Adaptive Mode Control Research Articles

Operation Control Method for High-Speed Maglev Based on Fractional-Order Sliding Mode Adaptive and Diagonal Recurrent Neural Network

The speed profile tracking calculation of high-speed maglev trains is mainly affected by running resistance. In order to reduce the adverse effects and improve tracking accuracy, this paper presents a maglev train operation control method based on a fractional-order sliding mode adaptive and diagonal recurrent neural network (FSMA-DRNN). First, the kinematic resistance equation is established due to the three types of resistance that occur during the actual operation of a train: air resistance, guide eddy current resistance, and suspension frame generator coil resistance. Then, the FSMA-DRNN control law and parameter update law are designed, and a FSMA-DRNN operation controller is composed of three parts: speed feed forward, fractional-order sliding mode adaptive equivalent control, and diagonal recurrent neural network resistance compensation. Furthermore, by using the designed operation controller, it is proven effective by the Lyapunov theory for the stability of the closed-loop control system. Apart from the proposed theoretical analysis, the proposed approaches are verified by experiments on the high-speed maglev hardware-in-the-loop simulation platform Rt-Lab, in line with the 29.86 km test line and a five-car train from the Shanghai maglev, showing the effectiveness and superiority for operation optimization.

Adaptive backstepping fast terminal sliding mode control of dynamic positioning ships with uncertainty and unknown disturbances

Considering the complexity of the operating environment of marine ships, higher demands are placed on the positioning accuracy and positioning performance of Dynamic Positioning (DP) systems. This paper designs an Adaptive Backstepping Fast Terminal Sliding Mode Control (ABFTSMC) to solve the problems of low positioning accuracy, slow convergence, and poor anti-interference performance in ship positioning due to uncertainty of dynamic model parameters and unknown time-varying environmental disturbances. A Fast Terminal Sliding Mode Control (FTSMC) is combined with backstepping techniques to ensure the robustness of the system to uncertainties and disturbances. Meanwhile, an adaptive law is used to estimate the unknown uncertainty terms. Moreover, the Lyapunov stability criterion is used to prove the convergence and stability of the designed controller. Multiple simulation results show that ABFTSMC has better control performance compared with Adaptive Backstepping Control (ABC) and Adaptive Backstepping Sliding Mode Control (ABSMC). All the comparisons could fully verify the effectiveness and superiority of the proposed control method.

Adaptive backsliding control method of permanent magnet synchronous motor based on RBF

The adaptive backstepping control method of permanent magnet motor has the problems of complicated coordinate transformation process and high position tracking error. Based on this, an adaptive backstepping control method of permanent magnet synchronous motor based on RBF is proposed. According to the principle of electrical machinery, the electromagnetic wave and magnetic field data are obtained, and the mathematical model of permanent magnet synchronous motor is constructed. Under the condition of keeping the resultant magnetomotive force after coordinate transformation unchanged, the structure of motor torque neural network is established by RBF method, and the coordinate transformation process is optimized. Through the compensation control strategy, the adaptive backstepping control mode is designed to realize the adaptive backstepping control of permanent magnet synchronous motor. The simulation results show that the position tracking error of the proposed method is 4.549 mm when the running time is 7 s and 43.699 mm when the running time is 14 s, which proves that the adaptive backstepping control effect of the proposed method is better.

Adaptive non‐singular fast terminal sliding mode based on prescribed performance for robot manipulators

AbstractA fast convergent non‐singular terminal sliding mode adaptive control law based on prescribed performance is formulated to solve the uncertainties and external disturbances of robot manipulators. First, the tracking error of robot manipulators is transformed by using the prescribed performance function, which improves the transient behaviors and steady‐state accuracy of robot manipulators. Then, a novel fast convergent non‐singular terminal sliding mode surface is brought up according to the transformed error, and the control law is derived to meet the stability requirements of robot manipulators. In practice, the upper boundary of the lumped disturbances cannot be accurately obtained. Therefore, an adaptive prescribed performance control (PPC) controller to lumped disturbances is brought up to ensure the stability and finite‐time convergence of robot manipulators. Finally, the system stability of robot manipulators is proved by the Lyapunov theorem. Simulation results and comparative analysis demonstrate the superiority and robustness of the raised strategy.

