Advanced Control for Applications最新文献

Electrical motors' effectiveness, optimization, and dependability have led to significant developments in the industrial revolution. Rotor commutation is essential for smooth operation and is done by energizing the proper rotor. The study introduces an innovative methodology for fault-tolerant control of Hall-Effect sensors governing brushless direct current (BLDC) motors. The implemented result validates the substantial saving of energy. The implementation here is unique and effective because the BLDC motor will be operated even if there is a fault in the Hall-Effect sensor. It will run smoothly and efficiently unless the two sensor failures are detected. This method addresses situations where the signal from a Hall Effect sensor stays consistently low or high for short periods. This developed system is mature enough to handle the subsystem when the data from the sensor detects faults, identifies faulty signals, and creates a replacement signal. The proposed research work uses MATLAB Simulink and the TI-DSP (TMS320F2812) KIT to show the high-speed simulation. The simulation results were validated through different motor speeds from 3500 to 2000 rpm. Drawbacks like cost and reliability have been overcome in the implemented research work. In the implemented research work, the simulation outcome shows approximately 10% better results in generating more torque than the existing methods using sinusoidal windings.

Gantry cranes of the H-type with dual electric-motor actuation are widely used in industry. In this article, the control problem of an H-type gantry crane which is driven by a pair of linear permanent magnet synchronous motors (dual PMLSMs) is considered. The integrated system that comprises the H-type gantry crane and its two LPMSMs is proven to be differentially flat. The control problem for this robotic system is solved with the use of a nonlinear optimal control method. To apply the nonlinear optimal control method, the dynamic model of the H-type gantry crane with dual LPMSM undergoes approximate linearization at each sampling instant with the use of first-order Taylor series expansion and through the computation of the associated Jacobian matrices. The linearization point is defined by the present value of the system's state vector and by the last sampled value of the control inputs vector. To compute the feedback gains of the optimal controller an algebraic Riccati equation is repetitively solved at each time-step of the control algorithm. The global stability properties of the nonlinear optimal control method are proven through Lyapunov analysis. The proposed control scheme achieves stabilization of the H-type gantry crane with dual LPMSMs without the need of diffeomorphisms and complicated state-space model transformations.

This article proposes a new Wireless EV charging system with a single stage boost assisted flyback (SSBAFB) inverter. Also, this presents Model predictive (MP) control for the proposed system. The key merits of the proposed SSBAFB inverter are better efficiency (boost stage optimizes the energy transfer), cost-effectiveness, possibility of integrating with renewable sources. The significant features of MP control are predictive performance, better dynamic response, multi-objective control, robustness, and many more. In the literature, two MP controls were employed, one on the transmitter side and the other on the receiver side for controlling efficiency and load regulation respectively. The proposed MP-controlled SSBAFB inverter-based WPT system utilizes single MP control, which can attend to both load regulation and efficiency enhancement. To validate the performance of the proposed WPT system, a 3.3 kW laboratory model has been developed. The steady-state and dynamic performance of the proposed system under closed-loop control exhibits better performance. The comparison of the proposed converter with possible converter topologies shows that the proposed converter stands out in producing high efficiency. A comparative study of the proposed MP controller with other conventional controls proves that the proposed MP controller offers better steady-state stability and fast dynamic responses.

Most electric cars and wind turbines employ switched reluctance motors (SRM), but it has some disadvantages, namely high torque ripple because of its power supply mode and multiphase communication. Model predictive torque control (MPTC) with sailfish optimization (SFO) method is proposed to reduce torque ripple of SRM using torque sharing function (TSF). To develop an efficient torque ripple algorithm, the flux-linkage characteristic curves are first acquired at protected rotor trial and create an accurate SRM model. It predicts future operation for drive system in SRM architecture. Second, the SFO algorithm is employed to enhance TSF parameters also to minimize the torque value of SRM. Then, the TSF-based MPTC method is developed to avoid problem like conversion of frequency produced by controller. Finally, atom search optimization (ASO) is used to adjust sensor for correct rotor position of the SRM. To verify the performance of the proposed method, MPTC-SFO is compared with direct instantaneous torque control (DITC) method. Proposed MPTC-SFO method attained more efficient result of 12.79% reduced torque ripple than DITC.

The design of non-linear control systems remains a challenge today, therefore through this work a procedure to obtain a vertex-reduced multi-model representation, without loss of convexity, is proposed as a suitable solution. That is, a novel approach which considers all parameter variations around the Continuous Stirred Tank Reactor (CSTR) system operating region is developed, resulting in a unique polytopic representation. After that, based on the linear matrix inequalities approach, a control scheme is developed to compute the optimal matrix gains, while the operating, states and inputs, constraints are satisfied and the stability conditions are ensured. Finally, the realistic simulation results highlight the model representation effectiveness in capturing the CSTR dynamic behavior in the operating region, despite parameter variations, allowing the optimal control law design, overcoming the non-linear system nature, to achieve the desired closed-loop system performance.

The time constant is often an important indicator parameter to evaluate the response speed of thermocouples. However, in practical applications, the output signal suffers from interference and deformation due to the excitation signal not satisfying the desired step size, data transmission delay, and the effect of mechanical vibrations, which do not match the output response of the system of the same order. In addition, the measurement results are not reproducible due to the subjective influence of the manual measurement method. In this paper, we propose a method to compute the time constant. The output signal is modified by filtering and fitting to enable the automatic calculation of the time constant, and the parameters of the algorithm are discussed. We developed a thermocouple time constant measurement device using a water bath and laser excitation conditions, obtaining response signals from thermocouples with different wire diameters and laser powers. The results demonstrate the effectiveness of the proposed time constant calculation methods in accurately capturing the changing trend of thermocouples with different wire diameters and laser powers. These methods successfully meet the requirements for processing the dynamic response signals of thermocouples under various excitation conditions.

Switched reluctance motors (SRMs) have gained popularity in various industrial applications due to their advantages, including structural simplicity, high reliability, low cost, and operational stability over a wide speed range without relying on rare-earth permanent magnet materials. However, these motors exhibit drawbacks such as weak torque density, low efficiency, and significant torque ripple, particularly in high-speed operation. An efficient converter-based control approach is proposed to manage speed and torque variations in SRM motors, addressing these issues. A multilevel converter (MC) is introduced as a fundamental component. The novel multilevel converter (NMC) accommodates SRMs with varying numbers of phases and exhibits quick demagnetization and excitation behaviors, enabling independent operation of each phase even during conduction overlaps. Subsequently, an effective controller is developed for the SRM motor, combining proportional integral derivative (PID) control with enhanced fire hawks optimization (EFHO). The EFHO optimizes the PID gain values to enhance controller performance. The converter operation minimizes torque ripple and facilitates speed management. The EFHO technique is a fusion of fire hawks optimization (FHO) and the sine cosine algorithm (SCA). This amalgamation improves the updating process of FHO by incorporating SCA. The proposed methodology is implemented in MATLAB and evaluated through various metrics, including SRM motor current, voltage, speed, and torque, under electric vehicle (EV) load conditions. Performance comparisons are conducted with traditional optimization algorithms such as the whale optimization algorithm (WOA) and ant colony optimization (ACO). The results validate the effectiveness of the proposed methodology in achieving improved SRM motor control and performance.