Bumpless‐transfer‐based fault tolerant control for the quadcopter: Piecewise homogeneous emission probability approach

This study presents novel formulations for robust state‐feedback control synthesis for discrete‐time linear time‐invariant systems based on linear matrix inequalities. The proposed formulations require searching for two adjustment matrices. The synthesis formulations include other formulations from the literature as particular cases according to specific values of the adjustment matrices. We propose the application of evolutionary optimization to determine the optimal values of these two adjustment matrices. One approach to obtaining a single‐step formulation for static output‐feedback control synthesis is to transform a state‐feedback control synthesis formulation via the simple change of variables. The alteration of variables considered in this study requires an adjustment matrix. The adjustment matrix influences the performance of the resulting controller or the existence of a feasible solution to the problem. Here, we also propose the application of evolutionary optimization to determine the optimal value of this adjustment matrix and the two adjustment matrices of the proposed formulations to obtain the optimal robust control system. Case studies verify that despite the increased complexity, the proposed formulations and the method required to tune them may be indispensable in achieving a robustly stable control system or an enhanced performance for more intricate problems.

Section: Introductionmentioning

confidence: 99%

ℋ_∞ robust control of discrete‐time systems based on new linear matrix inequality formulations and evolutionary optimization

Gonçalves,

Oliveira,

da Silva Júnior

2023

Koopman‐based 3‐dimensional path following control for robotic flexible needles

Zhang,

Qi,

Chen

et al. 2024

Robotic flexible needles have gained significant attention in minimally invasive medical procedures due to their ability to navigate complex anatomical structures and reach targets with high precision. Addressing the complex control problem in a 3‐dimensional environment, a Koopman‐based data‐driven control strategy is proposed in this paper. First, a 3‐dimensional (3D) path tracking problem is modeled using the simplified high‐dimensional bicycle model with the puncture kinematic. Then, with the Koopman operator theory, a finite linear approximation is designed and trained to simplify the nonlinear system of flexible needles. Finally, based on the linear approximation, a Koopman‐based model predictive control (MPC) scheme is proposed to realize 3‐dimensional path tracking for flexible needles. Based on simulations, the linear approximation and data‐driven control strategy are validated.

Intelligent switching gain based sliding mode control for optimization of power consumption in cooperative manipulators

Kia,

Korayem,

Lademakhi

2024

Cooperative manipulators, vital for intricate tasks, are gaining widespread attention across industries. Recognizing their impact on power consumption, costs, and task outcomes, this paper emphasizes the critical study of control methods, actuator power consumption, and manipulator accuracy. Addressing these challenges, we propose a neural network‐sliding mode controller (NN‐SMC) to optimize actuator power consumption and minimize errors in cooperative manipulators. Operating in trajectory and point‐to‐point modes, the NN‐SMC dynamically generates real‐time switching mode controller (SMC) gains (L and K) for precise control. Stability is ensured by maintaining gains within permissible ranges. In point‐to‐point mode, the NN orchestrates an optimal path generation, along with tailored gains. To evaluate performance accurately, a novel control performance index is introduced. Experimental results on 3‐DOF cooperative manipulators demonstrate a remarkable 28% increase in the control performance index for the trajectory mode and a substantial reduction in computational complexity for both modes. This work not only addresses inherent challenges in cooperative manipulators but also signifies a methodological advancement through the integration of neural network‐based control, promising enhanced efficiency and stability.