Computational Explosion Research Articles

Fast fixed-time extended state observer based command filtering backstepping control of free-flying flexible-joint space robots

System parameter perturbations, external disturbances, and input saturation issues seriously affect the on-orbit operation precision and rapid response capabilities of the free-flying flexible-joint space robots (FFSR). To this end, a fixed-time extended state observer (ESO) based non-singular command filtering backstepping controller is proposed. The fast fixed-time ESO is designed to estimate the parameter perturbations and external disturbances. To avoid the “computational explosion” caused by multiple derivatives of the virtual control law in backstepping control, a second-order command filter is employed to obtain the derivative of virtual control laws. Meanwhile, to reduce the filtering errors induced by the command filter, a non-singular fixed-time filtering error compensation algorithm is developed. Furthermore, considering the constraints of control moment gyroscope (CMG) and joint motors, an anti-saturation compensation algorithm is introduced to drive the system out of the saturated region rapidly, thus reducing actuator saturation effects on the control system performance. Theoretical analysis shows the proposed controller can ensure the system tracking error converges to any arbitrarily small neighborhood of the origin within fixed time. Simulation results demonstrate that the designed non-singular fixed-time command filtering backstepping controller exhibits fast convergence speed and high tracking accuracy.

Dynamics of fuel-lean and stoichiometric methane-air explosion venting: Experiments and simulations

The computational explosion venting analyzer (EVA), a zero-dimensional (0D) model based on the conservation of mass and energy, is being developed to model centrally- and rear-ignited explosions. For this purpose, a series of explosion venting experiments in a cylinder with vent areas of 132.7, 86.6, 67.9 cm2 (corresponding to the vent area ratios of K=0.47,0.31,0.24, respectively) is performed. Two equivalence ratios of ϕ=0.8 and 1 are considered to represent the fuel-lean and stoichiometric methane-air mixtures, respectively. The dynamics of explosions is studied through the observation of flame propagation and pressure measurements. In rear ignition experiments, laminar, so-called “finger flame” propagation is observed, while in the case of center ignition, a flame initially expands spherically and then is pulled by the vent, acquiring a half-elliptical and half-spherical shape. The peak pressures obtained from rear ignition exceed their counterparts in the center ignition experiments. The EVA is compared with the experimental matrix. No turbulence is implemented in stoichiometric simulations, and slight turbulence has been accounted for in the lean mixture simulations. It is found that the large vent, generally, imposes more disturbances on the flame shape and the fuel-lean mixtures are more prone to the diffusional-thermal instabilities. It is shown that such a simple numerical tool, as the EVA is, can estimate a complicated problem such as pressure evolution resulted from a vented gas explosion.

Adaptive output feedback control of stochastic systems with mismatched uncertainties input–output quantization

Inspired by the fact that remote control systems always demand to be quantified, and considering that there are always stochastic disturbances in the actual situation, this study discusses a sort of systems with unknown constants due to the existence of parameters that are untoward to measure. Therefore, in this research, a sort of stochastic nonlinear systems with input–output quantization mismatched uncertainties is studied by utilizing backstepping method. The difficulty is that the output signal becomes non-differential after being quantized. In addition, only the quantized output signal in the system can be regarded as known during the analysis. Therefore, the update of unknown parameters and the design of controller will grow arduous. In order to solve the above dilemma, a state observer and a projection operator are introduced. Moreover, in the design of the controller, the power form of Lyapunov function and differential method is employed to avoid the derivation of non-differentiable functions. Since the controller proposed in this study contains a small number of parameters that need to be designed, it is practical and can eliminate the problem of computational explosion. Simulation results demonstrate the effectiveness of the proposed scheme.

Event-Triggered Adaptive Fuzzy Neural Network Output Feedback Control for Constrained Stochastic Nonlinear Systems.

