to obtain a high energy conversion ratio. The control purpose is challenging due to the unknown nonlinear function, time-varying external disturbance and unmeasured variable. To deal with the above problems, a neural network control scheme based on tracking error observer and disturbance observer is proposed. Firstly, a canonical form of the PEMFC air-feed subsystem is developed based on the input–output feedback linearization method. Meanwhile, a tracking error observer which only uses the system output is developed for the reconstruction of the unmeasured variable. Secondly, the neural network based on a novel adaptive learning law is proposed to approximate the unknown nonlinear function. An identification model with a second-order low-pass filter is designed such that the adaptive learning law is adjusted by the observer error and modeling error, simultaneously. Finally, to reduce the effect caused by compound disturbance including the external disturbance and neural network approximation error, a nonlinear disturbance observer is proposed. The main contribution of this study is not only avoiding the prior knowledge of system uncertainty, external disturbance and the unmeasured variable but also improving the OER tracking performance under the measurement noise. Lyapunov theory analysis shows that the system tracking error is uniformly ultimately bounded. Numerical simulations and hardware-in-loop (HIL) experiments show that the proposed neural network control algorithm can maintain OER at the target value even under external disturbance. The experimental results show that the performance indexes including the mean absolute error (MAE), the root mean square error (RMSE) and the standard deviation (SD) of the proposed controller during the whole process are 0.0213, 0.0547 and 0.0538, respectively. Compared with the proportional–integral–derivative (PID) controller (MAE 0.0466, RMSE 0.0762 and SD 0.0745), robust adaptive radial basis function neural network (RBFNN-H∞) controller (MAE 0.0841, RMSE 0.0839 and SD 0.0813) and hybrid robust adaptive radial basis function neural network (HARBFNN-1st) controller (MAE 0.0255, RMSE 0.0613 and SD 0.0603), the proposed controller performs a better performance.

Robust adaptive neural network control for dynamic positioning of marine vessels with prescribed performance under model uncertainties and input saturation

This paper presents a dedicated robust adaptive neural network control (RANNC) scheme for dynamic positioning (DP) of marine vessels with a prescribed performance under model uncertainties, external disturbances and input saturation. To guarantee that the transient performance is always within the prescribed performance constraints, a novel error transfer function is firstly proposed to transfer the original dynamic positioning system with the constrained error behavior into an equivalent unconstrained one. Next, the RANNC scheme is developed with the backstepping technique, and the method of command filter is introduced to avoid the ”dimension disaster” problem. Considering the ship’s inertial matrix and damping matrix are not constant in practice, the adaptive control technique and RBF neural networks are adopted to design the model adaptive controller, which only requires the actuator matrix rather than a completely accurate model. Moreover, minimal learning parameter technique is introduced to minimize the computational burden triggered by weight update of neural networks. Finally, comparison simulation results are provided to illustrate the performance of the proposed RANNC scheme.

Direct yaw-moment control of vehicles based on phase plane analysis

In view of the problems related to vehicle-handling stability and the real-time correction of the heading direction, nonlinear analysis of a vehicle steering system was carried out based on phase plane theory. Subsequently, direct yaw-moment control (DYC) of the vehicle was performed. A four-wheel, seven-degree-of-freedom nonlinear dynamic model that included the nonlinear characteristics of the tire was established. The stable and unstable regions of the vehicle phase plane were divided, and the stable boundary model was established by analyzing the side slip angle–yaw rate ([Formula: see text]) and side slip angle–side slip angle rate [Formula: see text] phase planes as functions of the vehicle state variables. In the unstable region of the phase plane, taking the instability degree as the control target, a fuzzy neural network control strategy was utilized to determine the additional yawing moment of the vehicle required for stability restoration, which pulled the vehicle back from an unstable state to the stable region. In the stable region of the phase plane, a fuzzy control strategy was utilized to determine the additional yawing moment so that the actual state variables followed the ideal state variables. In this way, the vehicle responded rapidly and accurately to the steering motion of the driver. A simulation platform was established in MATLAB/Simulink and three working condition was tested, that is, step, sine with dwell, and sine amplification signals. The results showed that the vehicle handling stability and the instantaneous heading-direction adjustment ability were both improved due to the control strategy.

Event-triggered adaptive NN tracking control with dynamic gain for a class of unknown nonlinear systems

An event-triggered tracking control problem is investigated for a class of unknown nonlinear systems in this paper. First, to approximate the unknown function, the radial basis function neural network is used. Then, we propose an event-triggered adaptive neural network control scheme with dynamic gain. Both the event-triggered mechanism in the controller-to-actuator channel and the dynamic gain in the sensor-to-controller channel are considered to reduce the communication load between the controller and the actuator and in the meantime to alleviate the burden of parameter adjustment as well. This scheme makes full use of the advantage that the dynamic gain is driven by the tracking error to ensure that the system output signal can track the reference signal within a prespecified accuracy without adjusting the parameters in use. Moreover, a detailed theoretical proof is given to illustrate that Zeno behavior can be excluded under the designed event-triggered controller. Finally, a simulation example is provided to show the effectiveness of our control scheme.

Adaptive neural control for uncertain switched nonlinear systems with a switched filter-contained hysteretic quantizer

This paper demonstrates an adaptive neural control strategy for the uncertain nonlinear switched systems with dwell time. The unknown nonlinear functions, hysteresis nonlinear and external disturbances are taken into account in this system. A filter-contained hysteretic quantizer is introduced to filter out the high frequency components, leading to a durable solution for the conflict between hysteresis actuator and quantization, that is the hysteresis loop deforms when the input signal goes beyond the boundary. In addition, the adaptive neural network control strategy is developed using backstepping techniques to eliminate external disturbances and unknown nonlinear function. This strategy can guarantee that the tracking error converges to an adjustable field near zero.

