Key Takeaways & Executive Findings
- •• Proposes a novel hybrid control algorithm, Variable Stability Control (VSC), integrating backstepping, model predictive control, and model game control to mimic human-like adaptability. • VSC addresses uncertainty in steer-by-wire systems by dynamically adjusting control parameters via model predictive control and model game control, improving angle tracking performance. • Experimental validation demonstrates VSC's effectiveness in angle tracking, offering a promising solution for real-world automotive applications. • The approach highlights the potential of algorithm-hybrid strategies to emulate human decision-making in complex control environments.
Abstract
It is particularly challenging to develop a new control theory like human intelligence, as human cognition and decision-making are variable in changing environments. In this article, the idea of variable stability is adopted to design a human-like control algorithm, referred to as variable stability control. A variable model perturbation put into the system dynamics model is computed by model game control, which simulates changes in human cognition. Lyapunov stability control is employed to formulate a backstepping control law that mimics the underlying logic algorithm in human decision-making. Some variable algorithm parameters embedded into the control law are calculated using model predictive control, which imitates dynamic tuning in human decision-making. From another perspective, variable stability control is an algorithm-hybrid control approach validated in a steer-by-wire system for angle tracking. According to the experimental results, variable stability control is a promising candidate for angle tracking in steer-by-wire systems.
1. Introduction
As illustrated in Figure 1, cybernetics has a long history that can be divided into five periods, i.e. the primitive era, the mechanical era, the electrical era, the digital era, and the era of intelligence. Currently, humanity stands at the threshold of the era of intelligence. However, there may be not still a control algorithm to meet the demand of coping well with uncertainty in practical systems. On one hand, the adverse effects of uncertainty degrade control performance. On the other hand, developing an accurate model for uncertain systems is often challenging. Consequently, the control of uncertain systems has drawn much attention in both academic and industrial fields. Numerous control methods have been proposed to ensure system performance in the presence of uncertainty, including backstepping control (BSC) [1], sliding mode control (SMC) [2], model predictive control (MPC) [3], and optimal control [4]. However, these advanced controllers often fail to guarantee promising control performance for uncertain systems in complex and different working situations. To address this issue, this paper proposes a hybrid control algorithm called variable stability control (VSC), which integrates the advantages of BSC, MPC, and model game control (MGC).
As a Lyapunov-stability-based control method, the BSC was initially proposed by employing a recursive control design to stabilize a single-state feedback nonlinear system [5]. The higher-order system is decomposed into first-order subsystems [6]. The control law for each subsystem is derived under the condition that the derivative of the Lyapunov function is negative, utilizing a user-defined stepping manifold. The control design is executed recursively until the final control law containing all subsystems is established. In each stepping manifold, the stepping parameter is positive to ensure system stability and is selected properly to achieve good control performance [7]. Respecting the constant stepping parameters, the control performance is not satisfactory for time-varying systems [8]. The online identification of the stepping parameters is applied to cope with model uncertainties [9]. The adaptive performance of the BSC has not significantly improved. To identify and optimize the uncertainty and perturbation parameters, a reinforcement learning approach is employed to determine the stepping parameters [10]. By setting up a series of parameters in the stepping manifold [11], the upper bound of the tracking error is reduced, but a high computing power is required. Such methods face difficulties in finding the true optimal solution when real-world physical constraints are not considered.
The MPC is a promising approach that incorporates constraints to account for the physical characteristics of systems.
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Lin He, Ziang Xu, Yujiang Wei, Shanshan Peng, Huasheng Feng, Qin Shi (2025). Variable Stability Control Approach for Angle Following of Steer-by-wire System. Chinese Journal of Mechanical Engineering. https://doi.org/10.1186/s10033-025-01304-9
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Frequently Asked Questions
What is Variable Stability Control (VSC)?
VSC is a hybrid control algorithm that integrates backstepping control, model predictive control, and model game control to emulate human-like adaptability in uncertain systems, specifically for angle tracking in steer-by-wire systems.
How does VSC handle uncertainty in steer-by-wire systems?
VSC uses model game control to compute variable model perturbations, and model predictive control to dynamically tune algorithm parameters, allowing the control law to adapt to changing conditions and improve tracking performance.
What are the key components of VSC?
The key components are: a variable model perturbation computed by model game control, a backstepping control law based on Lyapunov stability, and variable algorithm parameters calculated using model predictive control.
What are the experimental results of VSC in steer-by-wire systems?
The experimental results demonstrate that VSC is a promising candidate for angle tracking in steer-by-wire systems, showing improved performance compared to traditional control methods.
Why is VSC considered human-like?
VSC mimics human cognition and decision-making by simulating changes in cognition through model game control and dynamic tuning of parameters through model predictive control, similar to how humans adapt to changing environments.
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