Serial-parallel Estimation Model Research Articles

Predefined-Time Hierarchical Coordinated Neural Control for Hypersonic Reentry Vehicle.

This paper investigates the predefined-time hierarchical coordinated adaptive control on the hypersonic reentry vehicle in presence of low actuator efficiency. In order to compensate for the deficiency of rudder deflection in advantage of channel coupling, the hierarchical design is proposed for coordination of the elevator deflection and aileron deflection. Under the control scheme, the equivalent control law and switching control law are constructed with the predefined-time technology. For the dynamics uncertainty approximation, the composite learning using the tracking error and the prediction error is constructed by designing the serial-parallel estimation model. The closed-loop system stability is analyzed via the Lyapunov approach and the tracking errors are guaranteed to be uniformly ultimately bounded in a predefined time. The tracking performance and the learning accuracy of the proposed algorithm are verified via simulation tests.

Robust Adaptive Learning Control of Space Robot for Target Capturing Using Neural Network.

This article investigates the robust adaptive learning control for space robots with target capturing. Based on the momentum conservation theory, the impact dynamics is constructed to derive the relationship of generalized velocity in the pre-impact and post-impact phase. Considering the nonlinear dynamics with contact impact, the robust control using nonsingular terminal sliding mode (NTSM) and fast NTSM is designed to achieve the fast realization of the desired states. Furthermore, for the unknown dynamics of the combination system after capturing a target, the adaptive learning control is developed based on neural network and disturbance observer. Through the serial-parallel estimation model, the prediction error is constructed for the update of adaptive law. The system signals involved in the Lyapunov function are proved to be bounded and the sliding mode surface converges in finite time. Simulation studies present the desired tracking and learning performance.

Adaptive humidity and temperature nonlinear control for uncertain air-handling units

An adaptive temperature and humidity nonlinear control method is introduced to deal with unknown humidity source intensity and heat load for an air-handling unit (AHU). The serial-parallel estimation model is used to prevent the offset of the equilibrium point. By comparing with the linearization technique and fuzzy logics, an adaptive nonlinear controller is designed directly, which avoids the linearization of the system model and collapse of the calculation. With the stability analysis and simulation results comparison, it is verified that the control precision of the designed controller is higher and the tracking performance is better.

Event-trigger-based composite adaptive fuzzy control for nonlinear time-varying state constraint systems with asymmetric input saturation

In this paper, a novel composite adaptive fuzzy control (AFC) based on the command filtered backstepping and event-triggered mechanism is proposed for nonstrict-feedback nonlinear systems with input saturation and time-varying full-state constraints. A fuzzy observer is designed to estimate the unmeasured states. Simultaneously, by introducing a serial-parallel estimation model, a fuzzy control tactic with the composite adaptive law of parameters is established which greatly increases the precision of unknown term estimation. In addition, the command filtering technique is employed to solve the issue of “explosion of complexity” and compensate for the errors caused by the command filters. The event-triggered mechanism is utilized to effectively decrease the data redundancy. By using the time-varying barrier Lyapunov functions (BLFs), all the states can remain within a prescribed time-varying interval. The designed controller can guarantee the convergence of the tracking error at the origin and the boundedness of all closed-loop signals. Finally, two simulation examples are carried out to validate the effectiveness of the proposed control scheme.

Robust Adaptive Composite Learning Integrated Guidance and Control for Skid-to-Turn Interceptors Subjected to Multiple Uncertainties and Constraints

This paper investigates a novel robust adaptive dynamic surface control scheme based on the barrier Lyapunov function (BLF), online composite learning, disturbance observer, and improved saturation function. It is mainly designed for a class of skid-to-turn (STT) interceptor integrated guidance and control (IGC) design problems under multi-source uncertainties, state constraints, and input saturation. The serial-parallel estimation model used in this study estimates the system states and provides “critic” information for the neural network and disturbance observer; then, these three are combined to realize online composite learning of the multiple uncertainties of the system and improve the interception accuracy. In addition, the state and input constraints are resolved by adopting the BLF and the improved saturation function, while the design of the auxiliary system ensures stability. Finally, a series of simulation results show that the proposed IGC scheme with a direct-hit intercept strategy achieves a satisfactory effect, demonstrating the validity and robustness of the scheme.

