Anti-disturbance Ability Research Articles

Anti-disturbance fault-tolerant formation containment control for multiple autonomous underwater vehicles with actuator faults

This paper investigates the time-varying formation containment problem for multiple autonomous underwater vehicles (MAUVs) system subject to actuator faults in three-dimensional space. The leader AUVs are required to track the virtual leader AUV with time-varying offset vectors, and at the same time the follower AUVs are demanded to converge into the convex hull formed by the leader AUVs. The extended state observers (ESOs) are designed to estimate the external disturbances and unknown nonlinearities of MAUVs system in real time, hence ensuring the anti-disturbance ability of MAUVs system. The tracking differentiators (TDs) are developed so as to avoid the differential explosion issues caused by the derivatives of the virtual control laws. The distributed formation containment control laws are put forward for compensating the impacts of unknown bounded actuator bias fault and loss of actuator effectiveness fault based on adaptive control strategy. Furthermore, the stability of MAUVs system is demonstrated by means of Lyapunov stability theory. At last, a numerical simulation is presented to confirm the effectiveness of the proposed method.

Active Disturbance Rejection Control Approach for Double Pendulum Cranes with Variable Rope Lengths

The overhead crane is a typical underactuated system with complicated dynamics and strong couplings. It is widely employed to transport heavy cargoes in many industrial fields. Due to the complexity of working environments, however, cranes often encounter various unfavorable factors, which may degrade the transportation efficiency. To enhance control performance and anti-disturbance ability, this paper proposes an active disturbance rejection control approach based on differential flatness for double pendulum cranes with variable rope lengths. The proposed approach can position the trolley accurately, regulate rope length, and suppress the swing angles of the payload and the hook simultaneously. During the controller design, flat outputs were constructed based on differential flatness technique to deal with system couplings, and the results prove that double pendulum crane system is differentially flat. After that, model uncertainties and external disturbances were estimated by the designed extended state observer. On this basis, a controller was developed based on the feedback control technique. Finally, a series of simulations were carried out to show that the control scheme is effective and robust.

Linear active disturbance rejection control of heavy-haul train operation based on an interval type-2 fuzzy logic system model

Abstract The heavy-haul train (HHT) has large capacity and high efficiency, which represents the level of freight and makes the amelioration of control performance a trend in various countries. Improving the model reliability and increasing the anti-disturbance ability of the operation controller are two main ways to improve the operation control accuracy of HHTs. Herein, to describe the large nonlinear system more precisely, an interval type-2 fuzzy logic system (IT2FLS) is introduced to obtain a dynamic model. Then, a linear active disturbance rejection controller (LADRC) is designed to achieve precise operational control. In addition, the ‘bandwidth method’ is combined with the sparrow search algorithm (SSA) to solve the difficulty of controller parameters adjustment. Afterwards, the stability analysis of the closed-loop control system is given. The simulation experiments are conducted based on data collected from HXD1 locomotives driven by excellent drivers. Results show that the speed tracking error is no more than 0.5 km/h, and demonstrate that the proposed method significantly improves the operational performance of HHTs.

Speed Tracking Control of High-Speed Train Based on Particle Swarm Optimization and Adaptive Linear Active Disturbance Rejection Control

This paper proposes a control scheme combining improved particle swarm optimization (IPSO) and adaptive linear active disturbance rejection control (ALADRC) to solve the high-speed train (HST) speed tracking control problem. Firstly, in order to meet the actual operation of a HST, a multi-mass point dynamic model with time-varying coefficients was established. Secondly, linear active disturbance rejection control (LADRC) was proposed to control the speed of the HST, and the anti-disturbance ability of the system was improved by estimating and compensating for the total disturbance suffered by the carriage during the operation of the HST. Meanwhile, to solve the problem of difficult parameter tuning of the LADRC, IPSO was introduced to optimize the parameters. Thirdly, the adaptive control (APC) was introduced to compensate for the observation error caused by the bandwidth limitation of the linear state expansion observer in LADRC and the tracking error caused by an unknown disturbance during the train’s operation. Additionally, the Lyapunov theory was used to prove the stability of the system. Finally, the simulation results showed that the designed control scheme is more effective in solving the problem of HST speed tracking.

