Control Moment Research Articles

High-Precision Suppression of Harmonic Vibration Force by Repetitive Controller With Triple-Loop Structure

The harmonic vibration force generated by multi-source disturbances through the magnetic bearing closed-loop control system seriously affects the improvement of the performance of the magnetically suspended control moment gyroscope (MSCMG). The harmonic noise introduced by the displacement sensor generates harmonic components through a closed-loop control system, and the compensation accuracy of synchronous displacement stiffness force is reduced by the low-pass characteristics of the power amplifier. In this paper, the rotor displacement signal and current signal are used as inputs to the repetitive controller and notch filter, and the power amplifier and repetitive controller are constructed into three closed-loop systems to achieve high-precision suppression of harmonic vibration forces. Finally, the effectiveness of the method is demonstrated by using a 200Nms MSCMG and a micro-vibration test system.

A Dynamicly Consistent Model of Normal Reactions at Points of a Mobile Platform Contact with a Surface Taking Account of the Design of Mecanum Wheels and Multicomponent Friction

The article investigates the influence of the dependence of normal reactions on motion parameters on the dynamics of a mobile platform by taking into account the design of mecanum wheels and multicomponent friction. To describe the dependence of normal reactions on motion parameters, the theorems on the change in momentum and angular momentum written for the mecanum platform are used. The influence of normal reactions on the dynamics of the mecanum platform is estimated from the results of numerical simulation. The mecanum platform dynamics model takes into account the design of the mecanum wheels and multicomponent friction. The multicomponent friction model proposed by V.F. Zhuravlev that takes into account sliding and spinning is considered. Estimates of the maximum deviations of the normal reactions of the supports, due to the dynamics of the mecanum platform, from the values of the normal reactions calculated for the mobile platform at rest (equal to 16.7% for KUKA youBot robot) are given. Inequalities that limit the maximum values of the control moments, under which the contacting rollers of the mecanum wheels do not come off from the supporting surface are obtained. Based on the simulation results, it is shown that the normal responses are changed by 5–6% of the normal response value calculated in the case of the mecanum platform at rest, which corresponds to the obtained estimates. These changes in normal reactions can lead to a decrease in the accuracy of the movement of the mecanum platform obtained with program control.

Source Quantitative Identification by Reference-Based Cubic Blind Deconvolution Algorithm

The semi-blind deconvolution algorithm improves the separation accuracy by introducing reference information. However, the separation performance depends largely on the construction of reference signals. To improve the robustness of the semi-blind deconvolution algorithm to the reference signals and the convergence speed, the reference-based cubic blind deconvolution algorithm is proposed in this paper. The proposed algorithm can be combined with the contribution evaluation to provide trustworthy guidance for suppressing satellite micro-vibration. The normalized reference-based cubic contrast function is proposed and the validity of the new contrast function is theoretically proved. By deriving the optimal step size of gradient iteration under the new contrast function, we propose an efficient adaptive step optimization method. Furthermore, the contribution evaluation method based on vector projection is presented to implement the source contribution evaluation. Numerical simulation analysis is carried out to validate the availability and superiority of this method. Further tests given by the simulated satellite experiment and satellite ground experiment also confirm the effectiveness. The signals of control moment gyroscope and flywheel were extracted, respectively, and the contribution evaluation of vibration sources to the sensitive load area was realized. This research proposes a more accurate and robust algorithm for the source separation and provides an effective tool for the quantitative identification of the mechanical vibration sources.

Dynamic Analysis and Experiment of Multiple Variable Sweep Wings on a Tandem-Wing MAV

