An electronic stability control (ESC) algorithm is proposed for a four in-wheel motor independent-drive electric vehicle (4MIDEV) utilizing motor driving and regenerative braking torque distribution ...control to improve vehicle stability. A stability judgment controller, an upper level controller, and a torque distribution algorithm are designed for the ESC system. The stability judgment controller is designed to generate the desired yaw rate and sideslip angle for vehicle stability, and the control mode, which is normal driving mode or ESC mode, is set according to the driver inputs and measurement signal inputs. The upper level controller consists of a speed tracking controller, a yaw moment controller, and four wheel-slip controllers to calculate the desired value of traction force, the desired value of yaw moment, and the four respective net torque inputs of the four in-wheel motors. The torque distribution algorithm is designed to generate each motor driving torque or regenerative braking torque input for each wheel. An average torque distribution strategy, a tire-dynamic-load-based torque distribution strategy, and a minimum-objective-function-based optimal torque distribution strategy are used separately in the torque distribution algorithm to control the motor driving torque or regenerative braking torque for vehicle stability enhancement. The proposed ESC algorithm was implemented and evaluated in a CarSim vehicle model and a MATLAB/Simulink control model. The three proposed torque distribution strategies can be used to regulate the vehicle to perform the following tasks: "single lane change," "double lane change," and "snake lane change." The simulation studies show that the yaw rate error root mean square RMS <inline-formula> <tex-math notation="LaTeX">(\gamma-\gamma_\mathrm{-des})</tex-math></inline-formula> decreased, on average, by 75 percent using the proposed optimal torque distribution algorithm compared with that without using stability control.
Smart grid is the new trend for clean, sustainable, efficient and reliable energy generation, delivery and use. To ensure stable and secure operation is essential for the smart grid, which needs ...effective stability analysis and control. As the smart grid has evolved through a growing scale of interconnection, increasing integration of renewable energy, widespread operation of direct current power transmission systems, and liberalization of electricity markets, the stability characteristics of it are much more complex than the past. Due to these changes, conventional stability analysis and control approaches have a series of drawbacks in terms of speed, effectiveness and economy. On the contrary, the emerging artificial intelligence (AI) techniques provide powerful and promising tools for stability analysis and control in smart grids and have attracted growing attention. This paper aims to give a comprehensive and clear picture of recent advances in this research area. First, we present a general overview of AI, including its definitions, history and state-of-the-art methodologies. And then, this paper gives a comprehensive review of its applications to security assessment, stability assessment, fault diagnosis, and stability control in smart grids. These applications have achieved impressive results. Nevertheless, we also identify some major challenges these applications face in practice: high requirements on data, imbalanced learning, interpretability of AI, difficulties in transfer learning, the robustness of AI to communication quality, and the robustness against attack or adversarial examples. Furthermore, we provide suggestions for potential important future investigation directions to overcome these challenges and bridge the gap between research and practice.
•An overview of state-of-the-art artificial intelligence (AI) is provided.•A review of AI’s applications to smart grid stability and control is undertaken.•This paper identifies some major challenges these applications face in practice.•Suggestions and potentially important future research directions are put forward.
To address the problem of the mutual interference and control distribution in the integrated stability control of active front steering (AFS) and electronic stability control (ESC), this study ...proposes a new integrated stability controller of AFS and ESC based on holistic control framework. A distribution rule of longitudinal and lateral tire force based on equal reserve capacity of tire force is designed to distribute the control right of steering and braking. A UniTire model with combined slip condition is also designed for the integrated control of steering and braking. Model predictive control (MPC) is adopted to address the constrained multi-objective control problem. To reduce the complexity of the system and improve the real-time performance of the system, a linear time-varying MPC controller is designed by linearizing the nonlinear system, and then the constrained MPC control problem is transformed into a quadratic programming problem for solving. Finally, the proposed method is validated on the basis of Matlab/Simulink and CarSim co-simulation platform. Results show that the proposed method has obvious effects on solving the mutual interference and control right distribution of steering and braking and improving vehicle stability.
