Good Steady-state Performance Research Articles

Model predictive control for Vienna rectifier with constant frequency based on inductance parameters online identification

When the finite control set model predictive control (FCS-MPC) is applied to the three-level converter, there are problems such as large current harmonics, high requirements for the computing efficiency of the microcontroller, complex multi-objective optimization and limited output vector switching. In addition,the mismatch of inductance parameter may directly affect the observation accuracy of FCS-MPC. To solve the above problem,a double vector model predictive control strategy with constant frequency strategy based on the inductance parameters on-line identification is presented.First, a single objective cost function based on the direct power is constructed by optimizing the redundant vector which is selected to balance the neutral-point potential, the design of weighting factor is avoided.At the same time, in order to effectively reduce the grid current ripple under single vector modulation, space vector pulse width modulation (SVPWM) is realized by combining with zero vector control mode. And, a parameter identification method based on model reference adaptive system(MARS) is proposed to overcome the effect of model parameter mismatch.Finally, the results show that the proposed F-MPCCF has good steady-state and dynamic performance from the static, transient and neutral-point potential control.

Improved Model Predictive Speed Control of a PMSM via Laguerre Functions

This paper proposes a model predictive speed control strategy for a surface-mounted permanent magnet synchronous motor by applying Laguerre functions. The model predictive controller (MPC) incorporates an integrator. A quadratic programming procedure is applied to solve the constrained optimization problem online. The paper also provides a solution for stability. The performance efficiency of the proposed scheme is validated by comparing the results with the performance of an optimal linear quadratic regulator, conventional state-space model predictive control, and a simple MPC algorithm with integral action. Extensive simulation results confirm the efficacy of the proposed scheme, showing that it achieves good steady-state performance while maintaining a fast dynamic response.

Direct thrust control for variable cycle engine based on fractional order PID-nonlinear model predictive control under off-nominal operation conditions

With the development and complexity of aero-engines, aero-engine control has been transformed to provide proper thrust and limit protection function under the framework of multivariable control. The multi-operating modes and multi-control variables of the variable cycle engine (VCE) lead to the complex and changeable conversion between measurable variables and thrust, making the traditional indirect thrust control hard to ensure the accuracy and safety of thrust control in different operation conditions. In this paper, a direct thrust control method based on fractional order PID-nonlinear model predictive control (FOPID-NMPC) algorithm is proposed to achieve the tracking of thrust and the limitation of parameters of the VCE under nominal and off-nominal operation conditions. In view of the existing thrust estimator designs which are only suitable for nominal conditions, the individual differences and performance degradation are fully investigated. After setting the label which represents degradation mode and constructing the health index which represents degradation degree instead of estimating health parameters directly, a dual margin degradation pattern classifier is constructed based on random forest (RF), and a long short-term memory (LSTM) neural network-based thrust estimator is designed to jointly constitute the estimation module. Then, FOPID and NMPC are combined to ameliorate the control quality and ensure the accuracy and security of thrust control at different operating conditions. This method can escape from local optimum when solving multi-extremum optimization problems. The comparative simulation of the proposed FOPID-NMPC with PID-NMPC and NMPC is performed and the influence of controller parameters on thrust response is explored. Simulations show that the designed controller has better dynamic performance, and also has good steady-state performance and safety.

Structured nonlinear process modeling using neural networks and application to economic optimization

This paper presents a model identification approach to develop structured dynamic models that utilize both the available first principles knowledge and neural networks (NNs) estimated from data. The NNs in the dynamic model are used to approximate some complex unknown functions that may be challenging to model in applications using the available first principles knowledge. The parameters in the NNs are estimated by solving a multistep ahead prediction error minimization problem. The efficacy of the modeling approach is demonstrated via case studies with an illustrative chemical reactor and an industrially relevant styrene polymerization process. In the latter example, we use NNs to approximate unknown reaction kinetics and polymer moment functions in the structured model. The economic performances of the structured models are analyzed by solving a steady-state optimization problem. First, we elucidate that to obtain a good steady-state economic performance using the structured models, the training data collected from the plant should contain sufficient steady-state information. In the styrene polymerization example, we examine the economic performances of two structured models with different NN parameterization choices. We show that a structured model that utilizes the most possible physical information about the process provides a median loss of 6.4% compared to the optimal performance of the true plant across a range of steady-state problems. We emphasize in the case studies that physical insight about the process is critical to obtain good economic performance using the structured models.

