This paper presents an LMI-based discrete PI–P cascade control method for active quarter-car suspension systems. The quarter-car dynamics are reformulated into a modified cascade sampled-data model that retains the body–wheel coupling between the sprung-mass and unsprung-mass dynamics. Based on this structure, a finite-memory discrete PI–P controller is developed, where the primary PI loop regulates the body-side response and the secondary proportional loop shapes the wheel–actuator-side dynamics. A Lyapunov stability condition and a tractable LMI synthesis method are derived for controller gain co-design. Numerical simulations show that the proposed controller improves sprung-mass acceleration attenuation while keeping the suspension deflection, tire-load-related response, and actuator effort bounded. The study is positioned for vertical ride comfort control rather than full-vehicle handling evaluation.

This study presents a robust event-triggered control protocol based on a discrete event-triggered communication scheme (DECS) to resolve vehicle platoon communication topological changes, external disturbances, and information delays. The random variation of the data transmission link among the platoons in real traffic was considered and modeled by the Markov chain combined with the directed graph method. The effects of delays and air resistance on the vehicle platoon were studied using the system parameters, such as external interference and equivalent information delays. To ensure the vehicle platoon's inner-vehicle stability, a variable-gain distributed controller is proposed based on Markovian jumping system stability theory and H control. Finally, the L2 stochastic string stability is defined to attenuate perturbations as they propagate through a platoon. Simulation studies were conducted on a vehicle platoon under four random-switching communication topologies with two different control methods to verify the theoretical results. Compared with traditional robust platoon control, the proposed control method achieves vehicle platoon stability with a lower computational burden.

To meet the rising demand for multi-Gb/s wireless communications in dynamic scenarios, mm-Wave transceivers featuring large output power, high-accuracy beam directivity and wide gain range are essential [1]–[10]. According to the 5G NR and IEEE 802.11ad/ay standards, the phased-array transceivers usually operate in the time-division duplex (TDD) mode by sharing the antenna for both the transmitter (TX) and receiver (RX). However, the large insertion loss and narrow bandwidth of the conventional bulky $\lambda / 4 \mathrm{T} / \mathrm{R}$ switch significantly limit the transceiver performance [1]. To reduce loss and save area, the transformer-based T/R switches have been leveraged, but they always encounter reduced TX-RX isolation and increased TRX co-design difficulties around 60 GHz [2]–[4]. Besides, for precise beam steering and low sidelobes, high-accuracy gain and phase controllers with large range are crucial. Unfortunately, for 60 GHz transceivers, their gain ranges are typically limited to $<20 \text{dB}$ with around $0.4 \text{dB} / 5^{\circ}$ gain/phase errors [2]–[4].

Distributed drive electric vehicles (DDEVs) endow the ability to improve vehicle stability performance through direct yaw-moment control (DYC). However, the nonlinear characteristics pose a great challenge to vehicle dynamics control. For this purpose, this paper studies the DYC through the Takagi-Sugeno (T-S) fuzzy-based model predictive control to deal with the nonlinear challenge. First, a T-S fuzzy-based vehicle dynamics model is established to describe the time-varying tire cornering stiffness and vehicle speeds, and thus the uncertain parameters can be represented by the norm-bounded uncertainties. Then, a robust model predictive control (MPC) is developed to guarantee vehicle handling stability. A feasible solution can be obtained through a set of linear matrix inequalities (LMIs). Finally, the tests are conducted by the Carsim/Simulink joint platform to verify the proposed method. The comparative results show that the proposed strategy can effectively guarantee the vehicle's lateral stability while handling the nonlinear challenge.

In this paper, a hybrid platooning robust stability control method based on the Backward Predictive Multi-Vehicle Following Method (BLAMCFM) is proposed to address the problems that the following models of traditional human-driven vehicles (HDVs) do not take into account the personalized characteristics of real drivers and that it is difficult to achieve the stability of a hybrid vehicle platooning system with the vehicle platooning control method. Firstly, data from various vehicles in front and behind are used as inputs to the HDV. Subsequently, a hybrid platooning system consisting of a self-driving and connected vehicle (CAV) and an HDV is established, considering several unfavorable factors such as data dropout, communication delays, and external disturbances. Secondly, a string stability criterion for the hybrid platooning system is provided in the presence of disturbances and delays, and a stability controller is built based on the Lyapunov-Razumikhin stability theory. In conclusion, it is shown through simulation studies that drivers observing the behavior of multiple vehicles in front and behind them simultaneously can successfully stabilize the traffic flow, and the effectiveness of the controller is confirmed by illustrating the function of CAVs in reducing traffic oscillations.

