Optimal Preview Control Research Articles

Robust fault-tolerant preview control for automatic landing of carrier-based aircraft

PurposeThis paper aims to propose the automatic carrier landing system with the fault-tolerant ability for carrier-based aircraft in the presence of deck motion, external airwake disturbance and actuator fault, which consists of the reference trajectory generation module and flight control module.Design/methodology/approachThe longitudinal and lateral basic controllers are designed based on the optimal preview control (OPC), which can ensure favorable tracking performance and anti-disturbance ability of system. Furthermore, based on the OPC, the robust fault-tolerant preview control scheme is proposed to attenuate the impact of actuator fault on system, which ensures the safe landing of carrier-based aircraft in case of actuator failure.FindingsBoth the Lyapunov method and simulations prove that the tracking errors can converge to zero and system states can be asymptotically stable both in normal and fault operations.Originality/valueThe fault-tolerant control strategy is introduced into preview control to deal with actuator fault, which combines feedforward control based on future previewable information and feedback control based on current information to improve the system performance.

Damping Magnetorheological Systems Based on Optimal Neural Networks Preview Control Integrated with New Hybrid Fuzzy Controller to Improve Ride Comfort

<div>Adaptive neural networks (ANNs) have become famous for modeling and controlling dynamic systems. However, because of their failure to precisely reflect the intricate dynamics of the system, these have limited use in practical applications and perform poorly during training and testing. This research explores novel approaches to this issue, including modifying the simple neuron unit and developing a generalized neuron (GN). The revised version of the neuron unit helps to develop the system controller, which is responsible for providing the desired control signal based on the inputs received from the dynamic responses of the vehicle suspension system. The controller is then tested and evaluated based on the performance of the magnetorheological (MR) damper for the main suspension system. These results of the tests show that the optimal preview controller designed using the GN both ∑-Π-ANN and Π-∑-ANN can accurately capture the complex dynamics of the MR damper and improve their damping characteristics compared with other methods. The seat and main suspension systems work together to provide more support and comfort for the driver and passengers. The short stroke of the MR damper is used in seat suspension as it allows for more precise control over the suspension and can provide a smoother ride. The new hybrid fuzzy type-2 (T-2) control is designed to accurately estimate the desired damping force for the seat MR damper. This system also allows for the damping force to be adjusted to meet the desired requirements of the seat MR damper. This integration of damping systems allows better control and stability of the vehicle and provides a smoother ride for drivers and passengers. Furthermore, integrating the damping systems increases the overall performance of the vehicle, making it better able to handle various road conditions.</div>

Carrier Aircraft Flight Controller Design by Synthesizing Preview and Nonlinear Control Laws

This paper proposes an innovative automatic carrier landing control law for carrier-based aircraft considering complex ship motion and wind environment. Specifically, a strategy is proposed to synthesize preview control with an adaptive nonlinear control scheme. Firstly, incremental nonlinear backstepping control law is adopted in the attitude control loop to enhance the anti-disturbance capability of the aircraft. Secondly, to enhance the glide slope tracking performance under severe sea conditions, the carrier motion is predicted, and the forecasted motion is adopted in an optimal preview control guidance law to compensate influences induced by carrier motion. However, synthesizing the inner-loop and outer-loop control is not that straightforward since the preview control is naturally an optimal control law which requires a state-space model. Therefore, low-order equivalent fitting of the attitude-to-altitude high-order system model needs to be performed; furthermore, a state observer needs to be designed for the low-order equivalent system to supply required states to the landing controller. Finally, to validate the proposed methodology, an unmanned tailless aircraft model is used to perform the automatic landing tasks under variant sea conditions. Results show that the automatic carrier landing system can lead to satisfactory landing precision and success rate even under severe sea conditions.

Research on Information Fusion Preview Control for Freight Train Speed Tracking

Background: With the increase in the speed and capacity of freight trains, the safety, energy efficiency and intelligent operation of train operation have become increasingly important. Automatic freight trains operation can replace manual operation with automated control systems, which can guarantee the safety of train operation, and improve operational efficiency and reduce operational energy consumption. Objective: Solve the problem of tracking accuracy and stability deterioration caused by multi-information of freight train. Methods: Global navigation satellite system, inertial navigation system and speed measuring motor are selected to construct a speed fusion measurement model by using loosely coupled integrated navigation and improved entropy weight method. Calculate the information quantity of performance index and control quantity in preview control, get the controlled quantity of information fusion optimal preview control. Results: The average tracking error of the multi-source information fusion preview controller is 0.038m/s, which is 49% lower than that of the control experiment. Conclusion: Multi-source information fusion preview controller can effectively reduce the tracking error of freight train speed tracking system and improve the accuracy of automatic freight trains operation.