Observer-Based Dynamic Event-Triggered Control for Multiagent Systems With Time-Varying Delay

This article is concerned with the dynamic event-triggered-based adaptive output-feedback tracking control problem of nonlinear multiagent systems with time-varying input delay. By utilizing the approximation capability of neural network (NN), a low-gain nonlinear observer is first established to estimate the immeasurable states. To mitigate the effect of time-varying input delay, an auxiliary system with communication information is designed to generate the compensation signals. Then, a distributed adaptive composite NN dynamic surface control (DSC) strategy is proposed to acquire the satisfactory tracking accuracy, where the filter errors are compensated by the introduced serial-parallel estimation model. Moreover, an effective switching dynamic event-triggered mechanism is developed to determine the communication instants and reduce the update frequency of the controller. It is proven that the consensus tracking error converges to a residual set of the origin. Finally, simulation results are presented to demonstrate the effectiveness of the proposed composite NN DSC scheme.

Dynamic event-triggered control for MSVs via composite-learning-based adaptive neural approach

This article investigates the control issue of marine surface vehicles (MSVs) subject to uncertainties and input saturation. To ensure the smooth implementation of backstepping design framework, the non-smooth saturation model is replaced by a smooth function, which is analogous to the hyperbolic tangent function. To exact reconstruct the uncertainties including unknown internal nonlinear dynamic and external disturbance, an idea of separation reconstruction is proposed. That is, the adaptive neural network (NN) is utilized to online reconstruct the unknown internal nonlinear dynamic, and a NN-based disturbance observer is proposed to timely recover the external disturbance plus the reconstruction error of adaptive NN. To enhance the control accuracy, a serial-parallel estimation model (SPEM) is involved to capture the prediction error of MSVs’ velocity. Based on this, a novel adaptive law used to update the NN weight is designed by involving the velocity error, prediction error and estimation value of disturbance. Furthermore, a composite-learning-based adaptive neural control (CLBANC) law is designed. To decrease the physical wear of actuator, a dynamic event-triggered mechanism is established between the control law and actuator. Finally, a dynamic event-triggered (DET) CLBANC (DET-CLBANC) scheme is developed for MSVs. Theoretical analysis indicates that the proposed control scheme is endowed with the ability to ensure the boundedness of all signals in the whole closed-loop system of MSVs. Moreover, the effectiveness of DET-CLBANC scheme is verified by simulation and comparison.

Dynamic Event-Triggered Adaptive Neural Output Feedback Control for MSVs Using Composite Learning

This paper investigates the control issue of marine surface vehicles (MSVs) subject to internal and external uncertainties without velocity information. Utilizing the specific advantages of adaptive neural network and disturbance observer, a classification reconstruction idea is developed. Based on this idea, a novel adaptive neural-based state observer with disturbance observer is proposed to recover the unmeasurable velocity. Under the vector-backstepping design framework, the classification reconstruction idea and adaptive neural-based state observer are used to resolve the control design issue for MSVs. To improve the control performance, the serial-parallel estimation model is introduced to obtain a prediction error, and then a composite learning law is designed by embedding the prediction error and estimate of lumped disturbance. To reduce the mechanical wear of actuator, a dynamic event triggering protocol is established between the control law and actuator. Finally, a new dynamic event-triggered composite learning adaptive neural output feedback control solution is developed. Employing the Lyapunov stability theory, it is strictly proved that all signals in the closed-loop control system of MSVs are bounded. Simulation and comparison results validate the effectiveness of control solution.

Adaptive neural control for coordinated rolling motion of vessels with zero speed via composite learning

An intelligent control method under the backstepping and dynamic surface control (DSC) design framework is proposed in this article. The proposed method is aiming at solving the problem of coordinated rolling motion control of the supply and receiving vessels with internal dynamic uncertainty, unknown external nonlinear disturbance, input saturation, and unmeasurable rolling angular velocity. In the control design, a neural-based state observer is constructed to generate the unmeasurable rolling angular velocity, which achieves a separate design from the control law. To compensate the input saturation effect, a second-order auxiliary dynamic system (ADS) is designed, and its states are inset into the transformation of coordinates required by backstepping. To realize the accurate reconstruction of the neural network for the dynamic uncertainty, the adaptive neural and nonlinear disturbance observer (NDO) are used to reconstruct the internal dynamic uncertainty and unknown external disturbance, respectively. And then the serial–parallel estimation model is introduced to establish a predictor of the system state, and a composite adaptive learning law is designed, which includes the velocity error and predictor error. As a result, the idea of classification reconstruction is developed. Finally, an adaptive neural composite learning coordinated rolling motion control solution is developed. Lyapunov stability analysis shows that all signals in the closed-loop control system are bounded. The effectiveness of the control solution is verified by the simulations.