Distributed drive electric vehicles actuated by in-wheel motors and brake-by-wire systems enable tracking target motion while improving extra vehicle performance. Outboard brake torque allocated on front and rear wheels generates diverse vertically reactive anti-dive forces, providing an innovative approach to mitigate brake dive without requiring active suspensions. However, the differing dynamics of regenerative and hydraulic braking, along with multiple uncertain vehicle parameters, pose significant challenges to achieving robustness under mixed uncertainties. Moreover, pitch-induced bias in onboard acceleration measurements further degrades control accuracy. To address above problems, this paper proposes a robust, comfort-enhanced longitudinal control system with coordinated braking. A three-degree-of-freedom vehicle dynamics model is developed to incorporate the effect of anti-dive forces. For accurate feedback, a robust H-2/H-infinity observer is designed to compensate pitch-variation-related acceleration measurement biases. By integrating dynamic and parametric uncertainties into the control-oriented model, the mixed mu-synthesis is employed to design a two-degree-of-freedom controller to robustly optimize the acceleration tracking and anti-dive performance. Compared to the controller designed by standard mu-synthesis, the proposed approach achieves a 10% improvement in robust performance. Real-vehicle experiments validate the system's effectiveness, demonstrating over a 27% reduction in pitch angle while maintaining satisfactory acceleration responses under blended braking conditions.

This brief presents a 57-71-GHz transmit/receive (T/R) front-end (FE) module for large-scale phased-array transceivers. The FE consists of a T/R switch (TRSW), power amplifier (PA), low-noise amplifier (LNA), phase shifter (PS), variable gain amplifiers (VGAs), and power combiner/divider (PC/PD). To alleviate the RX signal leakage to the TX branch in the traditional asymmetric TRSW structures, a novel switchable triple-coil transformer (STCT) is proposed at the PA output, achieving a broadband high off-impedance for the RX while introducing a low loss to the TX power. In addition, to facilitate large-scale phased-array calibration, both VGA and PS feature the orthogonal amplitude and phase control, leading to low phase and gain errors. Fabricated in a 65-nm CMOS, the proposed FE achieves a peak gain of 28.4/17.3 dB with BW3 dB from 54 to 70/55 to 70 GHz in TX/RX modes. Benefiting from the proposed STCT, the TX achieves an OP1 dB/P-sat of 13.9/15.4 dBm with PAE(1 dB)/PAE(max) of 10.7%/13.7%, respectively. The RX achieves a minimum in-band noise figure (NF) of 6.6 dB at 66 GHz. During the gain tuning, the FE achieves an rms gain/phase error of 0.13 dB/1 degrees and 0.1 dB/1.3 degrees in the TX and RX modes, respectively. The 6-bit phase shifting performs an rms phase/gain error of 0.85 degrees/1.3 dB and 0.8 degrees/1.1 dB in the TX and RX modes, respectively.

This paper proposes a novel trajectory tracking model predictive control (MPC) method leveraging deep neural network-based Koopman operator theory. Specifically, the approach employs a deep-learning-enhanced Extended Dynamic Mode Decomposition (Deep EDMD) framework, integrating encoder-decoder architectures to automatically learn nonlinear lifting and projection functions for vehicle dynamics. The resulting linear representation significantly simplifies control computations. Extensive simulations in a high-fidelity CarSim/Matlab environment demonstrate robust tracking performance under dynamic curvature scenarios, with maximum lateral displacement errors remaining within ISO 3888-2 standards. Comparative analyses reveal that the proposed method substantially outperforms traditional MPC, exhibiting improved stability, tracking accuracy, and adaptability under varying adhesion coefficients and vehicle speeds.