This article investigates the problem of command-filtered event-triggered adaptive fuzzy neural network (FNN) output feedback control for stochastic nonlinear systems (SNSs) with time-varying asymmetric constraints and input saturation. By constructing quartic asymmetric time-varying barrier Lyapunov functions (TVBLFs), all the state variables are not to transgress the prescribed dynamic constraints. The command-filtered backstepping method and the error compensation mechanism are combined to eliminate the issue of "computational explosion" and compensate the filtering errors. An FNN observer is developed to estimate the unmeasured states. The event-triggered mechanism is introduced to improve the efficiency in resource utilization. It is shown that the tracking error can converge to a small neighborhood of the origin, and all signals in the closed-loop systems are bounded. Finally, a physical example is used to verify the feasibility of the theoretical results.

Prescribed-time event-triggered fault-tolerant formation control of multiple UAVs under tracking error constraints

This paper studies an event-triggered fault-tolerant tracking control strategy design of multiple quadrotor UAVs (MQUAVs) formation subject to the composite influence of external disturbance, tracking error constraints and actuator faults. A prescribed time prescribed performance function is employed to solve tracking error constraints and it ensures the tracking error enters into the constraint boundary in the prescribed time. A first-order filter is designed to solve the problem of computational explosion caused by reference trajectory and unknown external disturbance. The adaptive method is used to deal with the unknown parameters and the comprehensive uncertainty which contains actuator faults and external disturbance. Furthermore, the event-triggered controller is designed to achieve the formation tracking with the reduction of update number of controller to save resources. The bounded stability of the tracking error is proved via the Lyapunov theory. At last, the effectiveness of the control strategy is verified by simulation.

An almost disturbance decoupling temperature and humidity control strategy of air-handling units

In the article, for air-handling units (AHUs), an almost disturbance decoupling temperature and humidity tracking controller is constructed directly based on dynamic nonlinear models by backstepping. Compared with model-free techniques such as proportion integration differentiation (PID) and so on, the proposed scheme uses the known information effectively in the model to ensure control accuracy. Different from linearization methods, the almost disturbance decoupling method is employed to attenuate the impact of disturbances. Without fuzzy logics, the proposed controller requires less computation since the exponential explosion of computation caused by the increase of fuzzy rules can be avoided. To show the practicability, the outdoor temperature and humidity, strength of humidity source, and heat load are considered to be time-varying. Finally, compared with PID, feedback linearization and fuzzy controllers, the effectiveness and superiority of the constructed controller are proved.

Fixed-time velocity-free safe formation control of AUVs with actuator saturation and unknown disturbances

This paper designs a fixed-time formation control law for multiple autonomous underwater vehicle (AUV) system (MAS) subjected to unmeasurable velocities, actuator saturation and time-varying disturbances. To address these challenges, the actuator saturation and time-varying disturbances are initially integrated into lumped disturbances, the initial step is to integrate actuator saturation and time-varying disturbances into lumped disturbances, followed by designing a fixed-time extended state observer (FTESO) to estimate the unavailable velocities and the lumped disturbances in fixed time. Moreover, a nonlinear filter is also utilized to eliminate the problem of computational explosion that arises due to derivatives in the controller design process. Additionally, a tan-type barrier Lyapunov function (TT-BLF) is presented to constrain the tracking errors within a predetermined time-varying constraint function. Then, a novel fixed-time formation control algorithm based on FTESO and TT-BLF is further designed to ensure that the formation tracking errors converge to a small neighborhood near origin in fixed time, and the fast formation task can be completed within a fixed time. Finally, the simulation experiments verify the effectiveness of the proposed controller.

Command filter-based adaptive fuzzy integral backstepping control for quadrotor UAV with input saturation

This paper presents an adaptive fuzzy integral backstepping control strategy combined with command filter to solve trajectory tracking problem of quadrotor unmanned aerial vehicle (UAV). By using Fuzzy logic system to approximate uncertain disturbance. The tracking error integral term is introduced to reduce the steady-state tracking error. The backstepping approach is combined with the command filter to solve the “computational explosion” problem, and the error compensation mechanism is introduced to reduce the influence of filtering error. The hyperbolic tangent function is used to solve the problem of input saturation. By choosing the appropriate Lyapunov function to calculate the adaptive law for tuning the adjustable parameters of the fuzzy logic system. Morever, the stability of the controller is analyzed by using Lyapunov stability theory. Numerical simulation results are provided to illustrate the effectiveness and superiority of the proposed control method.

