Arash Farnam

Wheel slip control is a critical aspect of vehicle safety systems, notably the antilock braking system (ABS). Designing a robust controller for the ABS faces the challenge of accommodating its strong nonlinear behavior across varying road conditions and parameters. To ensure optimal performance during braking and prevent skidding or lock-up, the ideal wheel slip value can be determined from the peak of the tire–road friction curve and maintained throughout the braking process. Among various control approaches, model predictive control (MPC) demonstrates superior performance and robustness. However, online MPC implementation encounters significant computational burdens and real-time limitations, particularly when dealing with larger problem sizes. To address these issues, this study introduces an offline robust model predictive control (RMPC) methodology. The proposed approach is based on the robust asymptotically stable invariant ellipsoid methodology, which employs linear matrix inequalities (LMIs) to calculate a collection of invariant state feedback laws associated with a sequence of nested invariant stable ellipsoids. Simulation results indicate a significant reduction in computational burden with the offline RMPC approach compared to online implementation, while effectively tracking the desired wheel slip reference values and system constraints. Moreover, the offline RMPC design aligns well with the online MPC design and verifies its effectiveness in practice.

Summary: This paper investigates a scalable recursive state estimation method for nonlinear two‐dimensional (2‐D) stochastic complex networks. In these networks, the switching topology is modelled by random variables generated by the Bernoulli distribution. Considering the Extended Kalman Filter structure, we employ the Taylor series expansion to handle the nonlinear functions in the model. Hence, high‐order terms emerging in the linearization process are modelled by norm‐bounded parameter uncertainties. Due to the uncertainty of such a system, we obtain an upper bound for the estimation error covariance. Furthermore, we obtain an upper bound for the cross covariance to reduce the proposed filter's computational complexity and improve scalability. Within the established setting, next, the proposed filter is designed in such a way that the presented upper bounds are minimized. Finally, a numerical example is provided to illustrate the effectiveness of the proposed state estimation algorithm.

This study investigates the robust stability analysis of an unstable second order plus time-delay (SOPTD) plant by using Fractional-Order Proportional Integral (FOPI) controllers. We assume that there are simultaneous uncertainties in gain, time-constants, and time-delay of the plant. At first, a graphical method is provided for a robust stability analysis of the closed-loop system. Then, a robust stability checking function is introduced to facilitate the robust stability analysis. Additionally, new bounds are presented to reduce the computational burden for the robust stability analysis. Finally, two examples are provided to show the correctness of the proposed method.

This letter aims to investigate the robust stability of an interval fractional-order plant with an interval time delay by a general form of fractional-order controllers. Based on a simple graphical procedure, necessary and sufficient criteria are proposed to analyze the robust stability of an interval fractional-order plant. Then, the idea of “robust stability testing function” is extended to analyze the robust stability of the considered type of systems. Besides, an upper frequency bound is provided which is useful for the simulation purposes and it helps to reduce the computational cost. Finally, two examples are presented to verify the usefulness of the obtained results.

The intermittent nature of wind speed imposesa nonlinear behavior on the dynamics of the wind turbine. Consequently, relying on one linear controller tuned to a single specific operating point cannot guarantee a feasible performance in the whole operating region despite providing a fast solution. Besides, wind speed variations cause oscillations in the output power of the wind turbine. To tackle these issues, the Soft Switching Multiple Model Predictive Control (SSMMPC) technique is introduced in which the nonlinear dynamical model of the wind turbine is approximated by defining multiple linear models around various operating points. The gap metric is used as a mathematical tool to construct a bank of multiple linear models that results in a bank of linear controllers. By assuming a relatively small gap threshold value between the neighboring linear models, the soft switching is guaranteed. In this way, there will be no unexpected impulsive behavior in the control input during switching from one MPC to another. The closed loop system stability is studied using Lyapunov theory and a novel switching stability proof based on probabilities is provided. The SSMMPC performance is investigated and compared with a TSR-based controller and a bidirectional control method through simulations using the NREL 10 kW wind turbine model and experimental results on a 7.5 kW wind turbine drivetrain. The results corroborate the improvements that can be attained by using SSMMPC in terms of more captured energy, better maximum power point tracking quality, lower generator torque oscillations, and smoother output power curve.

This paper investigates the problem of a scalable distributed state estimation for a class of discrete time-variant systems with state-saturation, quantization effects, and two redundant channels over a sensor network. In transmission data from a sensor to its estimator, two phenomena are considered together. First, the data of each sensor is transmitted to its estimator through two redundant communication channels. Second, innovation data is quantized before being used by the estimator. These phenomena are beneficial in alleviating the negative effects on measurements and reducing the energy consumption and bandwidth. In the structure of proposed filter consensus is used on estimations in which consensus is first achieved on the prediction estimation, then the accuracy of computed estimation is improved by two recursive equations. The parameters of the proposed filter are obtained for each sensor node by employing an upper bound for common error covariance, therefore less computational burden is required. Eventually, the comparative simulation results are presented to show that our method has better performance compared with a rival one recently published.