The current morphing technologies are mostly regarded as auxiliary tools, providing additional control torques to enhance the flight maneuverability of unmanned aerial vehicles (UAVs), and they cannot exist independently of the traditional control surfaces. In this paper, we propose a tandem-wing micro aerial vehicle (MAV) with multiple variable-sweep wings, which can reduce the additional inertia forces and moments and weaken the dynamic coupling between longitudinal and lateral motion while the MAV morphs symmetrically for pitch control or asymmetrically for roll control, thereby flying without the traditional aileron and elevator. First, load experiments were conducted on the MAV to verify the structural strength of the multiple variable sweep wings, and the control moments caused by the morphing of the MAV were presented through numerical simulations. Then, the effects caused by symmetric and asymmetric morphing were investigated via dynamic response simulations based on the Kane dynamic model of the MAV, and the generated additional inertia forces and moments were also analyzed during morphing. Finally, dynamic response experiments and open-loop flight experiments were conducted. The experimental results demonstrated that the morphing mode in this study could weaken the coupling between the longitudinal and lateral dynamics and that it was feasible for attitude control without the traditional aileron and elevator while flying.

Research on Yaw Stability Control Strategy for Distributed Drive Electric Trucks.

With the advancement of vehicle electrification and intelligence, distributed drive electric trucks have emerged as the preferred choice for heavy-duty electric trucks. However, the control of yaw stability remains a significant issue. To tackle this concern, this study introduces a layered control strategy for yaw moment. Specifically, the upper layer utilizes a yaw moment controller based on linear quadratic regulator (LQR) to compute the additional yaw moment required. Additionally, in order to enhance the performance of the yaw moment controller, the weight matrix in LQR is optimized using a hybrid Genetic Algorithm and Particle Swarm Optimization algorithm (GA-PSO). The lower layer consists of a torque distribution layer, which establishes an objective function for minimizing tire utilization rate. Quadratic Programming algorithm is then employed to compute the optimal torque distribution value, thereby improving the vehicle's stability. Subsequently, the stability control effects of the vehicle are simulated and compared on the Matlab/Simulink Trucksim joint simulation platform using four control strategies: the proposed control strategy, SMC, LQR, and without yaw moment control. These simulations are conducted under two working conditions: serpentine and double lane change. The results demonstrate that the proposed approach reduces the average yaw rate by 14.4%, 19.6%, and 42.15% while optimizing the average sideslip angle by 25.9%, 24.8%, and 52.3% in comparison to the other three control strategies. Consequently, the proposed control strategy significantly enhances the driving stability of the vehicle. Furthermore, the optimized allocation method reduces the average tire utilization rate by 42.6% in contrast to the average allocation method, thereby improving the stability control margin of the vehicle. These findings successfully validate the efficiency of the yaw stability control strategy presented in this article.

Research on Path Tracking and Yaw Stability Coordination Control Strategy for Four-Wheel Independent Drive Electric Trucks

Achieving accurate path tracking and vehicle stability control for four-wheel independent drive electric trucks under complex driving conditions, such as high speed and low adhesion, remains a major challenge in current research. Poor coordination control may cause the vehicle to deviate from its intended path and become unstable. To address this issue, this article proposes a coordinated control strategy consisting of a three-layer control framework. In the upper layer controller design, establish a linear quadratic regulator (LQR) path tracking controller to ensure precise steering control by eliminating steady-state errors through feedforward control. The middle layer controller utilizes the fractional order sliding mode control (FOSMC) yaw moment controller to calculate the additional yaw moment based on the steering angle of the upper input, utilizing the error of yaw rate and sideslip angle as the state variable. To collectively optimize the control system, establish a coordinated optimization objective function and utilize the hybrid genetic-particle swarm optimization algorithm (GA-PSO) to optimize the weight coefficient of LQR and sliding mode parameters of FOSMC, effectively improving the performance of the controller. In the lower layer torque distribution controller, use the quadratic programming method to achieve real-time optimal torque distribution based on tire utilization, which improves vehicle stability control. Through simulations conducted under four different working conditions, the proposed control scheme demonstrates a 15.54% to 23.17% improvement in tracking performance and a 10.83% to 23.88% optimization in vehicle driving stability compared to other control methods. This scheme provides a theoretical reference for path tracking and stability control in four-wheel independent drive electric trucks.