Composite neural learning event-triggered control for dynamic positioning vehicles with the fault compensation mechanism

This paper focus on the novel composite neural learning event-triggered control for dynamic positioning vehicles with the fault compensation mechanism. In the proposed algorithm, the system uncertainties are tackled with by incorporating the dynamic surface control (DSC) and the robust neural damping techniques. Besides, the serial–parallel estimation model (SPEM) is developed to produce the prediction errors, which could be employed to derive the corresponding composite adaptive law. And the unknown actuator faults and gain uncertainties are effectively compensated for merits of the composite intelligent learning method. Furthermore, the idea of relative threshold strategy is utilized to construct the event-triggered mechanism. The event-triggered input could reduce the communication burden in the channel from controller to actuators. Through the direct Lyapunov theory, the parameters setting can be derived to guarantee the semi-global uniformly ultimately bounded (SGUUB) stability of all error signals in the closed-loop system. Finally, the effectiveness of the proposed algorithm is validated through the simulation experiments.

Robust Composite Dynamic Event-Triggered Control for Multiple USVs with DLLOS Guidance

In this paper, a robust composite dynamic event-triggered formation control scheme is proposed for multiple underactuated surface vehicles (USVs) from two aspects, i.e., guidance and control. In the guidance module, a novel dual-layer line-of-sight (DLLOS) guidance principle is incorporated into the leader–follower framework to generate the reference path. To overcome the problem of unavailable leader velocity information, an adaptive speed controller is designed to adjust the navigational speed of followers. As for the control part, by utilizing the dynamic event-triggered method, the operational frequency of actuators can be reduced in a flexible manner. That can effectively avoid the excessive wear and chattering phenomenon of actuators. Furthermore, by the fusing of the radial basis function neural networks (RBF NNs) and the robust neural damping technique, the model uncertainty, environmental disturbances and some unknown parameters can be remodeled, and only two gain-related adaptive laws need to be updated online. The serial–parallel estimation model (SPEM) is established to predict the velocity variables, and the approximation performance of NNs can be enhanced by virtue of the derived prediction error. Through the Lyapunov stable theorem, all control signals in the closed-loop system are guaranteed semi-globally uniformly ultimately bounded (SGUUB) stability. Finally, digital simulations are illustrated to verify the effectiveness and superiority of the proposed algorithm.

Composite learning tracking control for underactuated marine surface vessels with output constraints.

In this paper, a composite learning control scheme was proposed for underactuated marine surface vessels (MSVs) subject to unknown dynamics, time-varying external disturbances and output constraints. Based on the line-of-sight (LOS) approach, the underactuation problem of the MSVs was addressed. To deal with the problem of output constraint, the barrier Lyapunov function-based method was utilized to ensure that the output error will never violate the constraint. The composite neural networks (NNs) are employed to approximate unknown dynamics. The prediction errors can be obtained using the serial-parallel estimation model (SPEM). Both the prediction errors and the tracking errors were employed to construct the NN weight updating. Using approximation information, the disturbance observers were designed to estimate unknown time-varying disturbances. The stability analysis via the Lyapunov approach indicates that all signals of unmanned marine surface vessels are uniformly ultimate boundedness. The simulation results verify the effectiveness of the proposed control scheme.