Application of the MPPT Control Algorithm Based on Hybrid Quantum Particle Swarm Optimization in a Photovoltaic Power Generation System

The Maximum Power Point Tracking method is a mainstream method for improving the operational efficiency of photovoltaic power generation, but it is difficult to adapt to the rapidly changing environment and lacks good steady-state and dynamic performance. To achieve fast and accurate tracking of the Maximum Power Point Tracking, the optimization of the contraction expansion coefficient of the Quantum Particle Swarm Optimization algorithm is studied, and then the Levy flight strategy is introduced to optimize the algorithm’s global convergence ability, thereby constructing the Hybrid Quantum Particle Swarm Optimization algorithm. Finally, the Hybrid Quantum Particle Swarm Optimization combined with the Maximum Power Point Tracking algorithm is obtained. The research results showed that the Hybrid Quantum Particle Swarm Optimization combined with the Maximum Power Point Tracking algorithm can always converge to the theoretical minimum value with a probability of more than 94% in the Roserock function and Rastigin function tests. The tracking error of the Hybrid Quantum Particle Swarm Optimization combined with the Maximum Power Point Tracking algorithm was less than 1% under lighting conditions. The convergence time of the Hybrid Quantum Particle Swarm Optimization combined with the Maximum Power Point Tracking algorithm in arbitrary shadow occlusion environments can reach a stable state within 0.1 s. In summary, the Hybrid Quantum Particle Swarm Optimization combined with the Maximum Power Point Tracking algorithm proposed in the study has excellent performance and very wide applicability. To a certain extent, it improves the total power generation capacity of the photovoltaic power generation system and the power generation efficiency of the photovoltaic array.

Grid-Connected Phase-Locked Loop Technology Based on a Cascade Second-Order IIR Filter

The moving average filter-based phase-locked loop (MAF-PLL) can obtain grid synchronization signals accurately under adverse grid conditions with a large amount of harmonics due to the high filtering capability of the MAF. However, MAF-PLL cannot achieve a fast dynamic response in the case of frequency drift, phase angle steps, and unbalanced voltage sag. MAF is essentially an FIR filter, and its filtering performance is hard to be adjusted. To address this issue, this paper proposes an alternative to MAF consisting of a set of cascading second-order IIR filters (CIIRF). Based on MAF, CIIRF introduces multiple zeros and poles from the zero–pole replacement perspective, and by changing the position of the poles, the filter performance can be adjusted. To improve the anti-interference ability of PLL based on CIIRF (CIIRF-PLL) in the presence of grid frequency drift, a frequency-adaptive scheme is also proposed. Simulation and experimental results show that CIIRF-PLL can accurately track the grid voltage phase in the case of frequency steps, phase angle jumps, harmonics injection, and unbalanced voltage sag and has good steady-state and dynamic performance.

Three-Level Inverter-PMSM Model Predictive Current Control Based on the Extended Control Set

In the neutral point clamped (NPC) three-level inverter-permanent magnet synchronous motor system, traditional model predictive current control (MPCC) uses the system predictive model to traverse the 27 basic voltage vectors, to achieve the d-q axis current component and neutral point voltage of the multi-objective optimal control. Finite control set model predictive control predicts the state change of the control target at future moments based on a finite number of switching states of the inverter. The control principle of this method is simple and easy to implement, but the control effectiveness of this control strategy is limited because only one basic vector can be selected as the optimum output per control period. In this paper, a model predictive current control strategy based on an extended control set (ECS-MPCC) is proposed, which can improve the control performance of the system by extending the control set to select multiple vectors in a single control period compared to the traditional strategy. In addition, to address the disadvantage of extending virtual space vectors leading to an increase in computation, this paper proposes a fast search method for optimal vector based on region reduction. The proposed method avoids the optimization process traversing all virtual space vectors, thus enabling a fast search for the optimal vector. The experimental results show that the proposed control strategy has good steady-state and dynamic performance.

Research on Push-Pull Energy Storage PWM Power Drive of High-Power High-Response Proportional Solenoid

As the traditional power drive circuit is difficult to meet the requests of high-power high-frequency proportional solenoid fast drive, this paper proposes a push-pull energy storage PWM power drive circuit. The circuit is equipped with an energy storage module, which releases energy when the proportional solenoid coil is charged, supplements the output of the power supply current, and shortens the arrival time of the steady-state current. When the coil is discharged, it recovers energy and shortens the time for the coil current to return to zero. In this paper, a theoretical model of push-pull energy storage power drive circuit is established, and simulation analysis and experimental verification are carried out for a proportional solenoid coil with rated current of 3.3A and power of 79W. The results show that the push-pull storage PWM power drive has excellent input-output linearity, minimal ripple, and good steady-state current output performance. Compared with the traditional single switch and reverse discharging power drives, the coil charging speed under the push-pull energy storage type power drive is increased by 25%, and the discharge speed is increased by 95% and 45% respectively.

