65 results about "Singular perturbation" patented technology

The invention discloses a T-S fuzzy and TD3-based slow dynamic unknown coal-fired power generation system coordination control method, which comprises the following steps: firstly, decomposing a coal-fired power generation system into a fast subsystem and a slow subsystem by using a singular perturbation theory, and decomposing an original control task into a stabilization task of the fast subsystem and a tracking task of the slow subsystem; for the fast subsystem, a fuzzy set is selected on a definition domain of the fast subsystem by using the slow variation characteristic of steam pressure, and a T-S fuzzy model is constructed; a fast subsystem controller is obtained by solving algebraic Riccati equations corresponding to a plurality of linear systems. For a slow subsystem, a double-Q network is adopted to reduce overestimation of a Q value, a dynamic learning rate and a batch size adjustment mechanism are introduced to accelerate training convergence, and learning is carried out under a TD3 algorithm framework to obtain control input of the slow subsystem. The fast subsystem and the slow subsystem control inputs to be combined and then act on the original system to obtain state information at the next moment, and the intelligent agent interacts with the original system to complete collaborative optimization.

The invention discloses a sensorless control method and system based on fast and slow time scale characteristics, and belongs to the technical field of permanent magnet synchronous motor control. Firstly, a basic dynamic equation of the surface-mounted permanent magnet synchronous motor on a two-phase synchronous rotation orthogonal coordinate system is deduced, and a simplified mathematical model based on fast and slow time scale characteristics is obtained. Then, a novel sliding-mode observer is constructed and used for estimating the position and speed information of the servo system in real time, and therefore non-sensing control is achieved. And finally, based on a singular perturbation theory, respectively carrying out Lyapunov stability analysis in a fast time scale and a slow time scale so as to obtain a gain determination method of the sliding-mode observer. Based on the fast and slow time scale characteristics of the permanent magnet synchronous motor, a novel modeling method and a sensorless control technology are provided, and estimated values of the position and the speed are used in a servo driving system.

The invention discloses a new energy grid-connected system transient stability evaluation method based on a normal type, and relates to the technical field of stable operation of a power system, and the method comprises the steps: building a full-order model of a new energy equipment grid-connected system, and dividing fast and slow variables according to the time constant of each state variable; performing perturbation on the full-order model based on a singular perturbation method to obtain a singular perturbation system, and obtaining a simplified model of the new energy equipment grid-connected system based on the singular perturbation system; performing linear and nonlinear transformation on the simplified model based on a regular method to obtain stability manifold estimation near a dominant unstable equilibrium point of the system, and further obtaining stability domain boundary estimation; and the transient stability of the system is judged by comparing the fault trajectory after the fault is removed with the stability domain boundary estimation. According to the method, the full-process automation from system modeling to stability judgment is realized, the accuracy and efficiency of evaluation are improved, and the complexity and nonlinear characteristics of the new energy equipment grid-connected system can be effectively processed.

The invention discloses a cross-scale decomposition and control method for an electro-hydraulic servo macro-micro rigid-flexible multi-body system, and belongs to the technical field of intelligent control. The invention aims to research a cross-scale decomposition and control method for an electro-hydraulic servo macro-micro rigid-flexible multi-body system based on a trajectory tracking control law of self-adaptive dynamic programming and a three-parameter four-scale singular perturbation decoupling problem for the electro-hydraulic servo macro-micro rigid-flexible multi-body system with uncertainty and variable load. The dynamics of the electro-hydraulic servo macro-micro rigid-flexible multi-body system mechanical arm is decomposed for three times, and then a slow-varying subsystem rigid motion controller, a secondary slow-varying subsystem flexible vibration controller, a secondary fast-varying subsystem micro mechanical arm controller and a fast-varying subsystem hydraulic controller are designed; and finally, combining all the controllers according to a multi-time scale theory to obtain a combined controller. According to the invention, a complex system can be greatly simplified, and the respective design of the controller is more practical.