Robustness Study of a Modular Smart Transformer Based on a Combined Control Strategy under Disturbed Operating Conditions in the Smart Distribution Grid

With the large-scale integration of renewable energy sources (RES) and electric vehicles, the smart distribution grid (SDG) represents a promising alternative to power distribution systems. However, the conventional transformer (CT) is a passive device that cannot meet SDG requirements. In this context, the smart transformer (ST) based on modular architecture is considered an attractive alternative to CT since it provides multiple DC buses in medium voltage (MV) and low voltage (LV) grids. This article proposes a modular ST model including a cascaded H-bridge rectifier in the MV AC-DC stage, dual active bridge converters in the high-frequency DC-DC stage, and a four-leg inverter in the LV DC-AC stage for SDG applications. To improve its dynamic performance, an adaptive backstepping-sliding mode control is introduced. The proposed controller enables the automatic estimation of unknown parameters, especially in the presence of intermittent RESs, by the adaptive laws of the adaptive backstepping controller (ABC) and the improvement of the ST robustness under grid disturbances by introducing the discontinuous control of the sliding mode controller (SMC). The obtained simulation results prove the effectiveness of the selected ST model and the higher robustness of the proposed controller against grid disturbances, compared to the ABC and SMC.

МАТЕМАТИЧЕСКОЕ ОБЕСПЕЧЕНИЕ ПОДСИСТЕМЫ СВЕТОФОРНОГО УПРАВЛЕНИЯ ИНТЕЛЛЕКТУАЛЬНОЙ ТРАНСПОРТНОЙ СИСТЕМЫ

The analysis of the requirements for the construction of intelligent transport systems (ITS) in the Russian Federation is given, the connectivity of the traffic light control subsystem with other subsystems and ITS modules is shown, which determines the need for an integrated approach to its creation. An algorithm for the formation of control actions for automatic motion control using pre-calculated control programs is given. In the above methodology, the periods of activity of pre-calculated control programs can be determined for time-dependent control and when control pro-grams are activated according to the observed traffic intensity. The functioning of the traffic light control subsystem in adaptive control mode is formulated as the control of traffic light objects with a predictive model.

All-Fiber Spatial Mode Excitation and Adaptive Control Based on Photonic Lanterns

All-Fiber Spatial Mode Excitation and Adaptive Control Based on Photonic Lanterns

Direct yaw-moment control of electric vehicles based on adaptive sliding mode.

The direct yaw-moment control (DYC) system consisting of an upper controller and a lower controller is developed on the basis of sliding mode theory and adaptive control technique. First, the two-degree of freedom (2-DOF) model is utilized to calculate the ideal yaw rate. Then, the seven-degree of freedom (7-DOF) electric vehicle model is given to design the upper controller by employing first-order sliding mode (FOSM) method, which is constructed to guarantee the actual yaw rate to approach the ideal value and gain the additional yaw moment. On this basis, an adaptive first-order sliding mode (AFOSM) controller is designed to enhance the system robustness against probable modelling error and parametric uncertainties. In order to mitigate the chattering issue present in the FOSM controller, a novel adaptive super-twisting sliding mode (ASTSM) controller is proposed for the design of DYC. Furthermore, the lower controller converting the additional yaw moment into driving or braking torque acting on each wheel is also developed. Finally, The simulation results indicate that the proposed DYC system can improve the electric vehicle driving stability effectively.

Based on Sliding Mode and Adaptive Linear Active Disturbance Rejection Control for a Magnetic Levitation System

The magnetic levitation system has evident advantages in reducing energy consumption, but its nonlinear characteristics increase the difficulty of control. This study proposes a control method that combines the improved particle swarm optimisation algorithm with sliding mode control and adaptive linear active disturbance rejection control (IPSO–SMC–ALADRC) to address the problems of weak anti‐interference ability and stability in the application of traditional control methods in single‐point magnetic levitation ball systems. First, a mathematical model of a single‐point magnetic levitation ball is established. Second, the proportional and differential coefficients of LADRC are adjusted using adaptive laws, and the adaptive LADRC is combined with SMC to achieve stable control of the magnetic levitation ball. Moreover, an improved particle swarm optimisation algorithm is proposed to address the considerable number of adjustable parameters in the controller. The convergence and stability of the control algorithm were demonstrated using the Lyapunov equation. Finally, PID and LADRC are introduced for simulation and experimental comparison to verify the effectiveness of this control method. Results indicate that IPSO–SMC–ALADRC has excellent stability and anti‐interference performance. This study addresses the problem of weak stability and anti‐interference performance in the application of traditional control methods in the maglev system and further promotes the application of active disturbance rejection control in the magnetic levitation system.

