Absolute Instantaneous Optimal Control Performance Index for Active Vibration Control of Structures under Seismic Excitation

In this paper, an instantaneous optimal control performance index for active control of structures under seismic excitation is analytically proposed. Absolute velocity and absolute displacement terms are implemented to the conventional state vector terms and eventually to the resulting performance index expression. The seismic response reduction effectiveness of the proposed performance index is compared with the linear quadratic regulator control (LQR). For numerical verification of the performance index, an eight-story shear building with a fully active tendon controller system under unidirectional earthquake is considered as the first example. For a more complex model, a three-dimensional tier building under the effect of bidirectional earthquakes is selected as second numerical example. Unidirectional near fault and bidirectional near fault earthquakes are used in the simulations. The control energy demand of each control method is also considered in the comparison. It is obtained from numerical simulations that the proposed performance index is as effective as LQR in attenuating structural vibrations. However, the resulting performance index does not require a priori knowledge of the seismic excitation like the LQR. The nonlinear Riccati matrix equation solution of the LQR is not required in the proposed performance index as well.

Real-Time Control Strategy of Elman Neural Network for the Parallel Hybrid Electric Vehicle

Through researching the instantaneous control strategy and Elman neural network, the paper established equivalent fuel consumption functions under the charging and discharging conditions of power batteries, deduced the optimal control objective function of instantaneous equivalent consumption, established the instantaneous optimal control model, and designs the Elman neural network controller. Based on the ADVISOR 2002 platform, the instantaneous optimal control strategy and the Elman neural network control strategy were simulated on a parallel HEV. The simulation results were analyzed in the end. The contribution of the paper is that the trained Elman neural network control strategy can reduce the simulation time by 96% and improve the real-time performance of energy control, which also ensures the good performance of power and fuel economy.

The Instantaneous Optimal Control Strategy of Parallel Hybrid Loader

In this paper, the instantaneous optimal control strategy of parallel hybrid loader is presented. The aim is to achieve the real time optimal allocation of internal combustion engine (ICE) torque and motor torque in any driving cycle for loader. Thus, all combinations of the ICE torque and the motor torque is determined in any demand torque. Then integrated instantaneous fuel consumption (IIFC) is calculated as a target function, by establishing the equivalent relationship between the electric energy consumption of battery and the fuel consumption, which is converted to the electric energy. When the minimum integrated instantaneous fuel consumption is found, the instantaneous optimal allocation of ICE torque and the motor torque is achieved. Finally in order to verify the functionality of the control strategy, the vehicle and the control algorithm co-simulation model is built on AMESim and Matlab/Simulink platforms. The simulation results show that the strategy is able to improve the fuel economy by more than 10% while ensuring the vehicle power performance.

Optimal Control of the Seismic Responses of High-Rise Structure Using Instantaneous Optimal and Iterative Learning Control

By combining instantaneous optimal control (IOC) and iterative learning control (ILC) , a new hybrid control strategy called ILC_IOC was established. The new hybrid control strategy was derived from the error model of state-space equation and the instantaneous quadratic form of performance function. During the period of control, control forces ware first obtained from traditional instantaneous optimal control, and then, modified by the iterative learning control, during the process of modifying, the amplitudes of control forces were modified. The Benchmark II model was selected as the model for simulating, and the N-S component of the 1940 El wave was selected as the input load. Results of the numerical simulation indicate that, comparing with traditional instantaneous optimal control, the new hybrid control strategy shows to be more effective.