The invention discloses a singular perturbation-based preset performance control method for a multi-motor driving system, which realizes effective control on slow and fast states and ensures the performance and stability of the system in the presence of singular perturbation. The method comprises the following steps: carrying out dynamic modeling on a multi-motor driving system, and decomposing a dynamic equation of the system into a fast subsystem and a slow subsystem by adopting a singular perturbation system; converting the tracking error by using a preset performance boundary, and obtaining a dynamic equation of the conversion error; the method comprises the following steps: converting an original limited tracking error into an unlimited tracking error through error conversion, converting an original limited tracking control problem into an unlimited tracking control problem, and ensuring that the converted tracking error is bounded, so that the original tracking error is kept within a preset performance boundary; designing a sliding mode surface and a disturbance observer, and estimating external disturbance suffered by the system; and designing a sliding mode controller, and performing compensation control by using the estimated external disturbance.

The invention relates to the technical field of intelligent fire stations, in particular to an intelligent modularized prefabricated cabin type tunnel fire station terminal dynamic early warning system based on edge calculation and multi-source perception. Comprising a multi-source data acquisition unit; the edge calculation processing unit is used for performing real-time fusion and dynamic modeling on the acquired data, fusing multi-source data based on Kalman filtering and LSTM, and constructing a risk evolution model with fast and slow dynamic components by adopting a singular perturbation theory; an intelligent early warning analysis unit; and a data management unit. According to the method, a multi-scale feature extraction framework is constructed by adopting parallel GRU branches through an improved long-short-term memory network: a short-term branch captures second-level rapid features such as flame flicker and sudden change of smoke concentration, and a long-term branch captures minute-level slow change trends such as temperature gradient rise; dynamic weights are given to different time scale features in combination with an adaptive attention mechanism; the design improves the pertinence and accuracy of feature extraction in a complex scene.

The application discloses a kind of based on multi-scale control technology's unmanned system cooperation privacy protection method, belong to unmanned system safety cooperation control field.This method solves the ill-conditioned numerical problem in the cooperative control of unmanned system with fast-slow coupling characteristics, and the state privacy leakage problem caused by communication eavesdropping.Scheme includes: establishing system dynamic model;Design the differential privacy leader-following multi-scale cooperative controller containing attenuation noise;Obtain the standard singular perturbation form of error system through matrix transformation;Based on multi-scale control theory, solve the state control gain that guarantees mean square consistency;Determine the noise control gain and privacy parameter in combination with probability theory and matrix spectral theory.The present application avoids the ill-conditioned numerical problem, and the controller is robust, while achieving accurate cooperative control, provides verifiable differential privacy protection for the initial state of the system, significantly improves the safety and reliability of unmanned system in the hostile environment.

This invention discloses an asynchronous, non-fragile H∞ control method and system for a rotating inverted pendulum, belonging to the field of robust control technology for DC motor rotating inverted pendulums. Addressing the problems of modal asynchrony, controller gain perturbation, time-varying delay, and quantization error in DC motor rotating inverted pendulum systems, this invention proposes an asynchronous, non-fragile H∞ control framework. The main steps include: constructing a Markov jump singular perturbation system model with time-varying delay and variable quantization density; designing an asynchronous, non-fragile output feedback controller for the rotating inverted pendulum, compatible with controller gain perturbation and quantization uncertainty; and deriving the linear matrix inequality conditions through modal-dependent Lyapunov functions to ensure the system's exponential stability in finite time and to meet the preset H∞ performance index. This invention can effectively improve the robustness and stability of DC motor rotating inverted pendulums under attack and parameter perturbation scenarios.

The invention discloses a vulnerability analysis method of a multi-time-scale micro-grid under FDI attack, which comprises the following steps: decomposing system dynamics of a direct current micro-grid into a fast boundary layer subsystem and a slow order reduction subsystem by using a singular perturbation theory, respectively deducing stability conditions of each subsystem under a false data injection attack condition, and analyzing the vulnerability of the multi-time-scale micro-grid under the false data injection attack condition; and the input state stability of the low-speed subsystem under the FDI attack is proved. The worst deviation of the system state under the attack condition is calculated by constructing a zootope reachable set analysis framework, and the maximum allowable attack amplitude capable of ensuring safe operation of the system is quantified. According to the method, through numerical simulation and hardware experiment verification, a novel attack surface introduced by multi-time scale characteristics can be effectively revealed, and theoretical support and guidance are provided for safe and stable control of the DC micro-grid.