An Adaptive Predictive Control Algorithm for Comprehensive Dendritic Canal Systems

Automatic canal control is essential to improve water utilization efficiency in irrigation districts. Water level error in the model used by the control algorithm significantly impacts performance. The integrator delay model, which has a linear structure, is widely used in multi-input/multioutput control algorithms, including linear-quadratic control and model predictive control. However, the integrator delay model is suboptimal for large irrigation districts; the canals are long, the flows are large, there are many scattered offtakes, and the operating conditions vary. We developed an adaptive predictive control mode for the Qinhan Canal in Ningxia Province, China. We used a segment integrator delay model for this large irrigation district and employed a differential evolution algorithm for online system identification; this minimizes error. We analyzed and controlled for various uncertainties (i.e., flow observation noise, geometric and hydraulic parameter errors, and accidental interference with the automatic control system). Simulations indicated that the water level fitting errors were significantly reduced compared with the original model, and water level regulation was significantly improved, especially in terms of the maximum absolute error. Adaptive predictive control was better than linear-quadratic control and model predictive control approach for water level. Adaptive predictive control coped well with several uncertainties. The main factors affecting water level control were the flow observation and gate opening control accuracy, and the extent of real-time data coverage. These features require careful attention when automating and modernizing irrigation districts.

Nonsingular Terminal Sliding Mode Adaptive Control for Unmanned Aerial Manipulator in Gliding Grasping

Nonsingular Terminal Sliding Mode Adaptive Control for Unmanned Aerial Manipulator in Gliding Grasping

A Novel Neural Network-Based Robust Adaptive Formation Control for Cooperative Transport of a Payload Using Two Underactuated Quadcopters

Designing a controller for the cooperative transport of a payload using quadcopter-type unmanned aerial vehicles (UAVs) is a very challenging task in control theory because these vehicles are underactuated mechanical systems. This paper presents a novel robust adaptive formation control design for the cooperative transport of a suspended payload by ropes using two underactuated quadcopters in the presence of external disturbances and parametric uncertainties. The structure of the proposed controller is divided into two subsystems: fully actuated and underactuated. An integral sliding mode adaptive control strategy is proposed for the fully actuated subsystem, and for the underactuated subsystem, an adaptive control strategy based on the combination of Backstepping and sliding mode is proposed. Then, the control parameters of the sliding surfaces of both control subsystems are adaptively tuned by a neural network. In addition, to improve the robustness of the proposed controller, a disturbance observer is incorporated to estimate and compensate the lumped disturbances. The asymptotic stability of the cooperative transport system is verified with the Lyapunov theorem. Finally, numerical simulations are performed in MATLAB/Simulink environment, and the results show that the proposed controller successfully transports the payload safely and without oscillations. Moreover, the desired formation pattern is maintained throughout the flight task, even with external disturbances and parametric uncertainties.

Human Decision-Making Modeling and Cooperative Controller Design for Human–Agent Interaction Systems

In this article, the problem of human intervention and autonomous controller design for human–agent interaction systems is addressed. In particular, a human drift diffusion model is developed for second-order linear multiagent systems subject to unknown external disturbances. The proposed human drift diffusion model can model human decision-making behavior using multiple sources of human decision-making information. Accurate human intervention timing is obtained by setting varying thresholds for different decision-making information. In addition, a fixed-time sliding mode adaptive behavioural controller is developed to execute human decisions in the framework of the null-space based behavioral control method. The controller guarantees that agents can follow human commands given an arbitrary initial task error and can finish tasks in fixed time. A simulation under various scenarios shows that the proposed human drift diffusion model is able to provide appropriate human intervention and the proposed controller can guarantee human command fulfillment within fixed time.

