Characteristic Model Research Articles

Adaptive control schemes based on characteristic model for servo motor drives

AbstractIn the paper, two adaptive control schemes based on characteristic model (CM) method are proposed to achieve great dynamic performance for servo motor drive applications. Two second‐order CMs of permanent magnet synchronous motors (PMSMs) are established based on the difference equation method. An adaptive controller consisting of four control laws is designed based on CMs. The proposed controller implements an improved maintaining/tracking control law that considers load torque ripple, plus a golden‐section adaptive control law, a logic differential control law, and a logic integral control law. In addition, two different servo systems using adaptive controllers are established, both of which are simple to implement and can achieve accurate position‐tracking or speed‐tracking in a servo drive system. The feasibility and effectiveness of the proposed adaptive control schemes are experimentally tested and verified on a servo motor drive platform.

Characteristic model–based generalized predictive control and its application to the parafoil and payload system

SummaryGeneralized predictive control (GPC) has been applied in systems to improve the control performance during the last decades. The designing of predictive model is the essential and vital problem in GPC application. Using characteristic model (CM) as predictive model in GPC can simplify the modeling procedure and improve computing efficiency of GPC. The CM is a kind of simple and easy‐to‐use dynamic model that is equivalent with precise model. Moreover, the creation of CM requires less system information than the practical models. The GPC with CM as predictive model named characteristic model–based generalized predictive control (CMGPC) is proposed in this paper, and it is used in the trajectory tracking control for parafoil and payload system. Simulations and analysis prove that CMGPC is equivalent to traditional GPC and CMGPC is a more efficient way for trajectory tracking of parafoil and payload system compared with conventional proportional integral derivative (PID) method.

Characteristic Model-based Generalized Predictive Control for Electro-hydraulic System with Dead-zones and Parameter Uncertainty

In the paper, the electro-hydraulic system (EHS) is regulated by the proportional directional valve (PDV) which has nonlinearities such as the dead-zone. Furthermore, the effective bulk modulus varies with the temperature and the entrained air in EHS; there exist the nonlinear friction forces and the unmodeled dynamics as well as the square root relationship between the flow rate and the pressure in EHS. All these factors make it difficult to design the controller for EHS. The characteristic model (CM) is used to describe the relationship between input and output of EHS whose exact mathematical model is difficult to obtain. The gradient method through projection modification is utilized to estimate parameters of the CM. The CM-based controller with generalized predictive control (GPC) is designed to make the load track the desired position precisely. Simulations demonstrate that the CM-based GPC controller has a better performance in dead-zone compensation and adaptation with parameter variation than the proportional integral derivative (PID) controller.

Trajectory tracking of powered parafoil based on characteristic model based all-coefficient adaptive control

One of the primary difficulties in using powered parafoil (PPF) systems is the lack of effective trajectory tracking controllers since the trajectory tracking control is the essential operation for PPF to accomplish autonomous tasks. The characteristic model (CM) based all-coefficient adaptive control (ACAC) designed for PPF systems in horizontal and vertical trajectory control is proposed. The method is easy to use and convenient to adjust and test. Just a few parameters are adapted during the control process. In application, vertical and horizontal CMs are designed and ACAC controllers are constructed to control vertical altitude and horizontal trajectory of PPF based on the proposed CMs, respectively. Result analysis of different simulations shows that the applied ACAC control method is effective for trajectory tracking of the PPF systems and the approach guarantees the transient performance of the PPF systems with better disturbance rejection ability.

Modeling and Simulation of an Electronic Oxygen Regulator Based on All-Coefficient Adaptive Control

Safe and reliable automatic pressure regulation of the oxygen mask is a primary consideration for the oxygen supply system. One kind of electronic oxygen regulator (EOR) structure is proposed, and its operation principle is explained in this paper. To avoid long controller design cycle, herein, some simulations are carried out on matlab for analysis by establishing a mathematical model according to the EOR flow dynamic characteristics. In the simulations, the all-coefficient adaptive control method based on a characteristic model (CM) and the proportional–integral–derivative (PID) algorithm are applied, and the results are thoroughly investigated by considering some disturbance, such as the user's changing pulmonary ventilation parameters, the air leakage of the mask, and the sensor noise. Results suggest that the all-coefficient control method is more effective to guarantee superior lower inspiratory resistance than the PID method with the environmental disturbance, which may be a plausible reference for the EOR controller design.

A new approach to fuzzy modeling and control for nonlinear dynamic systems: Neuro-fuzzy dynamic characteristic modeling and adaptive control mechanism

The study on nonlinear control system has received great interest from the international research field of automatic engineering. There are currently some alternative and complementary methods used to predict the behavior of nonlinear systems and design nonlinear control systems. Among them, characteristic modeling (CM) and fuzzy dynamic modeling are two effective methods. However, there are also some deficiencies in dealing with complex nonlinear system. In order to overcome the deficiencies, a novel intelligent modeling method is proposed by combining fuzzy dynamic modeling and characteristic modeling methods. Meanwhile, the proposed method also introduces the low-level learning power of neural network into the fuzzy logic system to implement parameters identification. This novel method is called neuro-fuzzy dynamic characteristic modeling (NFDCM). The neuro-fuzzy dynamic characteristic model based overall fuzzy control law is also discussed. Meanwhile the local adaptive controller is designed through the golden section adaptive control law and feedforward control law. In addition, the stability condition for the proposed closed-loop control system is briefly analyzed. The proposed approach has been shown to be effective via an example.