Sliding Mode Control Algorithms for Anti-Lock Braking Systems with Performance Comparisons

Robust Self-Learning PID Control of an Aircraft Anti-Skid Braking System

In order to deal with strong nonlinearity and external interference in the braking process, this paper proposes a robust self-learning PID algorithm based on particle swarm optimization, which does not depend on a precise mathematical model of the controlled object. The self-learning function is used to adapt to the diversity of the runway road surface friction, the particle swarm algorithm is used to optimize the rate of self-learning, and robust control is used to deal with the modeling uncertainty and external disturbance of the system. The convergence of the control strategy is proved by theoretical analysis and simulation experiments. The superiority and accuracy of the method are verified by NASA ground test results. The simulation results shows that the adverse effect of the external disturbance is suppressed, and the ideal trajectory is tracked.

A Lyapunov Stability Analysis of Modified HOSM Controllers Using a PID-Sliding Surface Applied to an ABS Laboratory Setup

The antilock braking system (ABS) is a mechatronic system that helps a driver maintain the maneuverability of a vehicle while braking by preventing wheel lock-ups. However, the design of high-performance controllers for this type of system is complicated because of its highly nonlinear dynamics. The problem becomes even more difficult to resolve when uncertainties in the parameters appear in its dynamics. In this paper, an ABS laboratory setup mimicking a quarter car model is considered. A modified high-order sliding mode (HOSM) controller using a proportional–integral–differential (PID) control as a sliding surface was designed. This controller provides a reference value of a tire slip. The proposed controller uses a tracking error to define the slip surface through the PID controller structure, and the modified HOSM controller holds the system on the previously designed slip surface. The closed-loop system stability has been proven in the sense of Lyapunov. Finally, the ABS laboratory setup allows for experimentally checking the performance of the modified HOSM controller using a PID-sliding surface, showing a considerable increase in the efficiency of the control system compared with a PID-like controller.

Review on Braking Energy Management in Electric Vehicles

The adoption of electric vehicles promises numerous benefits for modern society. At the same time, there remain significant hurdles to their wide distribution, primarily related to battery-based energy sources. This review concerns the systematization of knowledge in one of the areas of the electric vehicle control, namely, the energy management issues when using braking controllers. The braking process optimization is summarized from two aspects. First, the advantageous solutions are presented that were identified in the field of gradual and urgent braking. Second, several findings discovered in adjacent fields of automation are debated as prospects for their possible application in braking control. Following the specific classification of braking methods, a generalized braking system composition is offered, and all publications are evaluated primarily in terms of their energy recovery abilities as a global target. Then, conventional and intelligent classes of braking controllers are compared. In the first category, classic PID, threshold, and sliding-mode controllers are reviewed in terms of their energy management restrictions. The second group relates to the issues of the tire friction-slip identification and braking torque allocation between the hydraulic and electrical brakes. From this perspective, several intelligent systems are analyzed in detail, especially fuzzy logic, neural network, and their numerous associations.