Evolving Intelligent System for Trajectory Tracking of Unmanned Aerial Vehicles

This article develops an evolving type-2 quantum fuzzy neural network (eT2QFNN) control scheme for achieving trajectory tracking with unmanned aerial vehicles (UAVs). The proposed approach involves quantum membership functions, automatic rule growing process, and parameter adjustment learning scenario to deal with the problems of inadequacy, uncertainties, and noise in conventional control techniques. Furthermore, the proposed approach is operated in a parallel structure with the proportional derivative (PD) controller to compensate the transients in performance and learn the dynamic characteristics of the system. Besides, a sliding theory-based adaptive law is equipped with the control approach to compensate for the nonlinearity of the UAV. To assess the performance, numerical simulations and real-time experiments are carried for pitch and yaw axes control of two degrees of freedom (2DoF) helicopter test rig with the proposed approach. The simulations and experiments are aimed at achieving an offline path tracking with an objective to minimize the deviation error and improve the time response characteristics of the UAVs. The results depict the robustness of the proposed approach in terms of integral time absolute error for a helicopter following various trajectories. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article addresses the problem of trajectory tracking and attitude control in a two-rotor UAV system. In practical application, there are multiple end users for an efficiently controlled UAV system. Generally, the trajectory tracking and attitude control are associated with the capability of UAVs to perform vertical take-off, landing, maneuver, and cyclic rotation as per the change in path. Furthermore, the control of UAVs for trajectory tracking and attitude provides a unified framework in efficiently following the desired path and maintaining the desired attitude. This has potential applications in the field of search and rescue operations, surveillance, military applications, environmental exploration, and aerial cinematography. Although trajectory tracking and attitude control problems have been studied a lot, the drawbacks due to uncertainties, immediate response to trajectory changes, and attitude settling are still open and challenging. This article proposed an eT2QFNN for a two rotor of UAV system with PD controller using automatic rule growing process. Sufficient trajectories are developed to make the UAV follow them under the proposed approach. Both simulation and real-time experiments were conducted and the results of the developed controller are compared with conventional approaches.

Neural-networks and event-based fault-tolerant control for spacecraft attitude stabilization

This paper proposes a neural network and event-based fault-tolerant control scheme for spacecraft attitude stabilization in the presence of lumped disturbances, which consists of space disturbances, inertia uncertainties, and actuator faults. A neuro-adaptive estimator is employed to approximate the lumped disturbances, with the help of its powerful adaptive estimation capability of approximating any unknown smooth nonlinear function with arbitrary accuracy. The estimation is then utilized to formulate an integrated event-based dual-channel control scheme that can both guarantee the system's convergence and ensure the event triggering sequence possessing no-Zeno behavior simultaneously. The proposed control scheme provides a new and straightforward way for spacecraft attitude control to deal with lumped disturbances while requiring a low actuator updating frequency, thus saves on-board communication resources. Numerical simulations show the effectiveness of the algorithms.

Adaptive Neural Network Fixed-Time Control Design for Bilateral Teleoperation With Time Delay.

In this article, subject to time-varying delay and uncertainties in dynamics, we propose a novel adaptive fixed-time control strategy for a class of nonlinear bilateral teleoperation systems. First, an adaptive control scheme is applied to estimate the upper bound of delay, which can resolve the predicament that delay has significant impacts on the stability of bilateral teleoperation systems. Then, radial basis function neural networks (RBFNNs) are utilized for estimating uncertainties in bilateral teleoperation systems, including dynamics, operator, and environmental models. Novel adaptation laws are introduced to address systems' uncertainties in the fixed-time convergence settings. Next, a novel adaptive fixed-time neural network control scheme is proposed. Based on the Lyapunov stability theory, the bilateral teleoperation systems are proved to be stable in fixed time. Finally, simulations and experiments are presented to verify the validity of the control algorithm.

A Novel PID Control Strategy Based on Improved GA-BP Neural Network for Phase-Shifted Full-Bridge Current-Doubler Synchronous Rectifying Converter

In this paper, a phase-shifted full-bridge current-doubler synchronous rectifying converter (PSFB-CDSRC) based on IGBT and its control strategies are studied. In the main circuit, a current doubling synchronous rectifying circuit based on IGBT is presented to further reduce the power loss of power devices. Moreover, in the control strategy, in view of the existing researches, the basic BP neural network PID control performance of the rectifying converter still can be further improved. Therefore, this paper combines the quasi-Newton algorithm and traditional GA to propose an improved GA-BP (IGA-BP) neural network to further improve PID control performance. The simulation results demonstrate that the maximum efficiency of 5 V/500 A rectifying converter based on the proposed circuit scheme can reach 94.1%, and the rectifying converter has a good performance of excellent waveform and wide range of load. IGA-BP neural network PID control responds fast and reaches the stable state quickly in comparison with that controlled by the GA-BP neural network control strategy, and the steady-state time can be reduced by 10.5% through using IGA-BP neural network control strategy. This study can provide a valuable guidance and reference, not only in circuit scheme but also in the optimal PID control strategy for design of the high-efficiency DC/DC rectifying converter with higher power in the future.