Radio Networks Predictive Control of a T-type Inverter in Wind Energy

A new controlling method of three level inverters is proposed in this paper. Three-level T inverters are widely used in industry for high-power, medium-voltage power conversion and drives. Topics related to power losses due to commutation and quality of the output current is relevant issues in this power range. The neutral-point balancing problem in this topology is another subject that has been studied in recent years. Among the most common control methods for this converter, the literature states are non linear techniques, like hysteresis control, and linear methods, like the use of PI controllers in conjunction with pulse width modulation. The general predictive control scheme is applied here to the inverter. The behavior of the system is predicted for each possible switching state of this kind of inverter. The switching state that minimizes a given cost function is selected to be applied during the next sampling interval following the same strategy.

A new vehicle path-following strategy of the steering driver model using general predictive control method

In order to build an accurate and effective model, simulation of driver behavior is absolutely essential for the development of advanced driver assistance systems and the current assessment of vehicle handling stability. The purpose of the proposed active steering control is to assist the driver to follow the desired path, especially in situations of the vehicle under strong external disturbance, distracted driver, or other unforeseen circumstances that can cause deviations. Based on the preview optimal simple artificial neural network driver model, an active steering system using general predictive control method is established. In order to improve the path-following capability of vehicles under disturbances, a general predictive controller, with the deviation between the vehicles’ actual and desired lateral positons as inputs and with the corrective steering wheel angle as outputs, is developed to follow the desired path. Meanwhile, adapting recursion least square method with the forgetting factor to estimate the parameters of the controlled auto-regressive and integrated moving average model of the vehicle is designed. The proposed vehicle path-following control system is evaluated in some typical conditions (e.g. under strong crosswind condition in standard double-lane-change, etc.). Simulation results and analysis have verified that this new vehicle path-following strategy, given by general predictive controller, is capable of capturing driver’s steering behavior and the amended driver steering angle can improve the dynamic performance of vehicle under some external disturbances.

Levitation System Controller Design Based on the Implicit General Predictive Control Algorithm

The levitation control system is a key technique of the maglev system. Due to the strong non-linear character of the magnetic force, as well as the model uncertainties and external interferences of the maglev system, the Implicit General Predictive Control algorithm, which adjusts the parameters of the control scheme by using the input and output data, is adopted in this article. Taking the single electro-magnet levitation system as the research object, this algorithm not only guarantees the stability of the system, but also suppresses the vibration caused by the flexibility of the track. The advantages of this algorithm include: the superior control capacity, roll over optimization and little dependence on model. The simulation approves the validity of this method.

Adaptive General Predictive Control Using Optimally Scheduled Multiple Models for Parallel-Coursing Utility Units With a Header

An adaptive general predictive control using optimally scheduled multiple models (OSMM-GPC) is presented for improving the load-following capability and economic profits of the system of parallel-coursing utility units with a header (PUUH). OSMM-GPC is a comprehensive control algorithm built on the distributed multiple-model control architecture. It is improved from general predictive control by two novel algorithms. One is the mixed fuzzy recursive least-squares (MFRLS) estimation and the other is the model optimally scheduling algorithm. The MFRLS mixes the local and global online estimations by weighting a dynamic multi-objective cost function on the membership feature of each sampling point. It provides better parameter estimation on the Takagi–Sugeno (TS) fuzzy model of a time-varying system than the local and global recursive least squares, thus, it is proper for building adaptive models for the OSMM-GPC. Based on high-precision adaptive models estimated by the MFRLS, the model optimally scheduling algorithm computes the regulating efficiencies of all control groups and then chooses the optimal one in charge of the multiple-variable general predictive control. Through the model scheduling at each operation point, considerable fuel consumption can be saved; meanwhile, a better control performance is achieved. Besides PUUH, the OSMM-GPC can also work for other distributed multiple-model control applications.

Intermittent Predictive Steering Control as an Automobile Driver Model

The originality of this paper is the evaluation of intermittent control as a viable candidate to represent an automobile driver in a path tracking scenario. The control algorithm is based on general predictive control where the road curvature is considered known for a horizon in front of the automobile. The computed steering wheel command is used in an intermittent fashion, the intermittence period being one of the system parameter to study. Simulations are carried out and parameters of the driver, the automobile, and the road are varied. An intermittence period range giving satisfactory performances is observed. A comparison is made with actual car/driver behavior measurements for a lane change maneuver. It is concluded that, according to this driver model, there is a wide range of intermittence period that the automobile driver may be operating. Moreover, it is suggested to consider the intermittency of information as an important parameter for vehicle safety systems.