Event-Triggered Composite Learning Finite-Time Trajectory Tracking Control for Underactuated MSVs Subject to Uncertainties

In this paper, a novel event-triggered composite learning finite-time control scheme is presented for underactuated marine surface vehicles (MSVs) trajectory tracking under unknown dynamics and unknown time-varying disturbances. Line-of-sight (LOS) tracking control method is employed to address the underactuation problem of MSVs. The neural networks (NNs) are untilized to approximate unknown dynamics. The serial-parallel estimation model is employed to construct the prediction error, and the prediction errors and tracking errors are fused with construct the NN weights updating. Combining the result of approximation information, the disturbances observers can be created to achieve disturbance estimation. Fractional power technology is artistically introduced to realize the finite-time trajectory tracking control of MSVs based on composite learning. The proposed control scheme ensures the simultaneous realization of high precision tracking performance and unknown information approximation. Moreover, an event-triggered mechanism is introduced to reduce the transmission load and the execution rate of actuators. It is proved that the proposed control scheme ensures all error signals of the MSVs trajectory tracking control system can converge to the neighborhood of zero within a finite time. Finally, the simulation results on an MSV verify the effectiveness and superiority of the proposed control scheme.

DVSL guidance-based composite neural path following control for underactuated cable-laying vessels using event-triggered inputs

In this note, the path following issue of underactuated cable-laying vessels is addressed from two aspects, i.e., guidance and control. By exploiting the hierarchical design, a so-called dual virtual ships layered (DVSL) guidance algorithm is proposed with two reference path generators. In the first guidance layer, the logical virtual ship (LVS) is used to programme the setpoints-based reference path for cables. Based on the cable-related geometrical derivation, the corresponding ship’s reference path can be obtained in the second layer, and its smoothness can be guaranteed by the adaptive virtual ship (AVS). For the control part, a novel composite neural event-triggered control algorithm is developed by taking the main engine revolution and the rudder as actual control inputs, which are characterized by the practical measurability and the aperiodic update. The radial basis function neural networks (RBF-NNs) are employed to deal with the model uncertainty, and the approximation performance is improved with the aid of the serial-parallel estimation model (SPEM). By fusing the robust neural damping technique, only two adaptive parameters require to be updated, which leads to a simpler controller with less computation burden. Besides, the inputs-dominated event-triggered mechanism is introduced to release the communication traffic in the controller-to-actuator channel. Through the Lyapunov stability analysis, all signals in the closed-loop system are guaranteed to be semi-global uniformly ultimately bounded (SGUUB), and the existence of minimum inter-event time is proved. Finally, three simulation experiments are conducted to illustrate the effectiveness and the superiority of proposed algorithm.

Improved composite adaptive fault‐tolerant control for dynamic positioning vehicle subject to the dead‐zone nonlinearity

In order to tackle the marine practical constraints, for example the actuator faults, the dead-zone input, an improved composite adaptive neural control algorithm is proposed for dynamic positioning vehicles in presence of the unknown external disturbances. In the algorithm, the robust neural damping technique is employed to remodel the system model uncertainty and suppress the external interference. As for the dead-zone input, the dead-zone inverse model is constructed to derive the corresponding compensating terms. That could effectively release the constraints from the actuator faults and the dead-zone non-linearity. Furthermore, for merits of the composite intelligent learning method, one designs the serial-parallel estimation model to estimate the related velocity variables. The corresponding prediction error could be applied in the design of adaptive law. That could effectively improve the accuracy of parameter estimation and facilitate the robustness of the closed-loop system. The semi-global uniformly ultimately bounded stability is guaranteed for all error signals in the closed-loop system by utilizing the Lyapunov theory. Finally, the validity of the proposed algorithm is demonstrated through the simulation experiments.

Composite adaptive fuzzy decentralized tracking control for pure-feedback interconnected large-scale nonlinear systems

This paper focuses on a problem of composite adaptive fuzzy decentralized tracking control for a class of uncertain pure-feedback interconnected large-scale nonlinear systems with unmeasurable states. A fuzzy state observer is designed by using fuzzy logic systems; thus, the unmeasurable states of pure-feedback nonlinear systems are estimated based on the designed fuzzy state observer. A serial–parallel estimation model is designed by using the fuzzy state observer. To avoid the analytic computation, the command filters are employed to produce the command signals and their derivatives. The fuzzy adaptive laws are constructed by using prediction errors and compensating tracking errors. Finally, a controller is constructed by dynamic surface control technique. Through the proposed control method, all the signals in the closed-loop system are bounded, and the output of the system can track the given reference signal. The simulation studies verify the effectiveness of the proposed control method in this paper.