The invention provides a high-temperature superconducting magnet temperature equalization control method for circulating cold helium, and belongs to the field of superconducting magnet temperature control, and the method comprises the steps: calculating a difference value between a target temperature and an actual measurement temperature, and obtaining a steady-state error of each group of magnets; constructing a mathematical model of the cold helium cooling system according to a singular perturbation theory; a control target is determined, and the overall performance and stability of the cold helium cooling system are improved; and constructing a singular perturbation control model based on the variable universe Smith. According to the invention, the response speed and precision of the cold helium cooling system can be improved.

This invention provides a temperature balancing control method for high-temperature superconducting magnets using circulating cold helium gas. This method, which belongs to the field of superconducting magnet temperature control, includes: calculating the difference between the target temperature and the actual measured temperature to obtain the steady-state error for each magnet group; constructing a mathematical model of the cold helium cooling system based on singular perturbation theory; clarifying control objectives to improve the overall performance and stability of the cold helium cooling system; and constructing a singular perturbation control model based on the variable domain Smith. This invention can improve the response speed and accuracy of the cold helium cooling system.

The present invention discloses a method for constructing a diode circuit controller, a circuit control method, a device, and a device, belonging to the technical field of automatic control. The method for constructing the controller includes: establishing a partially unknown fuzzy Markov switching singular perturbation model of the diode circuit; establishing an event trigger mechanism for collecting the voltage and current of the diode circuit; selecting a fuzzy control law for the diode circuit under the conditions of singular perturbation and the event trigger mechanism; and establishing a feedback control system for the diode circuit based on the fuzzy control law and the partially unknown fuzzy Markov switching singular perturbation model. The technical solution disclosed in an embodiment of the present invention can effectively reduce the influence of singular perturbation parameters and communication burden on the multi-modal tunnel diode circuit system, solve the fuzzy control problem of the multi-modal tunnel diode circuit system, and improve the performance of the multi-modal tunnel diode circuit system.

The invention discloses a flexible connecting rod mechanical arm fixed time adaptive neural network compound control method which comprises the following steps: decomposing a flexible connecting rod mechanical arm model into a slow subsystem and a fast subsystem based on a singular perturbation theory, and defining a slow subsystem state variable and a fast subsystem state variable in the decomposition process; and the slow subsystem comprises an unmodeled dynamic state, and meanwhile, the control moment in the model is decomposed into a tracking controller used for controlling the slow subsystem and a flexible suppression controller used for controlling the fast subsystem. According to the technical scheme, elastic vibration of the flexible mode is relieved, meanwhile, tracking of the time-varying expected position by the mechanical arm is achieved, and the mechanical arm is stable within the fixed time.

The application provides a method and system for offshore wind turbine integrated dynamic feature reservation. The full state time series data of the offshore wind turbine multi-physical field coupling system is obtained, the modal parameters of the system are extracted by using a dynamic modal decomposition method, the participation factor matrix is calculated, the participation degree of each state quantity and the corresponding physical link to each mode is quantified, the dominant state quantity of the extracted mode and the corresponding physical link are located, the dominant state set is constructed, the time scale distribution of each mode is quantified, the time scale distribution characteristic atlas of different physical links of the overall system is formed, the fast time scale, analysis time scale and slow time scale boundaries in the atlas are divided according to different research needs, and the offshore wind turbine integrated reduced order model is constructed based on the singular perturbation theory. The application can effectively reserve the dynamic features of the offshore wind turbine multi-field coupling characteristics, and provides a precondition and quantitative basis for physical-based model reduction modeling.

The present invention provides a preset time control method and device for a flexible linkage manipulator, relating to the field of robotics. The method comprises: constructing a dynamic model of the flexible manipulator; performing a dual-time-scale model decomposition of the flexible manipulator dynamic model based on singular perturbation theory to obtain a slow-varying subsystem and a fast-varying subsystem; designing a preset time trajectory tracking controller based on the slow-varying subsystem using a composite learning algorithm based on a neural network and a disturbance observer, using a time transformation function and a state transformation function; designing a vibration suppression controller based on the fast-varying subsystem by constructing sliding mode variables; and obtaining control inputs for the flexible linkage manipulator based on the preset time trajectory tracking controller and the vibration suppression controller. This method can effectively suppress flexible vibrations and improve the robustness and adaptability of the system.

