Digital State Feedback Current Control using Pole Placement Technique for the 42V/14V Bi-Directional DC-DC Converter Application

Author(s):  
H.S. Bae ◽  
J.H. Yang ◽  
J.H. Lee ◽  
Bo H. Cho
Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2205
Author(s):  
Muhammad Usama ◽  
Jaehong Kim

This paper presents a nonlinear cascaded control design that has been developed to (1) improve the self-sensing speed control performance of an interior permanent magnet synchronous motor (IPMSM) drive by reducing its speed and torque ripples and its phase current harmonic distortion and (2) attain the maximum torque while utilizing the minimum drive current. The nonlinear cascaded control system consists of two nonlinear controls for the speed and current control loop. A fuzzy logic controller (FLC) is employed for the outer speed control loop to regulate the rotor shaft speed. Model predictive current control (MPCC) is utilized for the inner current control loop to regulate the drive phase currents. The nonlinear equation for the dq reference current is derived to implement the maximum torque per armature (MTPA) control to achieve the maximum torque while using the minimum current values. The model reference adaptive system (MRAS) was employed for the speed self-sensing mechanism. The self-sensing speed control performance of the IPMSM motor drive was compared with that of the traditional cascaded control schemes. The stability of the sensorless mechanism was studied using the pole placement method. The proposed nonlinear cascaded control was verified based on the simulation results. The robustness of the control design was ensured under various loads and in a wide speed range. The dynamic performance of the motor drive is improved while circumventing the need to tune the proportional-integral (PI) controller. The self-sensing speed control performance of the IPMSM drive was enhanced significantly by the designed cascaded control model.


2021 ◽  
Vol 1783 ◽  
pp. 012057
Author(s):  
Iswanto ◽  
Nia Maharani Raharja ◽  
Alfian Ma’arif ◽  
Yogi Ramadhan ◽  
Phisca Aditya Rosyady

2021 ◽  
pp. 107754632110429
Author(s):  
Pouriya Pourgholam ◽  
Hamid Moeenfard

Accurate modeling and efficient control of inverted pendulums have always been a challenge for researchers. So, the current research aims to achieve the following objectives: (I) proposing a comprehensive dynamic model for the inverted pendulums which accounts for the flexibility of the pendulum bar and (II) suggesting an appropriate supervisory fuzzy-pole placement control strategy for stabilizing the pendulum system. Using a Lagrangian formulation, the equations of motion are derived and linearized. Then, a state feedback controller with a reduced-order observer is designed to stabilize the system. Closed-loop simulations reveal that at least six modes shall be considered in the dynamic equations. To improve the quality of the transient response, a novel fuzzy system is developed for real-time assignment of the controller poles. Simulation results demonstrate that the control quality is significantly improved by adding a supervisory fuzzy system to the control loop. The developed approach for dynamic modeling of the system, and the idea of multi-level fuzzy-pole placement control architecture developed in this paper, may be successfully applied to improve the response specifications in other dynamic systems.


2016 ◽  
Vol 28 (04) ◽  
pp. 1650026
Author(s):  
K. Rouhollahi ◽  
M. Emadi Andani ◽  
S. M. Karbassi ◽  
M. Mojiri

Deep brain stimulation (DBS) is one of the most effective neurosurgical procedures to reduce Parkinsons tremor. The conventional method of DBS is open loop stimulation of one area of basal ganglia (BG). On the other hand, existing feedback causes the reduction of additional stimulatory signal delivered to the brain which results in the reduction of the side effects caused by the excessive stimulation intensity. Actually, the stimulatory intensity of the controllers is reduced proportionally by the reduction of hands tremor, which is in fact the intended rehabilitation of the disease. The meaningful objective of this study is to design an architecture of controllers to decrease three criteria. The first one is the hand’s tremor, the second one is the level of delivered stimulation signal to brain in disease condition and the third one is the ratio of the level of delivered stimulation signal in health condition to disease condition. In order to achieve these objectives, a new architecture of a closed loop control system to stimulate two areas of BG at the same time is presented. One area (STN: subthalamic nucleus) is stimulated with a state feedback (SF) controller (pole placement method) and the other area (GPi: globus pallidus internal) is stimulated with a partial state feedback controller (PSFC). Considering these criteria, the results illustrate that stimulating two areas leads to a suitable performance. Simulation results show that the PSF and SF controllers are robust enough to the variations of the system parameters. Moreover, we are able to estimate the parameters of BG model in real time; it is a valuable method to update the time variable parameters of this model.


Author(s):  
M P R Prasad

This paper considers kinematics and dynamics of Remotely Operated Underwater Vehicle (ROV) to control position, orientation and velocity of the vehicle. Cascade control technique has been applied in this paper. The pole placement technique is used in inner loop of kinematics to stabilize the vehicle motions. Model Predictive control is proposed and applied in outer loop of vehicle dynamics to maintain position and velocity trajectories of ROV. Simulation results carried out on ROV shows the good performance and stability are achieved by using MPC algorithm, whereas sliding mode control loses its stability when ocean currents are high. Implementation of proposed MPC algorithm and stabilization of vehicle motions is the main contribution in this paper.


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