Prescribed performance dynamic surface fuzzy control for strict‐feedback nonlinear system with actuator fault

In this paper, an adaptive fuzzy control scheme for strict-feedback nonlinear system with finite-time prescribed performance and actuator fault is studied. First, we consider a prescribed performance function that enables the tracking error to converge within a preset interval in a finite time. Subsequently, the fuzzy logic system and dynamic surface control technology are embedded in the backstepping design process, and the purpose of the dynamic surface control technique is to settle the problem of computational explosion when processing the backstepping design. The fuzzy logic system is used to approximate the unknown functions that appear in the system. Finally, a new controller is designed in combination with an updated law which ensures that all the signals in the closed-loop is bounded. Two simulation examples are taken to illustrate the feasibility of the presented control scheme.

An Inconsistency Fault Diagnosis Method for Lithium-Ion Cells in the Battery Pack Based on Piecewise Dimensionality Reduction and Outlier Identification

Abstract The inconsistency of cells in the battery pack is one of the main causes of battery failure. In practical applications, the terminal voltage is an important parameter that is easy to obtain and can characterize the inconsistency of cells. In this paper, a fault diagnosis method based on piecewise dimensionality reduction and outlier identification is proposed according to the voltage inconsistency of cells in the battery pack. This method uses a piecewise aggregate approximation (PAA) algorithm with a shift factor to reduce the dimension of the cell voltage time series, after which a deletion mechanism is designed based on the clustering algorithm and outlier identification to calculate the clustering quality after deleting each cell, reflecting the deviate degree of each cell. In addition, a safety management strategy is designed based on the Z-score method, and an abnormality coefficient is set to evaluate the inconsistency of cells. The effectiveness of the proposed diagnosis method is verified by monitoring the voltage data of two real-world electric vehicles. The verification results show that the method can not only detect the inconsistency before the failure of the faulty cell in the battery pack in advance, but also reduce the risk of computational explosion caused by the voltage time series and accurately locate the faulty cell.

Full-state time-varying asymmetric constraint control for non-strict feedback nonlinear systems based on dynamic surface method

We investigate the tracking control problem for a non-strict feedback nonlinear system with external disturbance and time-varying asymmetric full state constraints. Firstly, the unknown nonlinear term with external disturbance in the system are estimated by fuzzy logic system. The backstepping method is applied to the design of adaptive fuzzy controller. However, to prevent that the constraints are overstepped by introducing an improved log-type time-varying asymmetric barrier Lyapunov function (TABLF) in each step of backstepping design. Secondly, the dynamic surface control (DSC) is introduced in the designed algorithm to solve the computational explosion problem of controller caused by the derivative of control law. The proposed control scheme can speed up the tracking speed of the system. Compared with the previous work, it is verified that the combination of DSC and TABLF can obtain good performance within the constraint range, and can ensure fast and stable tracking convergence under external disturbance. Finally, two simulation examples verify the performance of the adaptive controller.

NN‐based decentralized adaptive event‐triggered control for nonlinear interconnected systems under intermittent DoS and injection attacks

SummaryThis article investigates the problem of decentralized adaptive neural network event‐triggered control of strict feedback nonlinear interconnected systems suffering injection attacks and intermittent denial‐of‐service (DoS) attacks. When DoS attacks occur, the sensor‐controller communication channel is jammed. This may result in system states and traditional backstepping methods are unavailable. A novel switching‐type state observer is constructed to overcome the aforementioned challenges. When the injection attacks occur, the input signals of the controller‐actuator communication channel are changed. A decentralized adaptive event‐triggered controller is designed by using the backstepping method, where a first‐order sliding mode differentiator is introduced to avoid “computational explosion.” The observer gain is derived via the linear matrix inequality technique simultaneously. By using an improved average dwell time method and Lyapunov function theory, all closed‐loop signals are bounded. Finally, a numerical example is used to verify the effectiveness of the proposed control scheme.

