Search Results (74)

Accurate modelling of nonlinear dynamical systems remains a fundamental challenge in control engineering, particularly in applications characterized by strong nonlinearities, uncertainty, and varying operating conditions such as anti-lock braking systems (ABSs). Although neural networks are widely used for nonlinear system identification, their performance is often limited by correlated input features, poor numerical conditioning, and reliance on computationally demanding nonlinear optimization. This paper proposes a novel neural network modelling framework that integrates improved almost-orthogonal functional input transformation with a linear-in-parameters structure. The proposed approach systematically constructs a nonlinear feature space in which correlations between basis functions are explicitly controlled through a perturbation-based near-orthogonality mechanism, resulting in improved conditioning of the regression matrix and enabling stable least-squares-based parameter estimation. The method is formulated for a general class of nonlinear discrete-time systems and experimentally validated on an Inteco ABS laboratory setup, where wheel slip dynamics are identified using measured wheel speeds and braking torque. The obtained results demonstrate improved modelling accuracy, increased robustness to measurement noise, non-Gaussian disturbances, and parameter drift, as well as lower computational complexity compared with conventional multilayer perceptron and polynomial-based models. These findings suggest that structured feature generation may improve the reliability of data-driven models and indicate potential applicability of the proposed framework for real-time and control-oriented applications in complex dynamical systems. Full article

To improve the anti-slip performance and energy-efficient torque coordination of four-wheel-drive articulated tractors operating in hilly and mountainous terrains, this study proposes an integrated control framework that combines a 7-DOF tractor dynamics model, a GA-optimized fuzzy slip-ratio controller, and a three-level dynamic torque allocation strategy. First, a control-oriented full-vehicle dynamics model is established by integrating tractor body dynamics, wheel rotational dynamics, and the Dugoff tire model. Then, a fuzzy slip-ratio controller is designed using the slip-ratio tracking error and its rate of change as inputs, and its key parameters are optimized using a genetic algorithm. On this basis, a three-level dynamic torque allocation strategy is developed to coordinate the four in-wheel motors according to wheel-load distribution and slip-related constraints. MATLAB/Simulink (version 2023a) simulations and hardware-in-the-loop (HIL) tests are carried out to validate the proposed strategy. Under the straight-line driving condition, the RMSE of the proposed GA-fuzzy controller is reduced from 0.02716 to 0.00897. Under the steering condition, the average RMSE is reduced from 0.02079 to 0.01003. In addition, under the torque-allocation validation condition, the average four-wheel RMSE is reduced from 0.29632 under equal torque allocation to 0.02159 under the proposed three-level dynamic torque allocation strategy. The results indicate that the proposed method can effectively maintain the slip ratio near its target value, suppress excessive slip and redundant torque output, and improve the anti-slip and energy-efficient performance of articulated tractors. More importantly, the study provides an integrated control framework that unifies GA-optimized slip regulation and three-level torque coordination specifically for four-wheel-drive articulated tractors. Full article

This paper addresses a recursive adaptive anti-lock braking (AB) control design problem for electro-hydraulic brake (EHB) systems subject to unknown tire–road-friction coefficients and disturbances. Compared with the relevant literature, the primary contributions are (i) the development of a novel nonlinear AB model integrated with a bond-graph-based EHB (BGEHB) system, and (ii) the proposal of an adaptive neural AB control approach incorporating a nonlinear disturbance observer (NDO). The AB and BGEHB models are unified into a single nonlinear braking model, with the wheel speed as the system output and the duty ratios of the BGEHB inlet and outlet valves as control inputs. To maintain an optimal slip ratio during braking, we design the NDO-based adaptive AB controller to regulate the wheel speed in a recursive manner. The designed controller incorporates a delay-compensation term to address the time-delay characteristics of the hydraulic system, employs a neural-network function approximator in the NDO and controller to compensate for the unknown tire–road-friction coefficient, and applies NDOs to compensate for disturbances due to the vehicle motion and BGEHB dynamics. The stability of the proposed control scheme is established via the Lyapunov theory, and a simulation comparison is presented to demonstrate the effectiveness of the proposed design approach. Full article