Study on Active Control of Stick-slip Vibration of Drill String System

The stick-slip vibration phenomenon of drill string widely exists in the drilling process, which has a great negative impact on drilling equipment, efficiency, cost and safety. In order to effectively suppress the stick-slip vibration of the drill string system, a two-degree-of-freedom torsional vibration model of the drill string system is established considering the nonlinear friction between the drill bit and rock. Based on this model, an optimal LQR controller is designed to suppress the stick-slip vibration of drill string, and the driving torque is fed back in real time through the state error. The effectiveness of the controller under parameter perturbation is verified by simulation analysis. The results show that the controller can effectively suppress the stick-slip vibration of drill string system under the desired speed, feedforward torque, start time and bit weight parameter perturbation, achieve real-time feedback of driving torque, and ensure that the angular velocity of rotary table and BHA can be tracked and maintained at the desired speed in a short time. The control effect shows good steady-state error and dynamic performance. The research results have a certain reference significance for suppressing stick-slip vibration of drill string system and improving drilling efficiency.

A Novel Optimal Voltage Vector Selection Control Strategy for Vienna Rectifier

At present, model predictive control (MPC) is widely used in power electronic converters. The objective function is typically utilized to select the optimal voltage vector, but the switching frequency is not fixed or too high during the selection process. Therefore, to address this issue, a novel optimal voltage vector selection control strategy for Vienna rectifier is proposed in this paper. Specifically, hysteresis link is introduced into finite control set-model predictive direct power control (FCS-MPDPC). The optimization criterion is changed from the traditional objective function tracking error minimization to the longest extension step in the hysteresis loop. As a result, the switching frequency can be reduced and the control algorithm can be simplified by redefining the optimization criteria of the rectifier output voltage vector. Moreover, according to the power control model of Vienna rectifier, three-level space vector pulse width modulation (SVPWM) is used to generate the switching function for the next cycle. Finally, an experimental platform for Vienna rectifier based on dSPACE hardware is built to verify the feasibility of the proposed strategy. Experimental results show that the control strategy can ensure the rectifier to run normally and exhibit good steady state performance.

A Novel Methodology to Enhance the Smooth Running of the PM BLDC Motor Drive Using PWM-PWM Logic and Advance Angle Method

This paper addresses the active torque ripple compensation of a permanent magnet brushless direct-current motor (PMBLDC) drive with a new pulse width modulation (PWM) technique and advance angle method. Torque ripple is a well-known problem in BLDC motors which is produced by a discrepancy between the stator current and the back electromotive force (back-emf) waveforms. The advanced angle method proposed in this paper generates a maximum torque in the PM BLDC motor by decreasing the displacement between the phase voltage and phase current in proportion to the load. Further, a simple and comprehensive PWM-PWM logic is proposed in this paper to reduce the torque ripples. The test results show that the BLDC motor drive achieves good steady-state performance while maintaining a quick dynamic response. The performance of the PWM-PWM logic and advance angle method, have been tested and compared with the practical results for the characteristics of DC bus voltage, DC bus current, electromagnetic torque, shaft torque, mechanical torque, phase voltage, phase current and PWM signal.

Performance of a vector controlled PMSM drive based on proportional–resonance control under single-phase open circuit fault

In this paper, a vector controlled PMSM drive based on a proportional–resonance controller (PR) under open circuit fault (OCF) is proposed. The PR controller is used to generate the stator voltages. While the field-oriented control (FOC) approach has been implemented using the pulse width modulation (PWM) technology. The simulation is built using MATLAB/ Simulink. Hardware experiments were performed, and the results captured using dSPACE DS1104. The behaviour of the PMSM is studied under three operations: healthy, faulty, and post-fault operation. Moreover, the speed control of the PMSM under the three operations has been presented. The simulation and the experimental results show a fast dynamic response with some ripple in the response of speed and torque during the healthy and post-fault operations. On the other hand, during the OCF, the ripple in the torque and speed response increases. However, the proposed method proves its efficacy by keeping the motor speed nearly at the input reference speed as well as guaranteeing the continued operation of the PMSM with good steady-state and dynamic performance.