The present invention discloses a collaborative attitude control and distributed vibration suppression method for ultra-large-scale flexible spacecraft. The method includes the following steps: Step 1, construct a rigid-flexible coupling dynamics model of an ultra-large-scale spacecraft with multiple flexible structures; Step 2, decompose the coupling dynamics model based on singular perturbation theory to obtain slow-varying attitude and fast-varying vibration subsystems respectively; Step 3, use an adaptive switching and input-saturated terminal sliding mode controller to complete high-precision and high-stability attitude control; Step 4, use distributed piezoelectric actuators based on consensus theory and feedback collaborative controllers to complete high-precision and fast vibration suppression. The beneficial effects of the present invention are as follows: precise dimensionality reduction of the ultra-large-scale flexible spacecraft model is achieved; attitude and vibration active controllers are designed relatively independently; the accuracy and stability of attitude control and vibration suppression of the ultra-large-scale flexible spacecraft are improved simultaneously in a shorter time.

The invention discloses a nonlinear power grid dynamic model order reduction method based on high-order polynomial mixed singular perturbation, relates to the field of smart power grids, and can reduce the order of a nonlinear power grid model so as to enable a model which cannot be processed by a stability algorithm due to too high order to be subjected to stability analysis. According to the method, firstly, multivariable high-order Taylor series expansion is carried out on each nonlinear part in a power grid differential equation set model at a balance point, so that a Taylor expansion is close to an original nonlinear expression as much as possible, and then a high-order derivative of a fast state variable in a system is made to be 0. According to the method, under the premise that important dynamic characteristics of the power grid are fully reserved, full order reduction is carried out on the model, and stability analysis of the high-order power grid becomes possible.

The application relates to a flexible mechanical arm control method and system based on a fractional order complementary sliding mode, and the method comprises the following steps: constructing a flexible mechanical arm dynamics model according to mechanical arm related parameters, a hypothetical modal method and a Lagrange method; decoupling the flexible mechanical arm dynamics model according to a singular perturbation method to generate a slow variable subsystem model and a fast variable subsystem model; establishing a super-helix extended state observer according to the slow variable subsystem model and generating an observed disturbance value; generating a fractional order complementary sliding mode control law according to a flexible mechanical arm position tracking error; generating a trajectory tracking control law according to a tracking differentiator, the observed disturbance value and the fractional order complementary sliding mode control law; generating a vibration suppression control law according to the fast variable subsystem model, a super-helix algorithm and a mechanical arm end vibration amount; and combining the trajectory tracking control law and the vibration suppression control law to generate a total control law. The control precision is improved through the fractional order complementary sliding mode, and chattering is effectively suppressed.

The invention belongs to the technical field of power systems, and particularly discloses a singular perturbation-reactive decoupling-based order reduction method and device for a network construction type converter. According to the invention, the original state space model is converted; on the basis of a singular perturbation strategy, order reduction processing is carried out according to target variable characteristics; order reduction processing is carried out on a voltage dynamic equation in a reactive power control outer ring, reactive power decoupling is carried out in the mode that a voltage state variable is introduced into an active power control state equation, and a differential equation based on the phase angle relation is established. Through the above mode, aiming at the electromagnetic dynamic process of the fast subsystem, order reduction processing is carried out on the electromagnetic dynamic state variable based on the singular perturbation strategy, order reduction processing is carried out on the voltage dynamic equation in the reactive power control outer loop, high-order dynamic items are eliminated, main dynamic characteristics in the state space are reserved, the model order and the solving complexity are obviously reduced, and the reliability of the system is improved. Therefore, the model order can be systematically reduced on the premise of ensuring key dynamic characteristics and stability analysis precision.

The present invention discloses a coordinated control method for a slow-dynamic unknown coal-fired power generation system based on T-S fuzzy and TD3, comprising the following steps: first, the coal-fired power generation system is decomposed into a fast subsystem and a slow subsystem using singular perturbation theory, and the original control task is decomposed into a stabilization task of the fast subsystem and a tracking task of the slow subsystem. For the fast subsystem, the slow-varying characteristics of steam pressure are utilized to select fuzzy sets on its definition domain to construct a T-S fuzzy model. The fast subsystem controller is obtained by solving the algebraic Riccati equations corresponding to multiple linear systems. For the slow subsystem, a dual-Q network is used to reduce the overestimation of the Q value, and a dynamic learning rate and batch size adjustment mechanism are introduced to accelerate training convergence. The control input of the slow subsystem is obtained by learning under the TD3 algorithm framework. The control inputs of the fast and slow subsystems are combined and acted on the original system to obtain the state information at the next moment. The intelligent agent interacts with the original system to complete collaborative optimization.