Bending Moment Control and Weight Optimization in Space Structures by Adding Extra Members in the Optimal Locations

Bending Moment Control and Weight Optimization in Space Structures by Adding Extra Members in the Optimal Locations

Active longitudinal motion control research of waterjet-propelled trimaran in random waves using direct CFD approach integrated with auto-control algorithm

The current numerical method for ship motion control primarily relies on mathematical ship models that require solving hydrodynamic derivatives. In this paper, a fully nonlinear viscous computational fluid dynamics (CFD) model integrated with motion control strategies on an appendage was proposed to directly investigate the longitudinal motion control problem in random waves. The longitudinal motion control model was established on a free-running waterjet-propelled trimaran CFD model by combining a speed controller on the propulsor and attitude motion controller on the stern flap appendage. Through direct CFD simulations in random waves, the motion control capability of the stern flap under different control strategies, including fixed, linear, and PID controllers, was evaluated. The results show that the PID controller is superior and achieves a mean trough reduction of the pitch and heave by 22.3% and 15.6%, respectively. Subsequently, the longitudinal motion control capability of the stern flap under different lengths, traveling speeds, and sea states was numerically researched. The results indicate that as the speed and sea state increase, the pitch control effects of the stern flap on the trimaran appear to be saturated and mainly affected by the limited motion control force and moment. Thus by using joint control with other appendages, better longitudinal motion stability could be achieved. The proposed model provides a direct evaluation method for controlling ship motion by appendages in waves and accounts for complex hull shapes, nonlinear environments, and real-time hydrodynamic feedback.

Dynamic Analysis of Flexoelectric Actuated Truncated Conical Shells

Conical shells are widely used in aerospace engineering, such as for rocket fairings, satellite-rocket joints, load-bearing structures of solid rocket motor cases, and so on. In this study, the truncated conical shell with a flexoelectric actuator was discussed, and the precision flexoelectric actuation and control effectiveness were evaluated. A flexoelectric patch covered with an electrode layer was attached to the external surface of an elastic truncated conical shell, and a line electrode was longitudinally attached to the external surface of the patch. When a control voltage was applied to the line electrode and the electrode layer, an inhomogeneous electric field was generated; consequently, the stresses were induced in the flexoelectric patch due to the converse flexoelectric effect, thus regulating the vibration of the conical shell. The dynamic model of the truncated conical shell with a flexoelectric actuator was established. Then, the actuation effects of the flexoelectric actuator on the conical shell were investigated in terms of the microscopic behaviors, that is, flexoelectric-induced stresses, membrane control forces, bending control moments, and modal forces. Furthermore, the influences of several design parameters (that is, the line-electrode radius, conical shell thickness, flexoelectric patch thickness, and conical shell half-apex angle) on the flexoelectric actuation behaviors were also investigated.

Vehicle stability control strategy for high-speed curves based on mode switching

This study aims to design a mode-switching control system based on a non-singular fast terminal sliding mode (NFTSM) algorithm for the lateral stability problem of vehicles driving in high-speed curves. First, a three-degrees-of-freedom model of the vehicle was established, and a predicted lateral load transfer ratio (PLTR) was proposed as a threshold for dividing the lateral stability index based on the conventional PLTR. Second, for the vehicle driving state in which the vehicle does not satisfy the PLTR threshold, the vehicle lateral deflection angle is adjusted based on the NFTSM controller to improve the bending path-tracking accuracy of the vehicle. For the vehicle critical rollover state that satisfies the PLTR threshold, an NFTSM-based additional yaw moment control algorithm is proposed to estimate various unknown disturbance and parameter ingestion terms in the vehicle modeling process using an adaptive radial basis function neural network and adaptively adjust the key parameters of the NFTSM controller. Finally, a joint Carsim-Simulink simulation model was built to verify the control algorithm proposed herein. The simulation results show that the mode-switching control system can improve the vehicle’s yaw and rollover stability in high-speed curves and prevent the occurrence of rollover.

