Journal of Control Science and Engineering

This paper presents an adaptive inertia weight particle swarm optimization (AIWPSO) employed for solving the multiobjective weight optimization problem of LQR applied for the vehicle active suspension system (ASS). To meet the competing control objectives of ASS including the ride comfort, road handling, and suspension travel, the state feedback controller design for ASS is formulated as an optimization problem and an improved PSO is employed for finding the optimal weights of the linear-quadratic regulator (LQR). Specifically, for solving the premature convergence of the particles and imbalance between exploration and exploitation capabilities of PSO, an adaptive inertia weight that updates the velocity of the particles based on the success rate is used. The efficacy of the AIWPSO-tuned LQR is experimentally tested on a quarter-car ASS plant using the hardware in loop (HIL) testing for an uneven road surface. Experimental results highlight that, compared to conventional PSO-tuned LQR, the proposed scheme can significantly minimize the vehicle body acceleration due to irregular road profile while guaranteeing the minimum tire friction for passenger safety. The ISO 2361-1 standards adopted to evaluate the ride and health criteria substantiate that the proposed scheme reduces the vibration dose value by 25.34% for a bumpy road profile. Moreover, the cumulative power spectral density (CPSD) of vehicle body acceleration assessed in both low- and high-frequency regions manifests the significant improvement in the ride comfort.

Packet classification algorithms have been the focus of research for the last few years, due to the vital role they play in various services based on packet forwarding. However, as the number of rules in the rule set increases, not only the preprocessing time but also the memory consumption is increasing greatly. In this paper, we first model and analyze the above issue in depth. Then, a fast, smart packet classification algorithm based on decomposition is proposed. By boundary-based rule traversal and smart rule set partitioning, both the preprocessing time and memory consumption are reduced dramatically. Experimental results show that the preprocessing time of our method achieves 8.8-time improvement at maximum compared with the PCIU and achieves about 31.5-time improvement on average compared with CutSplit for large rule sets. Meanwhile, the memory overhead is reduced by 40% at maximum and 27.5% on average compared with the PCIU.

This paper described that the stator current-based model reference adaptive system (MRAS) speed estimator is used for the induction motor (IM) indirect vector speed control without a mechanical speed sensor. Due to high sensitivity of motor parameters variation at low speed including zero, stability analysis of MRAS design is performed to correct any mismatch parameters value in the MRAS performed to estimate the motor speed at these values. As a result, the IM sensorless control can operate over a wide range including zero speed. The performance of the stator current-based MRAS speed estimator was analyzed in terms of speed tracking capability, torque response quickness, low speed behavior, step response of drive with speed reversal, sensitivity to motor parameter uncertainty, and speed tracking ability in the regenerative mode. The system gives a good performance at no-load and loaded conditions with parameter variation. The stator current-based MRAS estimator sensorless speed control technique can make the hardware simple and improve the reliability of the motor without introducing a feedback sensor, and it becomes more important in the modern AC IM. The sensorless vector control operation has been verified by simulation on Matlab and experimentally using Texas Instruments HVMTRPFCKIT with TMS320 F28035 DSP card and 0.18 kw AC IM.

This paper deals with the problem of observer design of the vehicle model which is represented by Takagi–Sugeno (T–S) fuzzy systems with the presence of uncertainties. A relaxed observer design is presented to estimate the unmeasurable states and the faults of the vehicle lateral dynamics model, simultaneously. The vehicle model is transformed into a system with unknown inputs. Then, it is rebuilt by adding the default to the system state equation. Based on the Lyapunov function approach and the introduction of some slack variables, sufficient conditions of unknown input observer design are formulated as Linear Matrix Inequalities (LMIs). Finally, the simulation section clearly shows the importance and effectiveness of the proposed strategy.

The smart grid and electric vehicles (EVs) are widely used all over the world. As the key role, the Vehicle-to-Grid (V2G) has been attracting increasing attention. The bidirectional grid-connected AC/DC converter is one of the indispensable parts in the V2G system, which can realize bidirectional power flow and meet the power quality requirements for grid. A three-phase bidirectional grid-connected AC/DC converter is presented in this paper for V2G systems. It can be used to achieve the bidirectional power flow between EVs and grid, supply reactive power compensation, and smooth the power grid fluctuation. Firstly, the configuration of V2G systems is introduced, and the mathematical model of the AC/DC converter is built. Then, for bidirectional AC/DC converters, the grid voltage feedforward decoupling scheme is applied, and the analysis of PI control strategy is proposed and the controller is designed. The system simulation model is established based on MATLAB/Simulink, and the experiment platform of the bidirectional grid-connected converter for V2G is designed in lab. The simulation and experiment results are shown, and the results evaluate the effectiveness of the model and the performance of the applied control strategy.

The allocation issues of the location of the cargo have affected the operational efficiency of retail e-commerce warehouses tremendously. Adjusting the cargo location with the change of the order and the operation of the warehouse is a significant research area. A novel approach employing the FP-Tree and the Artificial Fish Swarm Algorithms is proposed. Firstly, energy consumption and shelf stability are employed for the location-allocation. Secondly, the association rules among product items are obtained by the FP-Tree Algorithm to mine frequent list of items. Furthermore, the frequency and the weight of product items are taken into account to ensure the local stability of the shelf during data mining. Thirdly, another method of the location-allocation is obtained with the objectives of the energy consumption and the overall shelf stability along with the frequent items stored nearby that is conducted by the Artificial Fish Swarm Algorithm. Finally, the picking order distance is obtained through two methods of the location-allocation above. The performance and efficiency of the novel introduced method have been confirmed by running the experiment. The outcomes of the simulation suggest that the introduced method has a higher performance concerning criterion called the picking order distance.