Robust Actuator Fault-Tolerant LQR Control of Unmanned Bicycle Robot. A Feedback Linearization Approach

Introduction of Feedback Linearization to Robust LQR and LQI Control – Analysis of Results from an Unmanned Bicycle Robot with Reaction Wheel

“…The paper presents the analysis of control performance versus δ , at the final stage of research on the search of the control law resulting in superior performance, and yet computationally simple enough to be implemented on the STM32 microcontroller, for the considered robot. Earlier papers compared plain LQR to robust LQR approaches (with LQI considered), see [44], analyzed the impact of δ on robust versions of LQR and LQI algorithms, see [31], or the impact of initial condition of x 1 [17 versus δ on similar performance indices as in this paper.…”

“…The results turned the attention of the authors to a problem with lesser DoF to be concerned [32]. First, by introducing robustness into LQR/LQI control laws with results based on experiments [31], then introducing feedback linearization to a simulation model [44], with in-depth analysis of impact of initial conditions and robustness parameter [45], and different linearization schemes [17]. This paper is mainly focused on extending the results referring to the best combination of control approaches at the research stage of [31], when feedback linearization is used, and in the case of possible actuator failure, which mimics uncertainty introduced by the linearization of the robot model, or by possible constraints in the control signal.…”

Sparse in the Time Stabilization of a Bicycle Robot Model: Strategies for Event- and Self-Triggered Control Approaches

“…The mathematical description of a 2DoF robot model is given by the following set of non-linear differential equations [19,20]:ẋ…”

“…The triggering condition for ISTC is the same as for STC, (19), but now it is assumed that the input signal can change between triggers is constant. Therefore, the predictions of x t+l are calculated using the following model:…”

“…In previous papers, the authors performed 4DoF simulations [16] by introducing robustness into the linear-quadratic regulator (LQR) control scheme or by comparing different control strategies, e.g., LQR, LQI, and LMI-based LQR control in [17]. The results helped the authors to focus on problems with a lesser DoF to be concerned, e.g., by introducing robustness into LQR/LQI control laws with results based on experiments [18] and then by introducing feedback linearization to a simulation model [19], with an in-depth analysis of the impact of the initial conditions and the robustness parameter [20], and different linearization schemes [21]. In [22], the authors extended the results, referring to the best combination of control approaches at the research stage of [18] when feedback linearization was used, for possible actuator failure in order to represent the uncertainty introduced by the linearization of the robot model or by possible constraints imposed on the control signal.…”