Neural-Observer-Based Terminal Sliding Mode Control: Design and Application

In view of the fact that most devices are nonlinear systems, this article proposes a recurrent probabilistic compensation fuzzy neural network (RPCFNN) control scheme based on global fast terminal sliding mode control (GFTSMC) for a class of nonlinear systems with uncertainties. First, GFTSMC is developed to impose a finite-time convergence feature for the considered systems. Second, a novel RPCFNN framework is designed to further enhance the ability to deal with uncertainty. Due to the added probabilistic estimation and dynamic fuzzy operator, the developed RPCFNN controller possesses superior nonlinearity handling capability. Meanwhile, the stability of RPCFNN is proved by the Lyapunov theory. Finally, active power filter is taken as the representative of nonlinear systems to verify the effectiveness and feasibility of the proposed method. Simulation and experimental results show that the novel RPCFNN controller has good steady-state and dynamic performance and has excellent robustness to deal with uncertain disturbances.

An Available-to-Implement Thermal Facility for Dynamic Bleed Air Test of Aircraft Environmental Control System

As a critical system of onboard aircraft equipment, the environmental control system (ECS) has an essential impact on flight safety. The performance of the ECS is usually tested using the thermal test facility. The facility comprises temperature and pressure simulation units to simulate the engine bleed air. Currently, the ECS often fails due to the dynamic and rapid changes in the temperature and pressure of bleed air. To achieve the dynamic bleed air simulation, the most critical problem is to simulate the bleed air’s rapid heating and boost process during the actual engine working process. However, the temperature simulation unit has the characteristics of nonlinear and large inertia. Moreover, temperature and pressure control are strongly coupled. These characteristics usually lead to temperature and pressure dynamic control failure. This paper introduces a novel facility that adopted the hot and cold blending method to realize the rapid response of the temperature. Furthermore, it used a particular system structure to reduce pressure and temperature control coupling. In addition, it adopted the lookup-table-based PID (LPID) controller to acquire the rapid response and good steady-state performance of temperature and pressure control. Experimental control results are presented and discussed. The results showed that the facility could meet ECS’s dynamic and steady-state test requirements. The novel facility makes up for the insufficient dynamic test capacity of the previously developed ECS test facilities.

Design and Simulation of Satellite Attitude Control Algorithm Based on PID

According to the tasks of mapping and reconnaissance, the satellite needs to adjust its attitude and keep its attitude stable when it is in orbit. Firstly, the kinematic model and dynamic model of satellite attitude are established.Then the digital simulation of satellite attitude control system is built based on the mathematical model of satellite attitude, in which the control modules use PID controller and fuzzy PID controller respectively. Finally, the steady-state performance and dynamic performance of the two controllers are compared and analyzed.The experimental results show that both fuzzy PID control and fuzzy PID control can be used as effective means of attitude control.Both of them have high precision, fast speed and good steady-state performance to achieve the desired attitude. Fuzzy PID control also has the ability of self-tuning controller parameters, which makes the control process smoother.

Linear Quadratic Regulator Optimal Control with Integral Action (LQRIC) for LC-Coupling Hybrid Active Power Filter

Renewable energy generation and nonlinear load devices will generate harmonics and reactive power to power grids, resulting in current distortion and low power factors. To solve the power quality problems, the LC-coupling hybrid active power filter (LC-HAPF) is proposed, with lower DC-link voltage and lower cost compared with conventional active power filters (APFs). The LC-HAPF requires a controller to operate, therefore, hysteresis current control (HCC) and proportional current control (PCC) were proposed. However, they both result in significant steady-state error. Hence, linear quadratic regulator control (LQRC) with integral action (LQRIC) is proposed for the LC-HAPF in this paper to mitigate the steady-state error. The d-q-0 coordinate state-space model of the LQRIC-controlled LC-HAPF is derived, and a detailed design guideline of the weighting matrices Q and R of LQRIC is given. By the state-space model and weighting matrices, the gain matrix K of LQRIC can be acquired by MATLAB, thus a good steady-state performance can be ensured. Finally, the simulation results of different controllers for the LC-HAPF under 40V and 50V DC-link voltages are given to verify the effectiveness of the proposed LQRIC. The experimental results of LQRIC-controlled LC-HAPF are also given to verify the feasibility of the proposed LQRIC.