Robust adaptive tracking control for input and state constrained uncertain nonlinear systems with unknown control directions

This article is committed to studying the tracking control problem for a class of uncertain nonlinear system with unknown control coefficients. The system is subject to full-state constraints, input saturation constraint, and external disturbances simultaneously. By introducing a hyperbolic tangent function to approximate the saturated input function, the sharp corner caused by the input saturation is avoided. In the meanwhile, an auxiliary system is constructed to compensate the approximation error. By using the barrier Lyapunov function based adaptive backstepping control, the Nussbaum-type adaptive controllers are constructed for the augmented system with unknown control direction. It not only ensures the system states are always within the constrained range, but also guarantees the tracking performance of the system, no matter whether the control direction of the system is known or not. Meanwhile, dynamic surface control is used in the controller design, which avoids ‘computation explosion’ caused by the repeated derivation of virtual control law. It improves the tracking performance of the system and reduces the burden of the controller greatly. Finally, a simulation example is given to demonstrate the effectiveness of the proposed control scheme in three scenarios.

A novel hierarchical cooperative merging control model of connected and automated vehicles featuring flexible merging positions in system optimization

Merging sections on freeways are typical bottlenecks for traffic efficiency and safety. The connected and automated vehicles (CAV) technology has great capability in improving traffic performance of merging bottlenecks. Current approaches of system optimal cooperative merging control assume single fixed merging point (CMC-SMP) for on-ramp vehicles to reduce the complexity of their models. Non-fixed merging positions have the potential to further improve merging operations but may lead to the computational explosion. To solve this core contradiction, this study proposes a novel hierarchical system optimal cooperative merging control model considering flexible merging positions (CMC-FMP) to realize safe and efficient merging processes. The upper-level – the tactical planning model is formulated as a non-convex mixed integer quadratically constrained programming, aiming at minimal total travel time in the control zone. It optimizes not only the merging sequence but also vehicles’ critical states in merging processes, such as the state of on-ramp vehicles when they merge. The lower-level - the motion planning model generates feasible trajectories and the next step actions for every vehicle. A Monte Carlo Tree Search-based decomposition algorithm (MCTS-DA) is further designed to improve the computational efficiency of the tactical planning model. Meanwhile, solutions of MCTS-DA are proved to have good optimality compared to the direct solving method. A batch-based scheme is developed to realize real-time control. The results reveal that the average delays of CMC-FMP are remarkably lower than those of CMC-SMP. Especially in low on-ramp ratio scenarios, the difference in improvement can reach 64%. Furthermore, the sensitivity analysis indicates that large safe merging margins and short merging sections are disadvantageous for traffic efficiency for the merging flow controlled by CMC-FMP.

Barrier Lyapunov Function-Based Finite-Time Reliable Trajectory Tracking Control of Fixed-Wing UAV With Error Constraints

A prospective issue that the finite-time reliable trajectory tracking control (TTC) scheme for a fixed-wing unmanned aerial vehicle (UAV) with error constraint is considered in this paper. The outer-loop position dynamics is formulated as a quadratic integral system. By utilizing a finite-time command filter to deal with the computational explosion in backstepping control architecture, a finite-time super-twisting sliding-mode disturbance observer (SMDO) is proposed to estimate the uncertain external disturbances. In addition, a barrier Lyapunov function (BLF) is used to constrain the trajectory tracking error and verify the finite-time stability of the reliable TTC scheme. Eventually, the effectiveness is given by simulation results

Event-based adaptive fuzzy fixed-time control for nonlinear interconnected systems with non-affine nonlinear faults

This article studies the event-based adaptive practical fixed-time decentralized control problem for a class of large-scale uncertain nonlinear systems subject to non-affine nonlinear faults. By fusing the techniques of fixed-time control and command filter, the problems such as singular-value and computational explosion in fixed-time control based on backstepping design framework are solved. Based on the fuzzy approximation theory and Butterworth low-pass filter (BLPF) technique, the non-affine nonlinear fault terms are processed and fault-tolerant controllers are designed to realize fault compensation. In order to relieve the communication the pressure between system components, a novel switching event-triggering mechanism (SETM) based on user-set threshold parameters is proposed. This switching threshold design the scheme combines the advantages of the traditional fixed threshold and relative threshold design strategies, providing greater flexibility in balancing system performance and network constraints. By combining the practical fixed-time stability criterion with the Lyapunov stability theory, it is demonstrated that both the tracking performance and the closed-loop stability can be ensured in a fixed-time. Finally, two simulation examples are given to verify the effectiveness of the proposed fixed-time switching event-triggered control (SETC) scheme.