Neural network based adaptive control for a piezoelectric actuator with model uncertainty and unknown external disturbance

AbstractTo lessen the positioning error of the piezoelectric actuator (PEA) caused by hysteresis nonlinearity and unknown external disturbance, a neural network based adaptive controller is designed to realize the accurate trajectory tracking of the PEA. Specifically, a more universal model, consisting of a hysteresis submodel and a dynamics submodel, is first built for the PEA without the requirement of parameter identification. On this basis, a sliding mode adaptive controller capable of handling unknown parameters of the dynamics submodel is designed to weaken the damage of external disturbance to the system stability. Furthermore, to deal with the hysteresis submodel with unknown structure and parameters, a neural network based self‐tuning control scheme is developed to enable the PEA to accurately track the desired trajectory. Moreover, Lyapunov stability analysis is performed to strictly prove that the tracking error of the system can asymptotically converge to zero. Finally, the performance of the designed controller is verified via sufficient comparative simulations and experiments.

Clinical Characteristics and Complications of Mechanically Ventilated Children in a Pediatric Intensive Care Unit in Iran: Comparing Different Modes

Background: Mechanical ventilation (MV) is among the most common therapeutic modalities in pediatric intensive care units (PICU), which works based on a defined ventilation mode. Nowadays, conventional and alternative modes including adaptive pressure control (APC) and non-APC modes are frequently employed. Although MV can be helpful in many cases, it may cause some complications resulting in significant morbidity and mortality. Objectives: This study aimed to investigate the demographic features and complications of mechanically ventilated children in a PICU in Iran, as well as to compare different ventilation modes. Methods: A retrospective case-control study was conducted in PICUs of children’s medical center hospital - a tertiary referral pediatric hospital. Results: Of 66 patients included in this study, 33 patients were treated with APC modes, whereas 33 patients were treated with non-APC modes. The most common indications for intubation were respiratory failure (53%) and loss of consciousness (13.6%). The mean duration for intubation in patients with and without underlying disorder were 11.7 and 5.2 days, respectively (P-value < 0.01). The means of time for intubation in the APC and non-APC groups were 10 and 11.9 days, respectively (P-value = 0.145). A total of 23 (34.8%) patients had complications, including death, misplacement of the endotracheal tube, atelectasis, unplanned extubation, etc. There was no significant difference between groups regarding the rates of complications, except for atelectasis. Thirteen (19.7%) patients had atelectasis (2 patients in APC group (6%) and 11 patients in non-APC group (33.3%)) (P-value = 0.022). The mortality rate was the same for the both groups (P-value = 1). Conclusions: In sum, the most common indication for intubation was respiratory failure. No significant difference was observed among patients treated with the APC, and non-APC modes in terms of the complications occurred, except for atelectasis which occurred more frequently in the non-APC group. Therefore, it was concluded that there was no difference between conventional and alternative modes of mechanical ventilation in terms of morbidity and mortality.

Optimal Trajectory Planning of Connected and Automated Vehicles at On-Ramp Merging Area

Cooperative Adaptive Cruise Control (CACC) systems can significantly improve traffic safety and roadway capacity utilizing short following gaps of vehicles enabled by inter-vehicle communications. However, due to merging processes occurring at freeway merging areas, existing CACC operation approaches are generally not applicable and the operation will have to revert back to Adaptive Cruise Control (ACC) or human-driven mode, which in turn will result in a capacity drop. This paper proposes an optimal trajectory optimization strategy for Connected and Automated Vehicles (CAVs) to cooperatively carry out mainline platooning and on-ramp merging. Firstly, a control framework of the CACC is adopted for a longitudinal control of CAVs, which helps individual CAVs to join platoons and to maintain platoon operations. Secondly, to ensure smooth lane-changing executions while achieving stable platoons, an optimal controller that considers lane-changing motivation of merging vehicles and impact of merging on platoons, is proposed. Third, a Legendre pseudo-spectral algorithm is applied to transform the controller into a simpler nonlinear programming problem and to efficiently solve it. Simulation assessments of the proposed method are conducted at both individual vehicle level and traffic-flow level. At the individual vehicle level, the proposed method has the potential to improve the traffic safety without compromising fuel consumption and emissions compared with unoptimized feasible schemes. At a traffic-flow level, an online evaluation platform is implemented, and a typical freeway on-ramp area is studied. The simulation results have demonstrated that the proposed controller provides significant improvements in terms of efficiencies in traffic operations.