On the Development of a Path Tracking Controller by combining Optimal Preview Control and Pursuit Control Methods

On the Development of a Path Tracking Controller by combining Optimal Preview Control and Pursuit Control Methods

Performance Improvement of a Vehicle Equipped with Active Aerodynamic Surfaces Using Anti-Jerk Preview Control Strategy.

This paper presents a formulation of a preview optimal control strategy for a half-car model equipped with active aerodynamic surfaces. The designed control strategy consists of two parts: a feed-forward controller to deal with the future road disturbances and a feedback controller to deal with tracking error. An anti-jerk functionality is employed in the design of preview control strategy that can reliably reduce the jerk of control inputs to improve the performance of active aerodynamic surfaces and reduce vehicle body jerk to enhance the ride comfort without degrading road holding capability. The proposed control scheme determines proactive control action against oncoming potential road disturbances to mitigate the effect of deterministically known road disturbances. The performance of proposed anti-jerk optimal control strategy is compared with that of optimal control without considering jerk. Simulation results considering frequency and time domain characteristics are carried out using MATLAB to demonstrate the effectiveness of the proposed scheme. The frequency domain characteristics are discussed only for the roll inputs, while time domain characteristics are discussed for the corresponding ground velocity inputs of bump and asphalt road, respectively. The results show that using anti-jerk optimal preview control strategy improves the performance of vehicle dynamics by reducing jerk of aerodynamic surfaces and vehicle body jerk simultaneously.

Optimal Sliding Mode Preview Repetitive Control for Three-Phase Z-Source Inverters

Aiming at high output voltage and obvious current fluctuation of the off grids inverters with unbalanced and nonlinear loads, a compound control strategy of sliding mode controller and optimal preview controller based on Z-source inverter (ZSI) is proposed. ZSI can boost the pressure and improve the system energy conversion rate through by combining the state with the linear quadratic design method of optimal control. The preview controller is introduced in the feedforward compensation link. With the help of difference operator, an extended state error system covering the target value signal and lag link feedback is designed. The optimal preview repetitive control (PRC) is transformed into a linear quadratic regulation matter of the discrete systems. Furthermore, a preview repetitive controller is obtained by using the Lyapunov method, linear matrix inequality, and the design method of optimal controller, which can realize sliding mode control, state feedback, repetitive control, and preview compensation. Finally, a 10 kV A prototype is built to verify the effectiveness of the proposed strategy.

Optimal sliding mode preview repetitive control for continuous-time nonlinear systems

In this paper, the problem of sliding mode preview repetitive control (SMPRC) for continuous-time nonlinear systems is proposed. First, an augmented delay system is constructed, in which the output of the modified repetitive controller is taken as a part of the augmented state vector. The augmented delay system is then transformed into the controllable canonical form by using state and linear transformations. To introduce preview compensation, a virtual optimal preview control (PC) law is designed for the uncontrollable subsystem. Next, a sliding mode control (SMC) law is cooperated with the virtual optimal PC law to ensure the robustness of the overall system in the presence of uncertainties and external disturbances. Stability of the proposed controller is also proved. Finally, the numerical simulation results demonstrate the effectiveness of the proposed method. Abbreviations: LQR: linear quadratic regulator; MRC: modified repetitive control; PC:preview control; PRC: preview repetitive control; RC: repetitive control; SFC: statefeedback control; SMC: sliding mode control; SMPRC: sliding mode preview repetitivecontrol.