Law and Mechanism Study on Salt Resistance of Nonionic Surfactant (Alkyl Glycoside) Foam

In this paper, the effects of three cations, Ca2+, Mg2+, and Na+, on the stability of APG foams were investigated experimentally. The results show that cations can slow down the process of liquid drainage and coarsening of APG foam, which is beneficial to the stability of the foam. The salt resistance mechanism of nonionic surfactant (APG) was investigated by molecular dynamics simulation and compared with that of anionic surfactant (SDS) foam. Firstly, the distribution characteristics of cations in APG foam and SDS foam were explored. It was found that the cations in the APG foam were mainly distributed in the water layer away from the head groups, and the cations in the SDS foam were more likely to appear near the head groups. Then, the hydration of the head groups and the cation was investigated. The results show that cations have little effect on the number of water molecules in the hydration layer of APG head groups but will reduce the diffusion capacity of water molecules and increase the water retention capacity of the foam film, thereby enhancing the foam stability. The addition of cations will reduce the water retention capacity of the SDS foam film. In addition, the behavior of surfactant head and tail groups was also analyzed. It was found that the cations made the head groups of APG more inclined to be aligned perpendicular to the liquid interface, and the tail groups were more inclined to realize a cross-arrangement and cover the gas–liquid interface. This can not only slow down the gas phase mass transfer process of the adjacent foam and slow down the coarsening process of the foam but also increase the viscoelasticity and anti-disturbance ability of the foam film. The cations will weaken the staggered arrangement of the SDS molecular tail groups, and at the same time, will cause the SDS molecules to aggregate, which greatly reduces the stability of the foam.

Sparrow search algorithm applied to temperature control in PEM fuel cell systems

Working temperature is a critical parameter that influences the performance of proton exchange membrane fuel cells (PEMFCs). Appropriate thermal management can improve the efficiency of PEMFCs and prevent irreversible internal damage such as membrane degradation. To solve the slow response and poor dynamic performance of traditional temperature control strategies, the dynamic temperature mode of PEMFCs is established and a temperature control strategy based on the sparrow search algorithm-proportional integral differential (SSA-PID) is proposed in this study. The performance of the SSA-PID temperature control is verified by the simulations of current step change, vehicle dynamic load, and working parameter variation. The results show that the proposed method has the advantages of fast convergence, better dynamic performance, and anti-disturbance ability. The maximum power density of the SSA-PID temperature controller is 6.58% and 12.94% higher than GA (genetic algorithm)-PID and traditional PID controllers, respectively. Moreover, the proposed method can ensure temperature fluctuations within 0.5 K under dynamic disturbance.

A New Reconstruction Algorithm for Geometric Shape of Static Overhead Transmission Lines

This paper proposes a reconstruction algorithm for the geometric shapes of static overhead transmission lines that is based on the spatial coordinate data (obtained by field mapping) and the corresponding inclination data (obtained by attitude sensors). The algorithm takes the piecewise cubic Hermite interpolation function as the basic framework and corrects the aforementioned two types of data based on the “minimum energy principle” of the transmission line system. The introduction of a new energy function in modified energy method (MEM) overcame the shortcomings of conventional energy method (CEM) in fairing strategy, and the new Fairing Criterion made result curves more consistent with the mechanical properties of transmission lines. In the numerical simulation tests, the two energy methods were compared in terms of load type, horizontal span, elevation difference, and anti-disturbance ability, which showed that MEM has a higher reconstruction accuracy (average Euclidean distance is less than 1 cm) and better robustness than CEM. Meanwhile, the key parameters involved in the algorithm were discussed to guide the application in practical engineering. In addition, measurement tests under non-uniform icing were carried out in the field test station, and the results showed that the maximum relative error is less than 0.1%.

Research on photovoltaic MPPT control Strategy based on improved sliding mode control

MPPT intelligent control strategy algorithm is complex and practical application is difficult, So an improved sliding mode controller is proposed, which can improve engineering application, but also quickly and stably track the maximum power point of the system. In this paper, Boost circuit as the carrier, by increasing the amount of control in the controller, to build a new sliding mode control algorithm, Lyapunov stability theory analysis to demonstrate the correctness of the design algorithm, and through Simulink modeling and simulation, verify the feasibility of the control algorithm. The results show that the controller can quickly and stably track the maximum power of solar cells with small chattering and strong anti-disturbance ability regardless of the change of ambient temperature or light intensity.