Four-wheel independently driven electric vehicles (FWID-EV) endow a flexible and scalable control framework to improve vehicle performance. This paper integrates the torque vectoring and active suspension system (ASS) to enhance the vehicle’s longitudinal and vertical motion control performance. While the nonlinear characteristic of the tire model leads to a relatively heavier computational burden. To facilitate the controller design and ease the load, a half-vehicle dynamics system is built and simplified to the linear-time-varying (LTV) model. Then a model predictive controller is developed by formulating the objective function by comprehensively considering the safety, energy-saving and comfort requirements. The in-wheel motor efficiency and the power loss of tire slip are treated as optimization indices in this work to reduce energy consumption. Finally, the effectiveness of the proposed controller is verified through the rapid-control-prototype (RCP) test. The results demonstrate the enhancement of the energy-saving as well as comfort on the basis of vehicle stability.

An energy saving strategy and stability control method of heterogeneous intelligent connected platoon system consisted of fuel vehicles and pure electric vehicles were proposed. Firstly, the dynamics model and energy consumption model of heterogeneous vehicle platoon were established considering the factors that weaken the stability of vehicle system, such as uncertain parameters, communication delay and external interference. Secondly, the optimal economic vehicle speed was planned offline based on dynamic programming method, and a new energy-saving spacing strategy was proposed. Secondly, the state tracking error system was constructed. Based on the distributed robust control method, the stability criterion of platoon system under interference and information delay was given. Through the simulation test, it is found that the energy saving effect of following vehicles can be improved by 10.087 %, 10.311 %, 10.355 % and 42.138 % respectively under the premise of keeping the platoon system stable.

This article aims to address the realistic path tracking control problem toward high-system performance for commercial autonomous ground vehicles (AGVs) with simultaneously guaranteeing the tracking accuracy, yaw and roll stability under limited vehicle network resources in global position system temporarily unavailable environments. In such conditions, the vehicle full state information and road topography might not be accessible in real time. To this end, this article proposes an effective adaptive event-trigger (AET)-based robust path tracking control strategy with introducing the reliable Takagi-Sugeno (T-S) fuzzy state observer for practical implementation. First, the vehicle yaw and roll coupled dynamics is incorporated into the vehicle-road system model, with modeling the tire cornering stiffness uncertainty by the T-S fuzzy technique and resolving the system disturbances as unknown inputs. Then, the fuzzy observer structure is established with unmeasurable premise variables which are handled by norm-bound method. Next, a well-designed AET control framework is constructed to reduce the real-time network occupation rate and economize the communication bandwidth resources. Besides, the input constraint and rollover prevention are handled using the robust set invariance. After that, the parallel distributed compensation (PDC) controller and observer are co-designed through solving the effective linear matrix inequalities (LMIs). In addition, the close-loop stability and $H\infty$ performance are ensured by means of the delay dependent Lyapunov-Krasovski method. Finally, the validity and superiority of the proposed control strategy have been verified by Carsim-Simulink co-simulations in different dynamic scenarios with high-fidelity full vehicle model.

This paper presents a 52-67 GHz variable gain phase shifter (VGPS) in a 65-nm CMOS process. The proposed VGPS consists of a 6-bit impedance-invariant vector-sum phase shifter (VSPS) and an 8-bit X-type variable gain amplifier (VGA). For enhanced gain and phase precision, as well as broadband matching, a stacked serpentine coupled-line I/Q generator with three-coil magnetic coupling technique is utilized. The prototype VGPS achieves a 16-dB gain tuning range with a 0.5-dB step. The overall RMS gain error of VGPS is less than 0.15 dB within the 3-dB bandwidth, and less than 0.1 dB from 53 to 66 GHz. The VGPS covers the full 360 degrees with 5.625 degrees(6-bit) phase accuracy, and the RMS phase error is less than 2.4 degrees with a 1.1 degrees minimum error at 64 GHz. The chip consumes 20.8 mA from a 1.2-V power supply with a core area of 850 mu m x240 mu m.