Fuzzy synchronization of fractional-order chaotic systems using finite-time command filter

For conventional backstepping control, the analytical calculation of certain virtual controller is required in each step, so when system order increases, the computational burden also increases. An adaptive command filtered fuzzy synchronization approach is implemented for strict feedback fractional-order chaotic systems (FCSs) in this paper. The computational explosion in the backstepping is solved through a fractional-order command filter (FCF). The filtering approximation errors arising from the FCF are overcame by a error compensation mechanism (ECM). In every step, a fuzzy system is introduced to process system uncertainties. The stability of synchronization errors of a strict feedback FCS is ensured based on fractional Lyapunov stability criteria. Finally, numerical simulation examples illustrate the effectiveness of the proposed scheme.

Adaptive neural formation control of autonomous underactuated surface vessels based on disturbance observer with leader–follower strategy

In this paper, a leader–follower formation control strategy is presented based on adaptive neural network and disturbance observer, which is aimed at resolving model uncertainties as well as the time-varying disturbances for autonomous underactuated surface vessels. The model uncertainties which can be expressed by unknown nonlinear functions are approximated and compensated by the adaptive neural network. The disturbance observer introduced can estimate time-varying disturbances and compensate them to the feedforward control loop, so as to make the external time-varying disturbances suppressed and the robustness of controller against the disturbances improved. The dynamic surface control technology is applied in the procedure of designing the controller through utilizing the backstepping method, which solves the computational explosion of the derivative of virtual control signals. Finally, through Lyapunov analysis, the stability of adaptive neural formation control system is proved and all the error signals uniformly converge to a very small range ultimately. The excellent performance of the presented formation control strategy is demonstrated through numerical simulations.

Fuzzy adaptive finite-time output feedback control of stochastic nonlinear systems

An adaptive finite-time approach to the feedback control of stochastic nonlinear systems is presented. The fuzzy logic system (FLS) and a state observer are used to estimate the uncertain function and unmeasured state of the controlled system, respectively. A dynamic surface control (DSC) scheme is employed to deal with the “computational explosion” problem, which is inherent in traditional backstepping methods since the repetitive calculation of the derivatives of virtual control signals is avoided. A new output feedback controller is developed to guarantee that all the signals of the controlled system are bounded within a finite time range and the tracking deviation can converge to an arbitrarily small residual set within finite time. Simulations confirm the analytical and theoretical results of the presented algorithm.

Dual-Motor Synchronization Control Design Based on Adaptive Neural Networks Considering Full-State Constraints and Partial Asymmetric Dead-Zone.

This paper proposes a command filtering backstepping (CFB) scheme with full-state constraints by leading into time-varying barrier Lyapunov functions (T-BLFs) for a dual-motor servo system with partial asymmetric dead-zone. Firstly, for the convenience of the controller design, the conventional partial asymmetric dead-zone model was replaced with a new smooth differentiable model owing to its non-smoothness. Secondly, neural networks (NNs) were utilized to approximate the nonlinearity that exists in the dead-zone model, improving the control performance. In addition, CFB was utilized to deal with the inherent computational explosion problem of the traditional backstepping method, and an error compensation mechanism was introduced to further reduce the filtering errors. Then, by applying the T-BLF to the CFB process, the states of the system never violated the prescribed constraints, and all signals in the dual-motor servo system were bounded. The tracking error and synchronization error could converge to a small desired neighborhood of the origin. In the end, the effectiveness of the proposed control scheme was verified through simulations.