Sliding mode predictive control of a five-level rectifier with only four–IGBT

PurposeFive-level rectifiers have received widespread attention because of their excellent performance in high-voltage and high-power applications. Taking a five-level rectifier with only four-IGBT for this study, a sliding mode predictive control (SMPC) algorithm is proposed to solve the problem of poor dynamic performance and poor anti-disturbance ability under the traditional model predictive control with the PI outer loop.Design/methodology/approachFirst, mathematical models under the two-phase stationary coordinate system and two-phase synchronous rotating coordinate system are established. Then, the design of the outer-loop sliding mode controller is completed by establishing the sliding mode surface and design approach rate. The design of the inner-loop model predictive controller was completed by discretizing the mathematical model equations. The modulation part uses a space vector modulation technique to generate the PWM wave.FindingsThe sliding mode predictive control strategy is compared with the control strategy with a PI outer loop and a model predictive inner loop. The proposed control strategy has a faster dynamic response and stronger anti-interference ability.Originality/valueFor the five-level rectifier, the advantages of fast dynamic influence and parameter insensitivity of sliding mode control are used in the voltage outer loop to replace the traditional PI control, and which is integrated with the model predictive control used in the current inner loop to form a novel control strategy with a faster dynamic response and stronger immunity to disturbances. This novel strategy is called sliding mode predictive control (SMC).

Non-switching reference trajectory based discrete-time sliding mode path following control for dynamic positioning ship with time-varying disturbances

This paper investigates the discrete-time sliding mode (DSM) path following control problem for dynamic positioning ship (DPS) with time-varying disturbances and sideslip based on non-switching reference trajectory and discrete-time disturbance observer (DDO). Initially, a high-order DDO and a finite-time sideslip angle observer are designed to observe the time-varying disturbance and sideslip angle, respectively, to compensate for their effects on DPS. Then, the discrete-time integral sliding mode surface is designed by introducing the nonlinear integral with characteristics of small error amplification and large error saturation, which avoids the system instability due to integral saturation. Furthermore, a new DSM path following controller is designed based on the new reaching law composed of disturbance observation errors and non-switching reference trajectory. The conclusion that the position tracking error can asymptotically converge to zero is inferred. The proposed control scheme avoids the problem that the continuous-time control law is not suitable for the actual shipborne discrete-time controller and the system chattering caused by switching functions and simultaneously ensures the system’s anti-disturbance ability. Finally, comparative simulation results verify the effectiveness and superiority of the proposed control scheme.

A novel deep learning–based fault diagnosis algorithm for preventing protection malfunction

To prevent serious malfunctions and reduce the impact of faults during an emergency state of a power system, protection systems are required to have disturbance and fault state identification abilities. In this study, a novel fault diagnosis framework based on deep learning with anti-disturbance ability is proposed to identify the fault state and fault type information, even under the influence of system disturbance. The framework consists of two parts: unsupervised and supervised learning. Specifically, an unsupervised deep auto-encoder (DAE) is applied for offline feature selection and data cleaning. The DAE can extract key fault features and significantly improve the fault detection accuracy. Furthermore, two supervised convolutional neural networks are used to learn key fault feature extraction online from complex operation information in power systems and assess the fault situation and type. Using case studies, the proposed method was implemented and compared with existing intelligent methods. The results indicate that the proposed framework has a better performance in terms of fault state identification and protection malfunction prevention.

A disturbance suppression micro-Newton force sensor based on shadow method

The precision of micro-force measurement depends on the force sensor sensitivity and the environmental disturbance magnitude. However, micro-force sensors generally have the poor anti-disturbance ability. Inspired by the shadow formed by water striders walking on water surface under sunlight, a viscoelastic-polymer micro-force (VPMF) sensor based on the shadow method was proposed, which could suppress disturbances effectively due to the high damping ratio of 0.22. The shadow diameter change and the applied force were proportional. The experimental results indicated that the sensitivity could reach 2.15 μN/pixel with a good linear performance. Furthermore, compared with the cantilever, it was capable of the reduction of the disturbance influence by approximately 96.35%. Therefore, the VPMF sensor can be applied to reliable micro-force measurement in complex environments such as industrial sites.