Active steering control is the key technology to improving vehicle safety and driver comfort, and its performance is largely affected by the uncertainties of human-machine interaction. To this end, this paper proposes a robust cooperative game based active steering control strategy considering the human-machine interaction characteristic to ensure the steering stability of the vehicle. The driver-vehicle system is modeled based on the T-S fuzzy structure to describe the uncertainty of the vehicle’s longitudinal velocity. Then the human-machine shared steering control is represented by a dynamic game process, in which the driver and the active steering control system are regarded as the participants. The cooperative game theory is introduced to decide the interaction control level. Such a design can achieve a better overall performance of agents through modeling the human-machine interaction process. Finally, a robust H ∞ compensation control method is proposed to improve the anti-interference performance of the system, thus ensuring vehicle stability. The MATLAB/Carsim joint simulation platform is established to verify the effectiveness of the proposed controller. The results show that the proposed human-machine cooperative control framework can guarantee the dynamic interactive performance while improving the vehicle handling performance. Furthermore, the hardware-in-the-loop (HIL) tests based on the LabVIEW-RT system also prove the feasibility.

In order to solve the problem that driving style has not taken into account in the existing following models of human driving vehicles (HDV) and the random variation of HDV model parameters is rarely considered in mixed platoon control studies, an HDV model considering driver's style is first established, and the style parameters and response delay with large fluctuations are modeled as uncertain parameters. A mixed platoon system based on uncertain HDV model is established. Secondly, a robust controller based on Lyapunov-Krasovskii stability theorem is designed to eliminate the effects of parameter uncertainty, external interference, delay and data loss on the stability of the mixed platoon. Finally, simulation results show that the modeling method and control strategy can effectively achieve the stability of uncertain mixed platoon system.

This paper presents a 57-71GHz low-power variable gain power amplifier (VGPA) in a 65nm CMOS process. The proposed VGPA consists of a current-reused variable gain amplifier (VGA) with large accurate dB-linear gain tuning range and a two-stage power amplifier (PA) with high output power and efficiency. To ease calibration and to improve design time efficiency, a particle swarm optimization algorithm (PSO) is utilized to obtain the width of VGA transistors. In particular, this approach can simultaneously achieve the largest gain tuning range, lowest power consumption and lowest RMS gain error for a fixed number of bits. Over 57-71GHz, the measured gain tuning range is 23.5 dB with 0.5-dB step and a peak gain of 22.5 dB. The RMS phase error is from 1.4 degrees to 2.1 degrees and the RMS gain error is less than 0.09 dB over the 3-dB bandwidth. The minimum RMS gain error of 0.05 dB is achieved at 62GHz, demonstrating the best gain accuracy compared to state-of-the-art VGAs. Moreover, the VGPA achieves a peak saturation power (Psat) of 14.4 dBm and a peak power added efficiency (PAE) of 21.5%.

This paper presents a 39 GHz millimeter-wave (mm-Wave) CMOS variable-gain low-noise amplifier (VGLNA) supporting 5G phased array systems. The VGLNA utilizes a novel current-reused gm-boosting structure by cascading two compact three-winding transformers to simultaneously achieve broadband, high gain, and low noise performance. Furthermore, the VGLNA incorporates two passive transistors for both phase compensation and gain tuning, realizing a low phase error over large gain tuning range. In order to improve design efficiency and achieve superior circuit performance, a computer-aided multi-objective node admittance network synthesis (MONANS) approach is proposed to obtain the optimum design parameters of the proposed VGLNA. Fabricated in a 65-nm CMOS process, the prototype circuit achieves a measured peak gain of 11.3 dB and a minimum noise figure (NF) of 2.9 dB over 37-43.5 GHz. The measured gain tuning range is 14.4 dB with the minimum RMS phase error of only 0.4 degrees. The VGLNA occupies a core chip area of only 0.022 mm(2) and consumes only 11 mA from a 1.8 V supply voltage.

