Internal Model Sliding Mode Controller for Mass flow system based on sensed flow rate

Multiport DC-DC converters have been most commonly used in stand-alone renewable power systems to provide smooth and constant power to the loads. To supply the load without any interruption under various disturbances, the centralised controller in various typologies of multiport converter has been implemented only by using conventional proportional integral (PI) controller. This study presents the output voltage control of the three-port full-bridge converter interfacing photovoltaic (PV) system with three different controllers. The dynamic response of the proposed converter is analysed with the PI controller, fuzzy logic controller (FLC), and the sliding mode controller (SMC). The state space model of the three-port full-bridge converter is developed to analyse the performance using SMC. PI, FLC, and SMC-based control scheme is developed using the MATLAB/Simulink environment. The response of the PI, FLC, and SMC are analysed for line regulation, load voltage regulation, and for the converter parameter variation. The simulation results illustrate that the SMC gives a better steady state and dynamic response when compared with the PI and FLC. The results are presented to confirm the proposed control schemes.

Augmenting effectiveness of control loops of a PMSG (permanent magnet synchronous generator) based wind energy conversion system by a virtually adaptive PI (proportional integral) controller

To improve the performance of vehicle-grid system under multiple operation conditions and suppress the low-frequency oscillation (LFO) in electrified railways, this paper proposed a control strategy of electric multiple units (EMUs) traction line-side converter (LSC) based on sliding mode control (SMC). First, the accurate model of EMUs is established and then the mathematical model of it in the d-q frame is derived. Then, the application of sliding mode variable structure control strategy in the LSC of EMUs is presented in detail, which includes the design of outer loop voltage sliding mode surface and inner loop current control law. Moreover, the control performance for EMUs based on SMC and traditional linear proportional integral (PI) control is compared under multiple operation conditions in the case of one traction drive unit of EMUs and multi-EMUs, respectively. Finally, experiments are implemented on the hardware in the loop simulation platform based on software StarSim. The experiments results show that, compared with PI control, SMC owns better control performance, such as the lower total harmonic distortion of traction line-side current under multiple conditions, lower voltage fluctuation when braking occurs, better anti-interference ability with the change of system parameters, and can effectively suppress the LFO occurs under operation mode.

Sliding Mode – PI controller is recognized as robust controller with a high stability in a wide range of operating conditions, although it suffers from chattering problem. In addition, it cannot be directly applied to multi switches power converters. This paper concerns a sliding controller design with the proportional-integral (PI) control form. First, a theoretical analysis of the considered sliding PI controller is provided. Our analysis reveals that the proportional control term of the sliding PI controller, i.e., sliding proportional controller, can reduce the chattering problem and thus, the robustness can be established. Second, the chattering problem is eliminated by smoothing the control law in a narrow boundary layer, and a pulse width modulator produces the fixed frequency switching law for the inverter. The smoothing procedure is based on limitation of pulse width modulator. Therefore, the chattering problem of the proportional gain is resolved and the valuable robust control property of the sliding integral controller is illustrated again. The simulation model has been developed and tested using MATLAB software.

With the qualitative development of DC microgrids, the usage of different loads with unique conditions and features is now possible in electric power grids. Due to the negative impedance features of some loads, which are called constant power loads (CPLs), the control of DC power converters faces huge challenges from a stability point of view. Despite the significant advances in semiconductors, there is no upgrade in the control of gate drivers to exploit all potential of power electronic systems. In this paper, quantum computations are incorporated into artificial intelligence (AI) to stabilize a full-bridge (FB) DC-DC boost converter feeding CPL. Aiming to improve the bus voltage stabilization of the FB DC-DC boost converter, a quantum deep reinforcement learning (QDRL) control methodology is developed. By defining a reward function according to the specification of the FB power converter, the desired performance and control objectives are fulfilled. The main task of QDRL is to adjust the control coefficients embedded in the feedback controller to suppress the negative impedance effect resulting from deploying the CPLs. By deploying the potential advantages of quantum fundamentals, the deep reinforcement learning improved by quantum specifications will not only enhance the performance of the DRL algorithm on conventional processes but also advance related research areas such as quantum computing and AI. Unlike the basic quantum theory, which requires real quantum hardware, QDRL can be executed on classic computers. To examine the feasibility of the QDRL scheme, hardware-in-the-loop (HiL) examinations are conducted using the OPAL-RT. The comparison of the proposed controller with the classic state-of-the-art methodologies reveals the superiority and feasibility of QDRL-based control schemes in both the transient and steady-state conditions to other schemes. Analysis using various performance criteria, including the integral absolute error (IAE), integral time absolute error (ITAE), mean absolute error (MAE), and root mean square error (RMSE), demonstrates the dynamic improvement of the proposed scheme over sliding mode control (approximately 50%) and proportional integral control (approximately 100%).