Instantaneous Reactive Power Control Based on Five-Level Inverter for Grid Connected PV System Improvement

Grid-connected photovoltaic systems have become the most important and popular use of the solar energy. In this paper, a photovoltaic system connected to a three-phase network is presented. The system is connected in a single step using a five-stage voltage source inverter to improve the quality of the power in the distribution system. The control strategy applied for the optimal operation of the photovoltaic system is based on the theory of instantaneous reactive power. This strategy is simple and has the advantage of running the system all day without using Phase-Locked Loop (PLL). The multilevel inverter switching signals are derived from the Pulse Width Modulated Current Controller, which provides good dynamic performance in both steady state and transient conditions. The voltage of the continuous bus capacitors is controlled using the PI controller. The validation of this proposed system is carried out through simulation with different levels of irradiations and by a comparison with the performance of a two-level inverter used in the original configuration. This specific configuration shows good results regarding the waveforms of the voltages and the output currents as well as the level of the quality of the energy represented in this paper by the THD parameter that has been studied in comparison with the two-level converter.

Investigation of Torque Ripple Behaviour for Five-Level Inverter-Fed Modified Model Predictive Torque Control-Based PMSM Drive

This paper investigates the minimization of torque ripple in Permanent Magnet Synchronous Motor (PMSM) Drive using the Modified Model Predictive Torque Control (MMPTC) with multilevel inverters. The MMPTC grabs the good steady-state performance as of Model Predictive Current Control (MPCC) and provides direct torque control as in the Model Predictive Torque Control (MPTC). The MMPTC strategy also eliminates weighting factors in the cost function, unlike conventional Model Predictive Torque and Flux control (MPTFC). The torque ripple causes vibrations and noise and produces more heat loss in the drive, which leads to focus on the torque ripple reduction. Among the various torque ripple minimization approaches, the multilevel inverter configuration clutched more attention in electric drives due to rapid development in the semiconductor technology. Analysis of torque ripple behaviour of PMSM drive with MMPTC using two-level (2L), three-level (3L), and five-level (5L) inverters is carried out under different running conditions in MATLAB/SIMULINK software tools and also implemented in real-time with the help of dSPACE1104 controller.

Deadbeat two-vector model predictive current control for open-winding primary permanent-magnet linear motor

The structure of open-winding motor fed by the dual-inverter can increase the winding terminal voltage of the primary permanent-magnet linear motor (PPMLM). So, it is an effective method to improve the thrust performance of the PPMLM. Based on the detailed derivation of the mathematical model of the open-winding PPMLM and the in-depth analysis of the space voltage vector distribution characteristics of the dual-inverter, a deadbeat two-vector model predictive current control (MPCC) model is proposed in this paper. First, the optimal vector of the inverter 1 is determined by judging the position of the large sector. Second, two optimal vectors of the inverter 2 is determined by judging the position of the small sector. Finally, the duty cycle of the two optimal vectors of the inverter 2 is calculated to realize the proposed deadbeat two-vector MPCC algorithm. Compared with the traditional model predictive torque control (MPTC) and MPCC strategies, the proposed algorithm has the advantages of lower current harmonics (lower 34 % and 44 % under rated conditions, respectively) and better thrust performance (higher 77 % and 61 % under rated conditions, respectively) as well as lower computational complexity. Simulation and experimental results show that the proposed two-vector MPCC has good steady-state and dynamic performances, which can reduce the current harmonics and thrust ripple, and thus improve the motor drive performance compared with the traditional method.

Feedforward Model-Based Intelligent Compensation System for Spindle Dynamic Thermal Error

Among spindle dynamic errors, thermal error is an important factor affecting machining precision, the most convenient and economical solution for which is dynamic compensation of thermal error. In view of defects of complicated system, complex debugging, poor stability, high cost, etc., in the existing compensation approaches, thermal error compensation technology is investigated with compensation model established to complete the system design. The system is tested on the spindle of precise horizontal machining center. In the experiment, multiple linear regression, BP neural network, etc. are taken for technical analysis with dynamic feedforward model established. The experimental results indicate that the compensation system under study has dynamic compensation rate of over 90%, while the spindle has greatly shorter warm-up time, which can operate normally before entering the thermal steady state. The finally completed feedforward model-based intelligent compensation control system has good steady-state and dynamic compensation performance. In particular, its self-correction function greatly improves system adaptability and simplifies operation in use.