Operator-Based Fractional-Order Nonlinear Robust Control for the Spiral Heat Exchanger Identified by Particle Swarm Optimization

Fractional-order calculus and derivative is extended from integral-order calculus and derivative. This paper investigates a nonlinear robust control problem using fractional order and operator theory. In order to improve the tracking performance and antidisturbance ability, operator- and fractional-order-based nonlinear robust control for the spiral counter-flow heat exchanger described by the parallel fractional-order model (PFOM) is proposed. The parallel fractional-order model for the spiral counter-flow heat exchanger was identified by particle swarm optimization (PSO) and the parameters of a fractional-order PID (FOPID) controller were optimized by the PSO. First, the parallel fractional-order mathematical model for a spiral counter-flow heat exchanger plant was identified by PSO. Second, a fractional-order PID controller and operator controller for the spiral heat exchanger were designed under the identified parallel fractional-order mathematical model. Third, the parameters of the operator and fractional-order PID were optimized by PSO. Then, tracking and antidisturbance performance of the control system were analyzed. Finally, comparisons of two control schemes were performed, and the effectiveness illustrated.

Suppressing Quadrature Error and Harmonics in Resolver Signals via Disturbance-Compensated PLL

The aim of this study was to obtain accurate angular positions and velocities from resolver signals; resolver-to-digital conversion (RDC) often adopts a phase-locked loop (PLL) as a demodulation algorithm. However, resolver signals often come with quadrature errors and harmonics, which lead to a severe reduction in PLL accuracy. The conventional PLL does not consider the impact of the quadrature error, and the bandwidth of the PLL is much larger than the fundamental frequency of resolver signals for pursuing a low dynamic error. These reasons render the retention of resolver harmonics in the demodulation results. In this paper, a disturbance-compensated PLL (DC-PLL) is proposed, which consists of a phase detector for suppressing quadrature error and harmonics (SQEH-PD) and a second-order observer. Firstly, since the quadrature error does not change with the angle velocity, the pre-estimated quadrature error is used in the SQEH-PD to compensate for the quadrature error in resolver signals. Secondly, although the frequency of the harmonics changes with the velocity, the amplitudes of the harmonics do not change. Therefore, the pre-estimated amplitudes of harmonics and estimated angular position are used in the SQEH-PD to compensate for the harmonics in resolver signals. Thirdly, a second-order observer is designed to estimate the angular position and velocity by regulating the phase detector error. Compared with the conventional PLL, the proposed DC-PLL has a stronger anti-disturbance ability against the quadrature error and harmonics by configurating the phase detector error and the estimated position error, which have a linear relation. Simulation and experimental results prove the effectiveness of the proposed method.

High-potential control for durability improvement of the vehicle fuel cell system based on oxygen partial pressure regulation under low-load conditions

In the state-of-the-art high-power self-humidifying proton exchange membrane fuel cell (PEMFC) systems for vehicles, the high potential and low water production at idle or low load conditions strongly cause corrosion and decay of key materials and thus reduce durability. Therefore, the control technology of system-level durability requires an innovative design. Cathode recirculation is beneficial in alleviating the above unfavorable factors from the perspective of regulating oxygen and vapor partial pressure. This paper presents a pioneering study on the dynamics and control of cathode recirculation in vehicle high-power self-humidifying PEMFC system under low load conditions. First, a control-oriented dynamic model of the vehicle PEMFC system with a cathode recirculation loop is developed and the steady-state and dynamic performance is verified with experimental data from a 120 kW system. Active control of the intake component is achieved by re-feeding the reacted cathode gas to the air compressor outlet through a recirculation pump. On this basis, a high-potential controller based on oxygen partial pressure regulation is designed in combination with the dynamics of cathode recirculation. Results show that the designed dynamic fuzzy logic segmented proportional integral derivative controller with feedforward compensation achieves the optimal high-potential control effect by managing the oxygen partial pressure under variable low load conditions. It not only has excellent anti-disturbance ability but also effectively reduces the dynamic response time, transient overshoot, and steady-state error to satisfy the rapid and stable voltage output. Finally, the concomitant effect of humidification brought by the implementation of the optimal high-potential controller is analyzed, and the results show that the proton membrane is completely humidified.

A composite sliding-mode observer with PLL structure for a sensorless SPMSM system

To achieve high-precision speed and position control for the surface-mounted permanent magnet synchronous motor (SPMSM), this paper proposes a sensorless vector control strategy based on a composite sliding-mode observer (SMO) and an improved phase-locked loop (PLL) structure. Different from the existing sensorless methods, the proposed control strategy can further improve the accuracy of observation position and speed. First, a new sliding-mode function and the equivalent control are introduced to weaken the chattering and accelerate the reaching rate. Then, a sliding-mode current observer based on an adaptive law control is designed to observe the back electromotive force (back EMF). Meanwhile, the adaptive law can ensure the stability of the system in different states and reduce the system chattering. Second, an adaptive back EMF observer is presented to accurately observe the estimated back EMF. Furthermore, the stability of the two observers is proved by employing the Lyapunov second method. Next, the variable gain PLL with the signal suppressor is used to obtain an accurate rotor position and speed. Finally, the simulations and experiments are implemented to validate the performance of the proposed control strategy, in terms of speed and position tracking as well as anti-disturbance ability.