Mathematical modeling of movement of multi-rotor type aircraft

The problem of determining the features and setting the problem of mathematical modeling of multirotor aircraft (LA) is considered. Differences of their mathematical models from the classical mathematical models of aircraft and single-rotor helicopter type aircraft are considered. The analysis and substantiation of the forces and moments acting on a multirotor aircraft are carried out, taking into account the peculiarities of considering the corresponding coordinate systems necessary for studying the spatial motion of the aircraft. The problem of controlling the trajectory motion of an aircraft is formulated taking into account the rotation of its structure around the center of mass. Based on the consideration of the structural diagram of one of the most common four-rotor aircraft (quadcopter), a scheme for creating control forces and moments acting on the aircraft, under the influence of which the aircraft’s trajectory in space changes, is substantiated. The main mathematical dependences characterizing the kinematics of the motion of a multi-rotor aircraft are given. On the basis of the analysis carried out, a generalized block diagram of the control process for such an aircraft is substantiated and presented. To test the performance and adequacy of the mathematical model, a study was made of the movement of a quadrocopter in a vertical plane between given points in space in accordance with the law of forced control, which ensures the movement of an aircraft in space with maximum speed. The computer simulation of the obtained analytically mathematical dependences showed that this approach is applicable to the construction of mathematical models of the motion of multirotor type aircraft of various design layouts.

Feedback Linearization and Robust Control for Whirl Mode With Operating Point Deviation in Active Magnetic Bearings-Rotor System

The whirl mode, seriously affecting the stability of active magnetic bearings (AMBs), is a hot issue in the AMBs-rotor system. This article stresses a whirl mode problem accompanied by the operating point deviation when the magnetically suspended control moment gyro outputs torque. A feedback linearization controller is proposed to decouple the whirl mode effects from the system state-space representation. A robust controller for uncertain systems is also used to solve the operating point deviation problem. First, the AMBs-rotor system's state-space representation containing the whirl mode terms is described. Next, a system uncertainty caused by the operating point deviation is discussed. Then, a feedback linearization controller combined with a robust controller for uncertain systems is designed. Finally, simulations and experiments for validity verification are presented, and comparative experiments with a PID controller and a robust controller are shown and compared in this article.

Composite Control for Gimbal Systems With Multiple Disturbances: Analysis, Design, and Experiment

The proportional-integral (PI) controller has proven to be a remarkably effective control strategy in the servo field. However, it is difficult for PI to handle the problem of multiple disturbances degrading the velocity-tracking capability of the gimbal system in the control moment gyro (CMG). In this article, we propose a solution to this problem while preserving the advantages of PI from an engineering standpoint. Starting from the refined disturbance analysis based on a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5$</tex-math> </inline-formula> Nms CMG gimbal system experimental tests, multiple disturbances of the gimbal system can be classified according to frequency distribution in terms of 0 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim$</tex-math> </inline-formula> 5 and 128 Hz. Subsequently, under the composite hierarchical anti-disturbance control frame, a peak filter and a disturbance observer addition improve the anti-disturbance capability of the PI-based gimbal system at the frequencies of 0 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim$</tex-math> </inline-formula> 5 and 128 Hz. Notably, the proposed method has a relatively low order, which is appropriate for the processing power limit. Finally, the experimental results reveal that compared to three effective control schemes (PI, extended state observer-based composite controller, and PI-resonant controller), the proposed method can strikingly enhance the anti-disturbance ability and velocity-tracking performance of the gimbal system.

Study on combined stress failure envelope of CMG based on PSO-BP neural network

The investigation of the failure envelope of control moment gyroscopes (CMGs) under simulated dynamic and thermal vacuum conditions on the ground is crucial for ensuring the reliability of CMGs. In this study, a test rig is employed to simulate the combined stresses in a vacuum environment, including the temperature, CMG gimbal rotating speed, and satellite rotating speed. The objective is to obtain high-fidelity running status data of the CMG. The particle swarm optimization and BP neural network (PSO-BP) model is utilized to learn from these test data and subsequently predict the running status for other stress combinations, ultimately enabling the determination of the failure envelope surface of the CMG. The results demonstrate that this method significantly reduces the cost of testing to detect the CMG failure envelope while achieving high prediction accuracy and adaptability under combined stress situations. By employing the k-fold cross-validation method, the PSO-BP model demonstrates superior generalization performance to the BP model in predicting the running status of CMGs. The optimized hidden layer size and learning rate of the PSO-BP model are also discussed. Furthermore, the method presented in this paper can effectively incorporate the hidden experience data from the test. Finally, the PSO-BP method, in comparison to the BP method, yields a smooth and stable initial predicted value, effectively preventing the prediction result from falling into local optimization.