•Algorithm for Hamilton energy function in dynamical system is clarified.•Hamilton energy function is the most suitable Lyapunov function for dynamical control.•Control of energy flow is the most ...effective way to synchronization control of chaotic systems.
Lyapunov function provides feasible estimation and prediction of nonlinear system stability, and useful guidance for adaptive control in chaos and synchronization approach. In case of synchronization and control of chaotic systems, the involvement of adjustable gains in the Lyapunov function can be effective to optimize the convergence of orbits to stability and controllers within finite transient period. As a result, shorter transient period and lower power consumption can be approached by detecting the most suitable gains in the controllers and parameter observers. In this paper, we claim that the most suitable Lyapunov function can be the Hamilton energy for chaotic systems and more nonlinear dynamical systems, and so the parameter region for stability and controllability can be detected exactly, in addition, the reliability of controllers can be confirmed in practical way. Furthermore, the Lorenz and improved Chua oscillators in chaotic states are presented to confirm the dependence of Hamilton energy and stability on the intrinsic parameters and variables. It indicates that control of energy flow can be an effective scheme to control chaos in nonlinear systems and synchronization realization between chaotic systems, neurons and networks.
Because of the increasing complexity and nonlinearity of industrial processes, nonlinear model predictive control (NMPC) has been rapidly developed owing to its fast response and robustness. However, ...the complicated optimization process of NMPC limits its application. Hence, this paper proposes an NMPC method that is compatible with nonlinear modeling and concise online control. First, an elastic autoregressive fuzzy neural network (EAFNN) is proposed under reasonable assumptions. The EAFNN exhibits strong parameter identification and structure optimization capabilities because of its autoregressive layer and elastic mechanism. Second, the EAFNN is adaptively simplified into a linear model based on the real-time working condition information during online control. Third, based on a simplified model, NMPC provides an explicit solution without complex optimization procedures. Finally, numerical simulations and roasting process experiments are conducted. Experimental results show that the proposed method exhibits superior control performance and computational complexity compared with other methods, thereby verifying its effectiveness and superiority. The source code for EAFNN-MPC is publicly available at: https://github.com/553318570/EAFNN_MPC.git.
•An improved fuzzy neural network is proposed with higher nonlinear representation.•An elastic mechanism is introduced to learn the optimal model structure.•The control are based on a simplified model for reducing computational burden.•The method can stabilize the roasting temperature and handle interference.
This paper deals with vehicle sideslip angle estimation. The paper introduces an industrially amenable kinematic-based approach that does not need tire–road friction parameters or other dynamical ...properties of the vehicle. The convergence of the estimate is improved by the introduction of a heuristic based on readily available inertial measurements. The method is tested on a vast collection of tests performed in different conditions, showing a satisfactory behavior despite not using any information on the road friction. The extensive experimental validation confirms that the estimate is robust to a wide range of driving scenarios.
Real-time prediction of transient stability after a fault can potentially prevent occurrence of grid blackouts. In this paper, the measurement data obtained from phasor measurement unit (PMU) located ...at generator buses are used to compute the transient stability margin after a fault has occurred. For evaluating various control actions to be taken to stabilize severely disturbed and unstable generators, the stability margin is estimated using the transient energy function (TEF) technique. A look-up table of various modes of disturbance (MOD) is built offline for different fault locations and post fault topology. Following an actual fault, the most probable MODs are ranked by matching the "normalized" kinetic energy with the look-up table. The correct MOD is then chosen based on the lowest normalized potential energy margin and the Controlling Unstable Equilibrium Point (CUEP) is calculated. This technique overcomes previously reported difficulties in finding the CUEP in real-time applications. With knowledge of prefault operating condition obtained from SCADA and the information of the tripped line by analyzing the PMU data, the first swing transient stability margin is computed. The amount of control action needed is subsequently calculated. The proposed method has been tested on the IEEE 39 Bus Test System.