Compound learning tracking control of a switched fully-submerged hydrofoil craft

This paper addresses the tracking control of a fully-submerged hydrofoil craft (FSHC) on the horizontal plane. The motions of a FSHC can be classified into three cruising conditions, namely the hulborne, the taking off, and the foilborne. Considering that the FSHC has the different model dynamics and disturbances between the three cruising conditions, we treat the FSHC model as a switched system between the hullborne and the foliborne conditions, in which the taking off condition is conveniently omitted and deemed as the fast transient. The compound learning technique is introduced to offset the uncertainties, and it is comprised of two parts. First, the fuzzy logic system (FLS) is employed to approximate the unknown model dynamics in each degree of freedom. Based on a serial-parallel estimation model (SPEM), the composite adaptive laws are fabricated to estimate the weights of the FLS by combining the tracking errors with the prediction errors between the estimation model and the dynamic loop of the FSHC. Second, the disturbance observer (DOB) is fabricated to estimate the compound disturbance, which is comprised of the approximation error of the FLS and the disturbance. Based on the compound learning, we develop the switched adaptive backstepping control laws of the FSHC. By employing the average dwell-time method, all the tracking and the estimation errors in the switched system are proved to be semi-globally uniformly ultimately bounded (SGUUB). Finally, the numerical experiment is conducted to verify the effectiveness of the proposed scheme.

Efficient Learning Control of Uncertain Fractional-Order Chaotic Systems With Disturbance.

In this brief, the problem of synchronization control is investigated for a class of fractional-order chaotic systems with unknown dynamics and disturbance. The controller is constructed using neural approximation and disturbance estimation where the system uncertainty is modeled by neural network (NN) and the time-varying disturbance is handled using disturbance observer (DOB). To evaluate the estimation performance quantitatively, the serial-parallel estimation model is constructed based on the compound uncertainty estimation derived from NN and DOB. Then, the prediction error is constructed and employed to design the composite fractional-order updating law. The boundedness of the system signals is analyzed. The simulation results show that the proposed new design scheme can achieve higher synchronization accuracy and better estimation performance.

Disturbance observer-based composite neural learning path following control of underactuated ships subject to input saturation

This paper investigates the constrained waypoints-based path following control problem of underactuated ships in the presence of the actuator saturation and the unknown disturbance. An improved composite neural learning control algorithm is proposed by using the command filter and the robust neural damping techniques. In the proposed algorithm, the dynamic auxiliary system is established to generate the saturation error compensating (SEC) signal, which is used to modify the error dynamics such that the actuator saturation constraint is tackled. The neural networks are employed to deal with the model uncertainty, and the corresponding compensating effects are improved further by designing the simplified serial-parallel estimation model (SPEM). By constructing the robust neural damping term, only two adaptive parameters are required to be updated online. That leads to a smaller computational application burden. Furthermore, the composite disturbance observer (CDOB) is developed by fusion of the prediction error and the compensated tracking one, where the unknown disturbance can be estimated accurately and compensated effectively. In addition, considerable efforts are made to obtain the semi-global uniformly ultimately bounded (SGUUB) stability of the closed-loop system. The convictive experiments are performed to verify the effectiveness and superiority of the proposed algorithm.

Composite Neural Learning Fault-Tolerant Control for Underactuated Vehicles With Event-Triggered Input.

This article presents a novel composite neural learning fault-tolerant algorithm to implement the path-following activity of underactuated vehicles with event-triggered input. With the input event-triggered mechanism, the dominant superiority is to reduce the communication burden in the channel from the controller to actuators. In the proposed scheme, the system uncertainties are dealt with in the fusion of the neural networks (NNs) and the dynamic surface control (DSC) method. The serial-parallel estimation model (SPEM) is constructed to estimate the error dynamics, where the derived prediction error could improve the compensation effect of the NNs. As for the gain uncertainties and the unknown actuator faults, four adaptive parameters are designed to stabilize the related perturbation and not be affected by the triggering instants. Based on the direct Lyapunov theorem, considerable efforts have been made to guarantee the semiglobal uniformly ultimately bounded (SGUUB) stability of the closed-loop system. Finally, comparison and practical experiments are illustrated to verify the superiority of the proposed algorithm.