Anti-Jerk Optimal Preview Control Strategy to Enhance Performance of Active and Semi-Active Suspension Systems

This study aims to demonstrate how to compute the damping coefficient of a continuously variable damper for semi-active preview control suspensions while considering the sprung-mass jerk and the controller’s performance advantage. Optimal control theory is used to derive and validate the proposed preview approach to future road disturbances. Despite reduced body acceleration, semi-active suspensions with preview control display an increase in body jerk, implying that ride comfort may not be improved in practice. The optimal preview jerk controller for a semi-active system, on the other hand, can improve ride comfort without degrading road holding by minimizing the performance index that comprises the RMS value of jerk in addition to the RMS values of other outputs. The anti-jerk preview control suspension simulations considering frequency characteristics reveal a difference between suspension systems that consider jerk and those that ignore jerk. The time-domain simulations suggest that the proposed preview control strategy effectively to reduce body jerk, which other controllers cannot.

Stochastic optimal preview control response of a quarter-car nonlinear suspension model using spectral decomposition method

This paper considers a two degree of freedom quarter-car model of a four wheel vehicle with nonlinear spring travelling at a constant speed on a random road surface with a preview control to obtain the optimal vehicle response. The nonlinear spring behavior is assumed as a hysteresis and modelled by the Bouc–Wen model. This nonlinear vehicle model is linearized by using statistical linearization approach. The root mean square values of the control force, suspension stroke and road holding characteristics are determined by spectral decomposition method. The results obtained for the equivalent linearized model using the SDM are validated with Monte Carlo simulation, and the results show that the vehicle performs better using preview control.

Research on Trajectory Tracking of Sliding Mode Control Based on Adaptive Preview Time

The preview model is one of the common methods used in trajectory tracking. The traditional fixed preview time is not adaptable to most speeds and road conditions, which not only reduces the tracking accuracy but also reduces the vehicle stability. Therefore, a controller can be designed to determine the adaptive preview time based on an optimization function of the lateral deviation, the road boundary, and the road boundary of the whole vehicle motion response characteristics. Traditional optimal preview control theory predicts the next state of the vehicle by the assumption of constant transverse pendulum angular velocity. In this paper, an expectation-based approach is used to find the ideal steering wheel turning angle based on the adaptive preview time, and a single-point preview model is established. Based on the two-degree-of-freedom dynamics model, a sliding mode controller is designed for control, and the low-pass filters are designed to suppress jitter in the sliding mode controller. Simulation results with different preview times, different speeds and different road adhesion coefficients prove that the controller has a good control effect and has good effectiveness and adaptability to speed and adhesion coefficient.

Vibration control of semi-active suspension by the neural network that learned the optimal preview control of MLD model

Advances in image processing technology have made it possible to measure the surface shape of the road ahead while driving. We propose a new semi-active suspension control method that considers the forward road surface shape. A vehicle model equipped with a semi-active suspension can be expressed as an MLD (Mixed Logical Dynamical) model. When the shape of the road ahead can be measured accurately, that the information on the future disturbances is available before the vehicle undergoes. In this paper, we formulate the finite time optimization problem of the MLD model in consideration of the future disturbances as a MIQP (Mixed Integer Quadratic Programming) problem in the same way as the conventional optimal control problem without future disturbance. The solution to the MIQP problem can be obtained by a commonly available solver software. However, because the MIQP problem is classified as NP-hard, it is hard to obtain the control action within the control cycle period required by the vibration control problem with general computers for vehicles. In this paper, we achieve this reduction in computational load by constructing an approximation function for the designed controller. A multilayer neural network is adopted for the approximation. The performance evaluation of the proposed control method was evaluated by a simulation. In the simulation study, the proposed method was able to achieve better ride comfort with the equivalent suspension stroke compared to the traditional MLD predictive control and the Skyhook approximation methods. Moreover, the proposed method enables to generate the control signal within the control cycle period.

Vibration control of semi-active suspension with MR damper by optimal preview control of MLD model.

Vibration control of semi-active suspension with MR damper by optimal preview control of MLD model.

Modeling adaptive preview time of driver model for intelligent vehicles based on deep learning

In order to improve the adaptability and tracking performance of intelligent vehicles under complex driving conditions, and simulate the manipulation characteristics of the real driver in the driver–vehicle–road closed-loop system, a kind of adaptive preview time model for intelligent vehicle driver model is proposed. This article builds the intelligent vehicle driver model based on optimal preview control theory and the basic preview time is identified to minimize path error under various conditions based on particle swarm optimization. Then, the ideal compensation preview time is constructed in various conditions and the appropriate factors affecting compensation preview time are filtered out according to correlation analysis. Moreover, the architecture and training procedure of deep network is specified for compensation preview time prediction. Finally, the adaptive preview time is modeled by combining the basic preview time with the compensation preview time and the validity of adaptive preview time model is verified by the driver–vehicle–road closed-loop system under normal and aggressive driving conditions. The results show that the proposed adaptive preview time model can help intelligent vehicles better adapt complex driving conditions and effectively improve the path-following performance.