The application relates to the technical field of intelligent fire station, in particular to an intelligent modular prefabricated cabin type tunnel fire station terminal dynamic early warning system based on edge computing and multi-source sensing. The system comprises a multi-source data acquisition unit, an edge computing processing unit, an intelligent early warning analysis unit and a data management unit. The edge computing processing unit is used for real-time fusion and dynamic modeling of the collected data, multi-source data are fused based on Kalman filtering and LSTM, and a risk evolution model with fast and slow dynamic components is constructed by adopting singular perturbation theory. The improved long short-term memory network adopts a parallel GRU branch to construct a multi-scale feature extraction architecture: a short-term branch captures second-level fast features such as flame flickering and smoke concentration sudden change, a long-term branch captures minute-level slow change trends such as temperature gradient rise, and an adaptive attention mechanism is combined to give dynamic weights to different time scale features. The design improves the pertinence and accuracy of feature extraction in complex scenes.

The present invention belongs to the field of industrial Internet and control technology. In view of the phenomenon that continuous dynamic processes, discrete events, and the diversity of controlled objects and multiple time scales coexist in industrial Internet systems, a hybrid singular system modeling and stability analysis method based on multiple sampling rates is proposed to solve the stability analysis and controller design problems of the proposed model, while considering the influence of multiple sampling rates and singular perturbation parameters on system stability. The present invention can achieve the complex characteristics of multiple system couplings such as continuous dynamics, multi-sampling, discrete events and multiple time scales, that is, effectively improve the system operation performance and anti-interference performance. At the same time, the hybrid singular perturbation system model established by the Lyapunov function method is applicable to more complex actual systems and also provides a better compromise between system performance and execution cost, that is, it has strong feasibility, which means that it has good practical application prospects.

The invention provides a time-lag industrial process tracking control method based on inner and outer ring singular perturbation decomposition, and the method comprises the steps: firstly building a cascade singular perturbation time-lag system model for an industrial process containing fast and slow dynamic states and time lag, and decomposing a tracking problem into an optimal tracking problem of an outer ring slow subsystem and an inner ring fast subsystem based on a singular perturbation theory; linear quadratic tracking is converted into an adjustment problem by constructing an auxiliary system, and an optimal controller theoretical form is deduced. The condition that model parameters are difficult to obtain is considered, a data-driven reinforcement learning algorithm is designed for inner and outer ring operation layers, a compact equation least square solution converted by a Riccati equation is solved through input and output data, and an optimal control strategy is learned. And finally, the learned controller is utilized to realize the optimal tracking of the given signal by the system. The method does not depend on system prior information, solves the problem of traditional control in a multi-scale and time-delay coupling system, improves the control precision and response speed of a complex industrial process, and has important application value in the field of production automation.

To overcome the current technical problems of difficulty in analyzing the causes of bridge anomalies and the inability to actively prevent bridge resonance, this invention provides a Fourier method for anomaly reproduction and resonance prevention based on a bridge twin dynamic model. This method relies on the fact that the system matrix related to all bridge nodes and the observation coefficient matrix related to nodes are only related to the bridge parameters and do not change significantly during a single continuous bridge test. The reproduction of bridge anomalies or the simulation of bridge resonance and abnormal loading tests are relatively short. Based on the characteristics of slowly changing matrices, a singular perturbation method is used to set the slowly changing matrix as a constant matrix in a single continuous test. Fourier series approximation is used to approximate the fast-changing variable over time, obtaining the ambiguous excitation input when bridge anomalies occur. The system's reproduction of anomalies is verified through equivalent bridge simulation; thus, the artificial excitation input for simulating bridge resonance or anomalies is obtained, solving the technical problems of difficulty in analyzing the causes of bridge anomalies and the inability to actively prevent bridge resonance.

The application provides an iterative learning impedance control algorithm for a flexible driving exoskeleton, and the algorithm comprises the following steps: a dynamics model of an upper limb rehabilitation robot is established by measuring parameters such as the length, mass and stiffness of a series elastic actuator of an exoskeleton joint; secondly, the model is processed by using singular perturbation theory to simplify the complexity of the system; on this basis, a human-computer interaction control strategy is designed by combining impedance control and iterative learning algorithm, and collaborative rehabilitation training of the machine active and human active modes is realized; finally, the effectiveness of the algorithm is verified through simulation and experiment platform, and the results show that the method can adapt to the flexible driving characteristics, and improve the tracking accuracy and human-computer interaction safety of the exoskeleton.