Yaw stability regulation of electric vehicles based on model predictive control

The yaw stability control system contributes significantly to the vehicle's lateral dynamics in order to enhance the vehicle's handling and stability. Controlling the lateral stability of a vehicle requires maintaining the proper yaw rate and side slip angle. For this purpose, a Model Predictive Control-based electric vehicle is built along with a stability control system. With a 2DOF vehicle model and a single track, the desired yaw rate is obtained. The controller is created using the Model Predictive Control (MPC) method. This controller modifies the steering angles of the front wheels and generates the necessary control moment for stabilizing the vehicle's yaw path. The stability control system and the developed controller are evaluated using a nonlinear single-track vehicle model, and the simulation results demonstrate improved vehicle performance and a good yaw rate compared to the vehicle's state without the controller.

Coordinated control of AFS and DYC for electric vehicles with mechanical elastic electric wheels considering the roll effects and uncertain parameters

In order to improve the lateral stability of in-wheel motors electric vehicles (IMEV) equipped with mechanical elastic electric wheels (MEEW) under extreme conditions, this paper proposes an integral sliding mode control (ISMC) algorithm based on the coordination of active front steering (AFS) and direct yaw moment control (DYC) considering the roll effects and the uncertainty of tire cornering stiffness. Firstly, the AFS control algorithm based on adaptive integral terminal sliding mode is proposed, which uses radial-basis-function neural network (RBFNN) to adaptively approximate the integrated nonlinear unknown disturbance. Secondly, considering the roll effects and uncertain cornering stiffness, a mismatched uncertain system for integrated control of AFS and DYC is established. An integral sliding mode control algorithm based on linear matrix inequality (LMI) is designed, and the asymptotic stability of sliding mode dynamics is proved. Further, a constraint optimization algorithm is used to distribute the additional yaw moment. Then, a coordinated control strategy based on the elliptical stable region of the phase plane and the rollover index is designed to activate the integrated controller of AFS and DYC at the right time. Finally, the effectiveness of the control algorithm is verified by high-fidelity CarSim-Matlab simulations. The results show that the proposed controller can effectively and robustly ensure the vehicle lateral stability under extreme conditions.

Research on a Multimode Adaptive Cruise Control Strategy with Emergency Lane-Changing Function

In emergency situations, it is difficult to meet the requirements of safe driving only by relying on the braking system, and the probability of accidents can be reduced by employing an emergency lane-changing mode. To improve the adaptability of the distributed electric vehicle adaptive cruise control (ACC) strategy to complicated and volatile conditions, a multimode ACC strategy with emergency lane-changing function is proposed. Firstly, the ACC is divided into four modes aimed at the problem of complex conditions, and a switching strategy is designed to control the switching of them. Simultaneously, the car-following mode is divided in greater detail based on time to collision (TTC), and the acceleration weighted average algorithm is adopted for accuracy and output continuity during switching. Then, the ACC is established with a hierarchical control framework, in which a PID-based cruise mode and a multi-objective optimized car-following mode based on model predictive control (MPC) are devised. The target brake wheel cylinder pressure is selected as the emergency brake pressure in takeover mode. In addition to the MPC-based system, the emergency lane-changing mode incorporates a yaw moment controller in the upper-level controller to improve body stability during emergency lane changing in the upper-level controller. In the lower-level controller, the upper-level output is converted into driving torque, wheel cylinder pressure, and front wheel angle to control vehicle travel and generate additional yaw moment. Finally, the results indicate that the presented multimode switching strategy can adapt to complex and instable transportation environments. In the cruise control scenario, the host vehicle can rapidly reach cruising speed within 5 s. In the car-following scenario, the host vehicle can stably follow the preceding vehicle with an acceleration of −5–3.5 m/s2 and a jerk of −2–2 m/s3 throughout the entire process, maintaining a safe distance from the preceding vehicle. In emergency lane-changing scenarios, vehicles with body stability control can better follow the lane-changing trajectory, and tracking accuracy is improved by 65%. Simultaneously, parameters such as front wheel angle, yaw rate, sideslip rate, and lateral acceleration remain within the normal range. In mixed switching scenarios, each mode can be correctly switched according to diverse operating conditions, and obstacle avoidance can be accomplished through horizontal and vertical strategies, which also verify the effectiveness and rationality of the control strategy proposed.

