Ensuring performance requirements for semiactive suspension with nonconventional control systems via robust linear parameter varying framework

Abstract

In the article a method which is able to provide the required performance level of a system is proposed. Its principle is to combine the results of conventional control methods with those of methods based on nonconventional, for example, machine-learning-based ones. In more detail, it designs a robust linear parameter varying (LPV) control in a predefined form, whose output is equivalent to the output of a machine-learning-based control inside a predefined operational range. Outside of the operation range the output of the machine-learning-based control is overridden, while the intervention with the performance level is guaranteed. The efficiency of the proposed method is illustrated through an example on the semiactive suspension control design. The nonlinearities in the dynamics of the magneto-rheological damper are considered through a nonlinear parameter varying (NLPV) model. It designs an NLPV model-based LPV control, which is combined with a neural network to achieve preview capability.

Coordination of Lateral Vehicle Control Systems Using Learning-Based Strategies

“…For example, the parameters of the control-oriented model can be adapted to their real values, and thus, the results of this paper and of [26] can be composed. Another example of the extension is to provide a training process for the setting of ∆ max , which can be formed as a variable, see the results of [25]. Moreover, the variety in the fields of applications also provides future challenges.…”

“…In [24] an iterative learning-based model predictive control (MPC) method is proposed, in which the terminal cost and set for the model-based control is learned. Another example is found in [25], where the Linear Parameter-Varying (LPV) method for the design of a safe control next to the learning-based controller is used. Moreover, the learning features can have an impact on the formulation of control-oriented state-space models, see e.g., [26].…”

Optimal Motion Design for Autonomous Vehicles With Learning Aided Robust Control

“…Another benefit of the method is that the design of the robust control and the formulation of the supervisor are independent of the internal structure of the RL-based agent. Consequently, other types of agents can also be used, e.g., supervised learning [21]. Therefore, the proposed control structure can be compatible with the application of machine-learning-based techniques, which compatibility may increase the number of application areas of the proposed method, e.g., y L can contain video frames.…”

“…Advanced vehicle modeling frameworks have been developed, which involve data processing on the step of model formulation, e.g., through closed-loop matching [19]. Focusing on the step of control design, it has been provided design frameworks, in which classical and learning-based control solutions jointly have been involved, e.g., robust [20] and LPV control with neural networks [21], or [22] has proposed a safe model-based reinforcement learning method to achieve control for LPV systems. Furthermore, data can be incorporated in the control design for coordinating multiple unmanned vehicles [23].…”

Robust Control Design for Autonomous Vehicles Using Neural Network-Based Model-Matching Approach

“…Therefore, the engineers started focusing on the development of mixed control algorithms, which take advantage of both groups. For example, the combination of the machine learning algorithm and Linear Parameter Varying (LPV) approach can be found in [4,7]. Meanwhile, paper [8] presents an MPC-based solution, which is extended with a machine learning-based reachability set computation for trajectory tracking of autonomous vehicles.…”