An Adaptive Hybrid Control of Grid Tied Inverter for the Reduction of Total Harmonic Distortion and Improvement of Robustness against Grid Impedance Variation

Background: Grid-tied inverters play an efficient role in the integration of renewable energy resources with utility grids. Motivation: However, the interconnection between power converters and the grid has been seen to be responsible for various stability issues such as weak grid, and under weak grid conditions the injection of power to the grid becomes a challenging task due to continuously varying grid impedance affecting the stability margins as a result. Additionally, the grid impedance-related issues boost the voltage harmonics which further devalue its performance. These voltage harmonics propagate through the Phase-Locked Loop (PLL) circuit to the control unit of the inverter which in turn amplifies the low order harmonics of the inverter. Method: The aim of this research is to introduce a novel strategy that decreases the effect of grid impedance-variations on the performance and stability of an inverter. Hence, an adaptive hybrid mode control technique consisting of two parts is proposed in this research. The current regulator part is implemented in a synchronous reference frame for its gain and time parameters to improve the performance, stability, and response time. The adaptive harmonic compensators are implemented in a stationary reference frame for harmonic compensation purposes. This adaptive nature of harmonic compensator can effectively work in the case of frequency variation where the fixed value based harmonic compensator fails. Results: The adaptive harmonic compensators improve the performance by reducing total harmonic distortion (THD), reduce computation and improve stability when the grid has distorted voltage, variation in grid impedance and frequency. Impact and utility: Our results show that the system becomes less sensitive to grid impedance variations which makes the proposed technique very relevant to the stability performance applications.

Stable single transverse mode excitation in 50 µm core fiber using a photonic-lantern-based adaptive control system.

We report on the generation of single transverse mode output in large-mode-area fiber with a core diameter of 50 µm using a 3×1 photonic-lantern-based adaptive spatial mode control system. We have designed and fabricated the photonic lantern composed of a single mode fibers bundle taper region and a multi-segment multimode fiber splicing region. From simulation and experiments, we demonstrate that the quality of the output beam is significantly influenced by the size of the fibers bundle's waist and the segmented splicing scheme of the multimode fiber. Stable single transverse mode output is achieved at 1064 nm with M2 ∼1.4, which will provide a possible technical solution to increase the mode instability threshold in high power large-mode-area fiber systems.

Disturbance Observer-Based Finite-Time Control for Three-Phase AC–DC Converter

For three-phase ac–dc converters, load disturbances, and parameter uncertainties cause large fluctuations of the dc-link voltage, even resulting in system instability. In order to cope with this problem, the disturbance observer-based finite-time control (DOFTC) is put forward in this article, where the finite-time disturbance observer is designed to fast and accurately estimate disturbances. Subsequently, the stability of the ac–dc converter closed-loop system is analyzed and theoretically proved. Finally, experimental results verify the effectiveness of the DOFTC strategy, where good dynamic and steady-state performance is achieved under load disturbance and parameter uncertainties. Compared with the enhanced state observer-based sliding mode control and adaptive control method, the proposed DOFTC method not only reduced the fluctuations of the dc-link voltage by at least 55% but also improved the transient response.

Research on Trajectory Tracking Control of Inspection UAV Based on Real-Time Sensor Data.

In power inspection, uncertainties, such as wind gusts in the working environment, affect the trajectory of the inspection UAV (unmanned aerial vehicle), and a sliding mode adaptive robust control algorithm is proposed in this paper to solve this problem. For the nonlinear and under-driven characteristics of the inspection UAV system, a double closed-loop control system which includes a position loop and attitude loop is designed. Lyapunov stability analysis is used to determine whether the designed system could finally achieve asymptotic stability. Sliding-mode PID control and a backstepping control algorithm are applied to analyze the superiority of the control algorithm proposed in this paper. A PX4 based experimental platform system is built and experimental tests were carried out under outdoor environment. The effectiveness and superiority of the control algorithm are proposed in this paper. The experimental results show that the sliding mode PID control can achieve good accuracy with smaller computing costs. For nonlinear interference, the sliding mode adaptive robust control strategy can achieve higher trajectory tracking accuracy.