Performance Prediction of a 4WD High-Performance Electric Vehicle Using a Model-Based Torque-Vectoring Approach

Electric vehicles (EVs) enable the integration of powertrains with multiple motors, allowing for the adjustment of torque delivered to each wheel. This approach permits the implementation of torque vectoring techniques (TV) to enhance the vehicle’s stability and cornering response, providing better control of yaw moments. This study utilizes comprehensive telemetry data and an advanced simulator model to assess the influence of torque vectoring (TV) on a Formula SAE (Society of Automotive Engineers) competition vehicle. The telemetry data were collected from a fully instrumented 2WD car, which was then employed to calibrate the simulation model. The calibrated model was subsequently utilized to predict the performance enhancements that could be achieved by implementing a 4WD system. The methodology proved to be a valuable contribution to vehicle design development. This approach also helps evaluate the potential advantages of torque vectoring for drivers with limited experience.

A yaw stability-guaranteed hierarchical coordination control strategy for four-wheel drive electric vehicles using an unscented Kalman filter

A novel hierarchical coordination control strategy (HCCS) is offered to guarantee the stability of four-wheel drive electric vehicles (4WD-EVs) combining the Unscented Kalman filter (UKF). First, a dynamics model of the 4WD-EVs is established, and the UKF-based estimator of sideslip angle and yaw rate is constructed concurrently. Second, the equivalent cornering stiffness coefficients are jointly estimated to consider the impact of vehicle uncertainty parameters on the estimator design. Afterwards, a HCCS with two-level controller is presented. The sideslip angle and yaw rate are controlled by an adaptive backstepping-based yaw moment controller, and the computational burden is relieved by an improved adaptive neural dynamic surface control technology in the upper-level controller. Simultaneously, the optimal torque distribution controller of hub motors is developed to optimize the adhesion utilization ratio of tire in the lower-level controller. Finally, the proposed HCCS has shown effective improvement in the trajectory tracking capability and yaw stability of the 4WD-EVs under various maneuver conditions compared with the traditional Luenberger observer-based and the federal-cubature Kalman filter-based hierarchical controller.

Robust Preview Path Tracking Control of Autonomous Vehicles Under Time-Varying System Delays and Saturation

Realistic uncertainties and disturbances cannot be ignored when striving towards high-performance autonomous driving. Meanwhile, advanced vehicular sensing technology enables more and more self-driving techniques to come out. Against this backdrop, this paper aims to holistically address the system uncertainties and disturbances in the path tracking control of autonomous vehicles (AVs) by the preview control theory to achieve superior control performance. First, unmeasureable multi-uncertainties and the external disturbance are considered simultaneously into the robust gain-scheduling framework with variable longitudinal velocity. Then, by the equivalent delay approach, ineluctable time-varying system delays are effectively handled. Next, the physical constraints of the steering system and direct yaw moment control (DYC) system are considered for practical implementation through robust set invariance theory. The parallel distributed compensation (PDC) technique and auxiliary feedback matrices approach are introduced for reducing design conservatism. After that, by incorporating the road curvature into the augmented system state, the path tracking preview control problem can be solved without online optimization. Thereafter, the closed-loop system stability and guaranteed performance have been proved through the Lyapunov- Krasovski approach. The proposed controller design is finally reformulated as the optimization problem based on linear matrix inequalities (LMIs) via specific convexification techniques. Finally, to verify the validity of the proposed controller, Software in Loop (SIL) tests with Carsim full-vehicle model under different dynamic conditions have been implemented.