The invention discloses a motor sliding mode control method and system based on an equivalent input disturbance observer. The method comprises the following steps: firstly, proposing a basic dynamic equation of the surface-mounted permanent magnet synchronous motor on a two-phase synchronous rotation orthogonal coordinate system, introducing a speed tracking error, and establishing a mathematical model of a singular perturbation system; then, an equivalent input disturbance observer is constructed and used for estimating lumped disturbance of the servo system in real time, and convergence conditions of the observer are given; and finally, obtaining the H-infinity performance of the closed-loop system based on the Lyapunov stability theory, and determining the gain matrix of the non-cascade sliding mode controller according to the corresponding linear matrix inequality. The method effectively improves the control performance and the anti-interference capability of the driving system, and is suitable for the design of a high-performance servo control system of the permanent magnet synchronous motor.

The invention discloses a fuzzy adaptive control method for reduced order output of an arm support electro-hydraulic execution system, and belongs to the technical field of arm support electro-hydraulic execution system control. The method comprises the steps that firstly, a full-state model of a system is established for a boom electro-hydraulic execution system, secondly, order reduction processing is carried out based on the singular perturbation theory, and an order reduction model is obtained; then, designing a state observer based on the reduced-order model, and constructing a fuzzy adaptive state observer in combination with a fuzzy adaptive strategy; and finally, designing a fuzzy adaptive sliding mode controller based on the reduced-order model, and combining the fuzzy adaptive sliding mode controller with a fuzzy adaptive state observer to obtain a reduced-order output fuzzy adaptive controller. According to the method, the system state is observed through the fuzzy adaptive state observer, and system adaptive control is carried out, so that the robustness of the system can be effectively improved.

The invention aims to provide a singular perturbation theory-based dual-time-scale optimization control method for a human-micro-grid system, and belongs to the technical field of micro-grid system control. According to the method, a singular perturbation theory is adopted to decompose rapid electrical response and slow consumer motivation evolution of a micro-grid system into different time scales, and targeted optimization control strategies are respectively formulated. According to the method, the quick response capability of the micro-grid under transient disturbance is effectively improved, and meanwhile, long-term optimization under a steady-state condition is ensured, so that the stability and the operation efficiency of the system are remarkably improved. Different from a traditional control strategy, the time does not need to be divided into a plurality of time windows, and therefore the more accurate and efficient control effect is achieved.

The application discloses a kind of distributed time-varying optimal resource allocation methods based on cluster neural dynamics, it is related to distributed control technical field, and the specific steps of this method are as follows: define problem: to N recurrent neural network with set formation a recurrent neural network RNN set, for solving distributed optimal time-varying resource allocation problem, in the problem, non-quadratic local objective function and global equality constraint condition are changed with time, some global information is estimated using dynamic average consensus protocol, a new type of neural dynamics method with double time scale is proposed, and the time-varying optimal resource allocation of given problem is asymptotically tracked in a distributed manner, analysis shows that the proposed neural dynamics model is regarded as singular perturbation system, and converges to time-varying optimal resource configuration by singular perturbation theory, finally, the results are verified by two examples.

A control method for vehicle magnetorheological suspension based on dual-timescale separation includes: establishing an equivalent drive circuit model with time delay; separating the equivalent drive circuit model into two timescales based on singular perturbation theory to construct a fast-changing current subsystem and a slow-changing control subsystem; designing a slow-changing control subsystem controller for the slow-changing control subsystem, calculating the reference current output by the slow-changing control subsystem, and then outputting the reference current to the fast-changing current subsystem; designing a fast-changing current subsystem controller for the fast-changing current subsystem, designing a Smith predictor to compensate for the time delay, and calculating the compensated duty cycle output by the fast-changing current subsystem by combining the reference current and the non-singular reaching law; and outputting the compensated duty cycle to the equivalent drive circuit model to achieve real-time control of the magnetorheological suspension. This invention can effectively handle the multi-source time delay problem of magnetorheological suspension in two timescales, improving vehicle ride comfort and handling stability.