This paper aims to develop an automatic miscalibration detection and correction framework to maintain accurate calibration of LiDAR and camera for autonomous vehicle after the sensor drift. First, a monitoring algorithm that can continuously detect the miscalibration in each frame is designed, leveraging the rotational motion each individual sensor observes. Then, as sensor drift occurs, the projection constraints between visual feature points and LiDAR 3-D points are used to compute the scaled camera motion, which is further utilized to align the drifted LiDAR scan with the camera image. Finally, the proposed method is sufficiently compared with two representative approaches in the online experiments with varying levels of random drift, then the method is further extended to the offline calibration experiment and is demonstrated by a comparison with two existing benchmark methods.

This paper presents a robust finite frequency ${H} _{\infty }$ control strategy for improving vibration performance and ride comfort of electric vehicles through in-wheel motor-active suspension system(IWM-ASS). Since the human body is much sensitive to the vertical vibration of 4 -8 Hz, the main objective is dedicated to deal with the vibration challenge that matches the characteristics of the human body by applying the finite-frequency technique. Firstly, the uncertain quarter-vehicle active suspension model with dynamic damping in-wheel motor driven system is established, in which in-wheel motor is suspended as dynamic vibration absorber(DVA) to isolate the force transmitted to motor bearing in IWM-ASS. Based on the framework of generalized Kalman–Yakubovich–Popov lemma and stability theory, then the performance index of ${H} _{\infty }$ norm from external disturbance to controlled output for IWM-ASS is attenuated within the concerned frequency range while other system requirements such as parameter uncertainty, suspension deflection constraint and actuator saturation are also guaranteed in controller design. The resulting robust finite frequency state feedback ${H} _{\infty }$ controller is finally designed utilizing two new theorems, and solved via a set of linear matrix inequalities. Simulations for frequency-domain and time-domain responses are implemented and compared with the entire frequency control method to evaluate the effectiveness of the proposed strategy. It can be concluded from the results that the developed control strategy can effectively attenuate the negative vibration and enhance ride comfort and road-holding ability for electric vehicles of IWM-ASS.

The traffic light in urban areas dominates the traffic flow, resulting in variation of energy consumption of the vehicles involved. To mitigate the impact of traffic bias on the energy efficiency of electric vehicles (EVs), this article proposes an event-driven energy-efficient driving control (EEDC) strategy based on a receding horizon two-stage control framework, which harnesses the Internet of Vehicles to incorporate the traffic light and preceding vehicle for adaption of different driving scenarios. At the core of the first stage are the vehicle driving event classification rules, which classified the urban traffic scenarios into four events. This article contributes to empirical solutions on the design of the traffic scenario classifier considering conflict goals, including driving efficiency and safety. In the second stage, the speed trajectory in each driving event is optimized using Pontryagin’s minimum principle to reduce vehicle energy consumption. A real-time solution for the energy-efficient driving problem is derived with the consideration of vehicle dynamics, control input, and speed limit constraints. Finally, extensive simulations and road tests are conducted to evaluate the effectiveness of the EEDC. The results show that the EEDC is excellent in energy efficiency improvement over two benchmark strategies in different traffic scenarios while satisfying the constraints in inter-vehicle driving safety and travel time. Moreover, the road tests demonstrate that the EEDC is capable of energy saving in real-world driving.

Acceleration performance, braking performance, handling performance and ride performance are crucial for driving safety of vehicles. The accurate acquisition of tire-road friction coefficient (TRFC) is the key to the stable operation of vehicle chassis control systems. Compared with the direct measurement of TRFC by using expensive sensors, indirect measurement methods combined with vehicle dynamics models and advanced estimation filters are more cost-effective and suitable for severe traffic scenarios. In this paper, a novel limited memory random weighted unscented Kalman filter (LMRWUKF) is proposed to estimate the TRFC under different traffic conditions. First, a nonlinear three-degree-of-freedom vehicle dynamics model including longitudinal, lateral and yaw directions is established. Then, the longitudinal force and lateral force of the tires are normalized by analyzing the Dugoff tire model. Next, an improved unscented Kalman filtering algorithm combined with the limited memory filter and random weighted theory is used to estimate the TRFC. Finally, the co-simulation platform of MATLAB/Simulink and Carsim is built to verify the effectiveness of the LMRWUKF. The test results indicate that the estimation performance of LMRWUKF is better than that of traditional unscented Kalman filter.