This paper presents a coordinated control strategy for an electro-hydraulic composite braking system in in-wheel motor electric vehicles to enhance regenerative energy recovery and braking safety. A novel hydraulic control unit (HCU) without a pressure-reducing valve is designed to simplify structure and maximize energy utilization. Based on the ideal braking force distribution, a force allocation strategy coordinates motor and hydraulic braking across modes, ensuring motor torque can compensate total braking torque when wheel lock occurs. An anti-lock braking (ABS) strategy relying solely on motor torque adjustment is proposed, keeping hydraulic torque constant while rapidly stabilizing slip within 13–17%, thereby avoiding interference between hydraulic and motor braking. A joint Simulink–AMESim–CarSim platform evaluates the strategy under varying conditions, and real-vehicle tests in regenerative mode confirm feasibility and smooth switching. Results show the proposed approach achieves target braking intensity, improves energy recovery, reduces torque oscillations and valve actions, and maintains stability. The study offers a practical solution for integrating regenerative braking and ABS in in-wheel motor EVs, with potential for hardware-in-the-loop validation and advanced stability control applications. Full article

Anti-lock braking systems (ABSs) play a vital role in vehicle safety by preventing wheel lockup and maintaining stability during braking. However, their performance is strongly affected by variations in tire–road friction, which often limits the effectiveness of conventional controllers. This research proposes and evaluates a fuzzy logic controller (FLC)-based ABS using a quarter-vehicle model and the Burckhardt tire–road interaction, implemented in MATLAB/Simulink. Two input variables (slip error and slip rate) and one output variable (brake pressure adjustment) were defined, with triangular and trapezoidal membership functions and 15 linguistic rules forming the control strategy. Simulation results under diverse road conditions—including dry asphalt, concrete, wet asphalt, snow, and ice—demonstrate substantial performance gains. On high- and medium-friction surfaces, stopping distance and stopping time were reduced by more than 30–40%, while improvements of up to 25% were observed on wet surfaces. Even on snow and ice, the system maintained consistent, albeit modest, benefits. Importantly, the proposed FLC–ABS was benchmarked against two recent studies: one reporting that an FLC reduced stopping distance to 258 m in 15 s compared with 272 m in 15.6 s using PID, and another where PID outperformed an FLC, achieving 130.21 m in 9.67 s against 280.03 m in 16.76 s. In contrast, our system achieved a stopping distance of only 24.41 m in 7.87 s, representing over a 90% improvement relative to both studies. These results confirm that the proposed FLC–ABS not only demonstrates clear numerical superiority but also underscores the importance of rigorous modeling and systematic controller design, offering a robust and effective solution for improving braking efficiency and vehicle safety across diverse road conditions. Full article

This paper addresses the challenge of stabilizing an unmanned aerial vehicle with an arm (UAM) on a pipeline with disturbance, where the disturbance factors include white noise, mass uncertainty, and wind disturbance. An anti-disturbance control method is proposed utilizing nonlinear model predictive control (NMPC). Initially, the natural wind field model is developed. Considering wind disturbance, the UAM dynamics are analyzed utilizing Newton–Euler theory. Subsequently, the no-slip constraints and the terminal constraints are defined to prevent UAM from destabilizing and falling. The NMPC-based algorithm is developed to ensure the stable control of UAM, transforming the optimization problem into a nonlinear programming problem. The terminal cost function and the inequality constraints for establishing the state variables using linear quadratic regulator (LQR) are meticulously studied. Finally, numerical simulations are carried out to further verify the proposed method, considering internal disturbance about physical parameters and external disturbance about wind. Simulation results show that the disturbance is well compensated, and the UAM tilt angle is less than 0.3 deg. Therefore, the proposed control method can comprehensively consider the input energy consumption and the realization of stability, and has a certain degree of anti-interference. Full article

Traditional vehicle emergency braking research suffers from inaccurate maximum road adhesion coefficient identification and suboptimal wheel slip ratio control. To address these challenges in electronic hydraulic braking systems’ anti-lock braking technology, firstly, this paper proposes a CDOA-SENet-CNN neural network to precisely estimate the maximum road adhesion coefficient by monitoring and analyzing the braking process. Secondly, correlation curves between peak adhesion coefficients and ideal slip ratios are established using the Burckhardt model and CarSim 2020, and the estimated maximum adhesion coefficient from the CDOA-SENet-CNN network is used with these curves to determine the optimal slip ratio for the single-neuron integral sliding mode control (SNISMC) algorithm. Finally, an SNISMC control strategy is developed to adjust the wheel slip ratio to the optimal value, achieving stable wheel control across diverse road surfaces. Results indicate that the CDOA-SENet-CNN network rapidly and accurately estimates the peak braking surface adhesion coefficient. The SNISMC control strategy significantly enhances wheel slip ratio control, consequently increasing the effectiveness of vehicle brakes. This paper introduces an innovative, stable, and efficient solution for enhancing vehicle braking safety. Full article