Coordinated speed and position control of integrated motor-transmission system

In this paper, a coordinated driving motor speed and shifting motor displacement control strategy is proposed for the integrated motor-transmission (IMT) system during the gearshift process. For active speed synchronization of IMT system, speed reference to driving motor is redesigned by using a polynomial speed trajectory. Compared with conventional step speed change reference, it can help improve the ride performance of IMT system. While in the gear release as well as engagement phase, a robust optimal preview controller is developed for the shifting motor to realize rapid and reliable position tracking of the sleeve in spite of load disturbance. Based on real time value of the driving motor speed and also sleeve axial position, proposed speed and position controllers are coordinated in plan during the whole gearshift process. Co-simulations with Matlab/Simulink and AMEsim are conducted to demonstrate dynamical characteristics of the IMT system during the whole gear shifting process, in which a two-layer switching logic is built by using Matlab/Stateflow. Comparative simulation tests are carried out to show the effectiveness as well as performance of proposed control strategies.

Design of an optimal preview controller for linear discrete-time periodic systems

In this paper, the preview tracking control problem for linear discrete-time periodic systems is considered. First, to overcome the difficulty arising from periodicity of the system, the linear discrete-time periodic system is transformed into an ordinary time-invariant system by lifting method. Secondly, the difference between a system state and its steady-state value is used to derive an augmented system instead of the usual difference between system states. Then, the preview controller for the augmented system is proposed by the preview control theory, which solves the preview tracking control problem for the periodic systems. Moreover, an integrator is introduced to ensure that the output can track the reference signal without static error. Finally, the obtained results are illustrated by the simulation examples.

Modeling human-like longitudinal driver model for intelligent vehicles based on reinforcement learning

The driver model is the decision-making and control center of intelligent vehicle. In order to improve the adaptability of intelligent vehicles under complex driving conditions, and simulate the manipulation characteristics of the skilled driver under the driver-vehicle-road closed-loop system, a kind of human-like longitudinal driver model for intelligent vehicles based on reinforcement learning is proposed. This paper builds the lateral driver model for intelligent vehicles based on optimal preview control theory. Then, the control correction link of longitudinal driver model is established to calculate the throttle opening or brake pedal travel for the desired longitudinal acceleration. Moreover, the reinforcement learning agents for longitudinal driver model is parallel trained by comprehensive evaluation index and skilled driver data. Lastly, training performance and scenarios verification between the simulation experiment and the real car test are performed to verify the effectiveness of the reinforcement learning based longitudinal driver model. The results show that the proposed human-like longitudinal driver model based on reinforcement learning can help intelligent vehicles effectively imitate the speed control behavior of the skilled driver in various path-following scenarios.

Vibration control of semi-active suspension considering non-Newtonian fluid property by optimal preview control of MLD model.

Vibration control of semi-active suspension considering non-Newtonian fluid property by optimal preview control of MLD model.

Semi-active vibration control of semi-active suspensions with the optimal preview control for the MLD model.

Semi-active vibration control of semi-active suspensions with the optimal preview control for the MLD model.

Preview Tracking Control of Linear Periodic Switched Systems with Dwell Time

This paper studies the preview tracking control problem for linear discrete-time periodic switched systems. Firstly, an augmented error system is constructed for each subsystem by stabilizing the augmented error systems through the method of optimal preview control, and the tracking problem of the switched system is transformed into the switched stability problem of closed-loop augmented error systems. Secondly, a switched Lyapunov function method is applied to search the minimal dwell time satisfying the switched stability of the closed-loop augmented error systems. Thirdly, the switched preview control input is solved from the controller of the individual augmented error system, and then the sufficient conditions and the preview controller can be obtained to guarantee the solvability of the original periodic switched preview tracking problem. Finally, numerical simulations show the effectiveness of the stabilization design method.