Super‐spiral sliding mode controller design for single‐winding bearingless switched reluctance motor

The strong coupling effect between the torque system and the suspension system of the single-winding bearingless switched reluctance motor (SWBSRM) would result in continuous interference to the system and bring difficulties to the design of the control system. Moreover, strong external disturbances will further reduce the stability of the control system. To improve the anti-disturbance ability and enhance the robustness of the control system of SWBSRM, a design method of the entire control system based on the second-order sliding mode (SOSM) controllers is proposed. The strongly coupled SWBSRM torque system and suspension system are linearised and decomposed through the feedback linearisation method to benefit the design of SOSM controllers. Then, combined with a super-spiral algorithm, the super-spiral sliding mode controllers in the outer loop for torque and suspension system are designed separately. The design of controllers considers the interference caused by the coupling effect and the influence of external disturbance. The range of gain, which can ensure the finite time convergence of the control system is determined through proof of stability. Simulation and experiment results show that the second-order sliding mode controllers have faster response speed and control precision when suppressing coupled interference and strong external disturbance.

Application of the Gray Wolf Optimization Algorithm in Active Disturbance Rejection Control Parameter Tuning of an Electro-Hydraulic Servo Unit

A valve-controlled hydraulic cylinder system has the characteristics of uncertainty and time-variance, and the electro-hydraulic servo unit encounters shock, vibration, and other external interference when working, which seriously affect the stability of the valve-controlled hydraulic cylinder system. Therefore, it is necessary to introduce an active disturbance rejection controller (ADRC) into the electro-hydraulic servo control. However, there are many ADRC parameters, and it is difficult to set these only with expert experience. Therefore, we propose applying the gray wolf optimization algorithm (GWO) to the ADRC, to auto-tune the parameters and find the optimal solution. In addition, the advantages of the GWO in ADRC parameter tuning are proven and analyzed. The simulation and experimental results showed that the GWO algorithm had a faster mean time for parameter tuning and the smallest fitness value (integrated time and absolute error), compared to the particle swarm optimization algorithm and genetic algorithm. Moreover, a valve-controlled cylinder system, after parameter tuning by the gray wolf optimization algorithm, could accurately adjust the parameters of the auto-disturbance rejection controller, with a faster response speed, smaller overshoot, and better anti-disturbance ability.

Torque Ripple Suppression for Permanent-Magnet Synchronous Motor based on Enhanced LADRC Strategy

Permanent-Magnet Synchronous Motor has shown advanced performance in the industry, but its smooth operation is affected by torque ripple. The conventional Linear Active Disturbance Rejection Controller (LADRC) has a strong anti-interference ability. However, this usually makes it more sensitive to measurement noise, reducing steady-state accuracy. This study proposes an Enhanced LADRC controller to obtain strong system disturbance and noise suppression performance. Firstly, the total disturbance differential signal is introduced into the Linear Extended State Observer to improve the anti-disturbance ability of the system, and the bandwidth parameter configuration method is used to reduce the number of parameters be adjusted in the controller to two. Second, A Fal-Filter is built through the nonlinear function and inserted into the controller to weaken the measurement noise. Finally, the simulation model is established on the dSPACE motor control platform, and the corresponding control algorithm is implemented. Simulation and experimental results show that the control scheme can significantly improve the phase current distortion and reduce torque ripple.

Application of new sliding mode control in vector control of PMSM

Aiming at the characteristics of low observation accuracy and high chattering of traditional sliding mode observers (SMO), a new SMO is proposed to reduce chattering and improve accuracy. In PMSM vector control, PI control has the problems of poor anti-disturbance ability and speed pulsation, while the traditional sliding mode control (SMC) has the problem of serious chattering, Therefore, on this basis, an SMC based on the new reaching law is designed, and the simulation is carried out in Matlab/Simulink. The simulation results show that the improved SMO has better performance and stronger stability than the traditional SMO. At the same time, compared with the traditional SMC and PI control, the SMC based on the new reaching law can make it respond well when the motor target speed changes suddenly, and effectively improve the problem of traditional SMC chattering, The control accuracy and steady-state error of the PMSM system are improved.