To study and quantify the impact of water storage on lake slope stability after the closure of an open-pit mine, we targeted slope control measures by large-scale parallel computing methods and strength reduction theory. This was based on a three-dimensional refined numerical model to simulate the evolution of slope stability under different water storage levels and backfilling management conditions, and to quantitatively assess the risk of slope instability through the spatial distribution of stability coefficients. This study shows that during the impoundment process, the slope stability has a nonlinear decreasing trend due to the decrease in effective stress caused by the increase in pore water pressure. When the water storage was at 0 m, the instability range is the largest, and the surface range is nearly 200 m from the edge of the pit; when the water level continued to rise to 50 m, the hydrostatic pressure of the pit lake water on the slope support effect began to appear, and the stability was improved, but there is still a wide range of unstable areas at the bottom. In view of the unstable area of the steep slope with soft rock in the north slope during the process of water storage, the management scheme of backfilling the whole bottom to −150 m was proposed, and the slope protection and pressure footing were formed by discharging the soil to −40 m in steps to improve the anti-slip ability of the slope. Full article

The design and optimization of drive distribution strategies are critical for enhancing the performance of Formula Student electric racing cars, which face demanding operational conditions such as rapid acceleration, tight cornering, and variable track surfaces. Given the increasing complexity of racing environments and the need for adaptive control solutions, a multi-mode adaptive drive distribution strategy for four-wheel-drive Formula Student electric racing cars is proposed in this study to meet specialized operational demands. Based on the dynamic characteristics of standardized test scenarios (e.g., straight-line acceleration and figure-eight loop), two control modes are designed: slip-ratio-based anti-slip control for longitudinal dynamics and direct yaw moment control for lateral stability. A CarSim–Simulink co-simulation platform is established, with test scenarios conforming to competition standards, including variable road adhesion coefficients (μ is 0.3–0.9) and composite curves. Simulation results indicate that, compared to conventional PID control, the proposed strategy reduces the peak slip ratio to the optimal range of 18% during acceleration and enhances lateral stability in the figure-eight loop, maintaining the sideslip angle around −0.3°. These findings demonstrate the potential for significant improvements in both performance and safety, offering a scalable framework for future developments in racing vehicle control systems. Full article

Aiming at the problems of energy utilization efficiency and braking stability in electric vehicles, a high-efficiency and energy-saving control strategy that takes both driving and braking into account is proposed with the distributed hub motor-driven vehicle as the research object. Under regular driving and braking conditions, the front and rear axle torque distribution coefficients are optimized by an adaptive particle swarm algorithm based on simulated annealing and a multi-objective co-optimization strategy based on variable weight coefficients, respectively. During emergency braking, the anti-lock braking strategy (ABS) based on sliding mode control realizes the independent distribution of torque among four wheels. The joint simulation verification based on MATLAB R2023a/Simulink-Carsim 2020.0 shows that under World Light Vehicle Test Cycle (WLTC) conditions, the optimization strategy reduces the driving energy consumption by 3.20% and 2.00%, respectively, compared with the average allocation and the traditional strategy. The braking recovery energy increases by 4.07% compared with the fixed proportion allocation, improving the energy utilization rate of the entire vehicle. The wheel slip rate can be quickly stabilized near the optimal value during emergency braking under different adhesion coefficients, which ensures the braking stability of the vehicle. The effectiveness of the strategy is verified. Full article

This paper addresses the issue of wheelset slip in trains caused by low-adhesion track surfaces and proposes an active anti-slip tracking control strategy. Considering the wide operational range of trains and the complex adhesion conditions between wheels and rails, a comprehensive model of the train, incorporating adhesion effects, is developed and then transformed into a mathematical model with perturbations. To tackle the slip phenomenon on low-adhesion track surfaces, a robust adhesion observer with high dynamic accuracy is designed. Building on this, an active anti-slip strategy is proposed to ensure that the control command does not exceed the maximum traction force available from the track surface. To further enhance controller performance, higher-order sliding mode control is integrated with a saturation compensation law. Finally, a Hardware-in-the-Loop (HIL) platform is constructed using a Digital Signal Processor (DSP) controller and a Modular Test (MT) PXI real-time simulator. The simulator loads the adhesion model, while the DSP controller executes the designed anti-slip control algorithm. Experimental results demonstrate that the proposed controller effectively prevents wheelset slip under low-adhesion conditions and significantly reduces tracking errors along the target speed-displacement curve. Full article

This paper conducts an in-depth study on anti-lock braking technology in electronic hydraulic braking systems, focusing on a road-surface recognition algorithm based on fitted curves and a slip-rate control method based on sliding-mode variable structure. Firstly, a road-surface recognition algorithm using fitted curves is proposed, which extracts characteristic information by fitting the μ-λ curve, achieving the accurate identification of different road-surface conditions and providing optimal slip rates for subsequent braking control. Secondly, a slip-rate control strategy based on sliding-mode variable structure is designed to achieve optimal slip-rate control during vehicle braking, ensuring braking stability and safety under varying road conditions. Through theoretical analysis and simulation experiments, the results show that the proposed road-surface recognition algorithm can effectively identify various typical road surfaces (such as dry, wet, and icy/snowy surfaces) with high accuracy. The sliding-mode variable-structure control strategy can achieve good slip-rate control under different road conditions, effectively improving vehicle braking performance. This study provides an efficient and reliable technical solution for anti-lock braking control in electronic hydraulic braking systems, with significant theoretical and practical implications for enhancing vehicle braking safety. Full article

The rapid growth of automotive intelligence and automation technology has made it difficult for traditional in-vehicle servo systems to satisfy the demands of modern intelligent systems when facing complex problems such as external disturbances, nonlinearity, and parameter uncertainty. To improve the anti-interference ability and control accuracy of the system, this study proposes a joint control method of electronic mechanical braking control combined with the anti-lock braking system. This method has developed a new type of actuator in the electronic mechanical brake control system and introduced a particle swarm optimization algorithm to optimize the parameters of the self-disturbance rejection control system. At the same time, it combines an adaptive inversion algorithm to optimize the anti-lock braking system. The results indicated that the speed variation of the developed actuator and the actual signal completely stopped at 1.9 s. During speed control and deceleration, the actuator could respond quickly and accurately to control commands as expected. On an asphalt pavement, the maximum slip rate error of the optimized control method was 0.0428, while the original control method was 0.0492. The optimized method reduced the maximum error by about 12.9%. On icy and snowy roads, the maximum error of the optimization method was 0.0632, significantly lower than the original method’s 0.1266. The optimization method could significantly reduce slip rate fluctuations under extreme road conditions. The proposed method can significantly improve the control performance of the vehicle-mounted servo platform, reduce the sensitivity of the system to external disturbances, and has high practical value. Full article

The movement of a wheeled vehicle is a non-regular dynamic process characterized by a large number of states that depend on the movement conditions. This movement involves a large number of situations where elastic tires skid and slip against the base surface. This reduces the efficiency of movement as useful mechanical energy of the electromechanical drive is spent to overcome the increased skidding and slipping. Complete sliding results in the loss of control over the vehicle, which is unsafe. Processes that take place immediately before such phenomena are of special interest as their parameters can be useful in diagnostics and control. Additionally, such situations involve adverse oscillatory processes that cause additional dynamic mechanical and electrical loading in the electromechanical drive that can result in its failure. The authors provide the results of laboratory road research into the emergence of self-oscillatory phenomena during the rolling of a wheel with increased skidding on the base surface and a low traction factor. This paper reviews the methods of designing an anti-slip control system for wheels with an oscillation damping function and studies the applicability and efficiency of the suggested method using mathematical simulation of the virtual vehicle operation in the Matlab Simulink software package. Using the self-oscillation suppression algorithm in the control system helps reduce the maximum amplitude values by 5 times and average amplitudes by 2.5 times while preventing the moment operator from changing. The maximum values of current oscillation amplitude during algorithm changes were reduced by 2.5 times, while the current change rate was reduced by 3 times. The reduction in the current-change amplitude and rate proves the efficiency of the self-oscillation suppression algorithm. The high change rate of the current consumed by the drive’s inverters may have a negative impact on the remaining operating life of the rechargeable electric power storage system. This impact increases with the proximity of its location due to the low inductance of the connecting lines and the operating parameters, and the useful life of the components of the autonomous voltage inverters. Full article

To improve the slip rate control effect for different road conditions during emergency braking of wheel hub motor vehicles, as well as to address the problems of uncertainty and nonlinearity of the system when the electro-mechanical braking system is used as the actuator of the ABS, a hierarchical control strategy of the anti-lock braking system (ABS) using active disturbance rejection control (ADRC) is proposed. Firstly, a vehicle dynamics model and an ABS model based on the EMB system are established; secondly, a speed observer based on the dilated state observer is used in the upper layer to design a pavement recognition algorithm, which recognizes the current pavement and outputs the optimal slip rate; then, an ABS controller based on the ADRC algorithm is designed for the lower layer to track the optimal slip rate. In order to verify the performance of the pavement recognition method and control strategy, vehicle simulation software is used to establish the model and simulation. The results show that the road surface recognition method can quickly and effectively recognize the road surface, and comparing the emergency braking control effects of PID and SMC under different road surface conditions, the ADRC strategy has better robustness and reliability, and improves the braking effect. Full article