Parallel robots are nowadays used in many high-precision tasks. The dynamics of parallel robots is naturally more complex than the dynamics of serial robots, due to their kinematic structure composed by closed chains. In addition, their current high-precision applications demand the innovation of more effective and robust motion controllers. This has motivated researchers to propose novel and more robust controllers that can perform the motion control tasks of these manipulators. In this article, a two-loop proportional–proportional integral controller for trajectory tracking control of parallel robots is proposed. In the proposed scheme, the gains of the proportional integral control loop are constant, while the gains of the proportional control loop are online tuned by a novel self-organizing fuzzy algorithm. This algorithm generates a performance index of the overall controller based on the past and the current tracking error. Such a performance index is then used to modify some parameters of fuzzy membership functions, which are part of a fuzzy inference engine. This fuzzy engine receives, in turn, the tracking error as input and produces an increment (positive or negative) to the current gain. The stability analysis of the closed-loop system of the proposed controller applied to the model of a parallel manipulator is carried on, which results in the uniform ultimate boundedness of the solutions of the closed-loop system. Moreover, the stability analysis developed for proportional–proportional integral variable gains schemes is valid not only when using a self-organizing fuzzy algorithm for gain-tuning but also with other gain-tuning algorithms, only providing that the produced gains meet the criterion for boundedness of the solutions. Furthermore, the superior performance of the proposed controller is validated by numerical simulations of its application to the model of a planar three-degree-of-freedom parallel robot. The results of numerical simulations of a proportional integral derivative controller and a fuzzy-tuned proportional derivative controller applied to the model of the robot are also obtained for comparison purposes.

DC microgrids are gaining popularity due to their lack of reactive power compensation, frequency synchronization, and skin effect problems. However, DC microgrids are not exempted from stability issues. The stability of DC microgrids based on decentralized architecture is presented in this paper. Centralized architecture can degrade system performance and reliability due to the failure of a single central controller. Droop with proportional integral (PI) controller based on decentralized architecture is being used for DC microgrid stability. However, droop control requires a tradeoff between voltage regulation and droop gain. Further, global stability through PI controller cannot be verified and controller parameters cannot be optimized with different operating conditions. To address limitations, an equivalent sliding mode (SM) controller is proposed for a DC microgrid system in this paper. Detailed simulations are carried out, and results are presented, which show the effectiveness of an equivalent SM controller.

The main problem of using a Proportional Integral (PI) Controller in Brushless Direct Current (BLDC) motor speed control is tuning the PI’s parameter and its performance cannot adapt to the system behavior changes. Particle Swarm Optimization (PSO) has been chosen to optimize the tuning. Fuzzy Logic Controller (FLC) is used to online tuning PI’s parameters to adapt to system conditions. Optimal adaptive PI, which combines the PSO method and FLC method to tune PI, is proposed. It was successfully implemented in the simulation environment. The test was carried out in three conditions: step responses, set-point changes, and disturbance rejection. The proposed algorithm is superior with no overshoot/undershoot. Whereas in terms of settling time is in between PI and PI-PSO. PI controller has the smallest control effort. However, the other parameter is the worst. PI-PSO is superior in terms of settling time and Integral of Absolute Error (IAE) except for the step response test. The proposed method has lower IAE and higher control effort by 78.73 % and 60 % compared to PI control. On the other hand, it has a higher IAE dan lower control effort by 11.82 % and 33.88 % compared to PI-PSO. Therefore, the optimal adaptive PI control can reduce energy consumption compared to optimal PI with better performance than PI control.

Renewable energy-based power generation, particularly photovoltaic (PV)-connected grid systems, has gained popularity in recent years due to its widespread adoption for residential and commercial customers of all sizes, from kilowatt (KW) to megawatt (MW). The purpose of this work is to demonstrate how an efficient CUK-integrated boost converter with continuous current flow may be used to maximise the output of solar arrays. The constant voltage at the converter output is maintained with increased dynamic performance using a Proportional Integral (PI) controller based on a hybrid optimization technique GWO-PSO (Grey Wolf Optimization-Particle Swarm Optimization). This hybrid solution permits accurate and speedy monitoring of the PI controller, which reduces voltage ripples successfully. This system is connected to the 1 grid via a voltage source inverter (VSI). To ensure grid synchronisation, the inverter’s command pulses are generated by a PI controller depending on the grid’s actual power. The Total Harmonic Distribution (THD) of grid current is used to characterize the overall system. The THD is determined to be 1.8 percent with the suggested PI controller and 3.41 percent with the PI controller, and the tracking performance of the GWO-PSO algorithm is 99.8 percent.

Proportional Integral (PI) controller has attracted researchers in industrial control processes because of its simplicity and robust performance in a wide range of operating conditions. It has been used to provide proportional delay differentiation on web servers in previous work. However, PI controller cannot achieve satisfactory results due to (1) the web server's non-linearity properties, and (2) the difficulty of building an accurate model for the web server. To address these issues, a nonlinear fuzzy PI controller is proposed in this paper, which has the advantage of fuzzy controller while maintaining the simplicity and robustness of PI controller. The proposed controller are self-tuned according to the periodical online performance measurement. The experimental results demonstrate that our fuzzy PI controller outperforms the PI controller in several aspects.

Greenhouse system (GHS) is the worldwide fastest growing phenomenon in agricultural sector. Greenhouse models are essential for improving control efficiencies. The Relative Gain Analysis (RGA) reveals that the GHS control is complex due to 1) high nonlinear interactions between the biological subsystem and the physical subsystem and 2) strong coupling between the process variables such as temperature and humidity. In this paper, a decoupled linear cooling model has been developed using a feedback-feed forward linearization technique. Further, based on the model developed Internal Model Control (IMC) based Proportional Integrator (PI) controller parameters are optimized using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to achieve minimum Integral Square Error (ISE). The closed loop control is carried out using the above control schemes for set-point change and disturbance rejection. Finally, closed loop servo and servo-regulatory responses of GHS are compared quantitatively as well as qualitatively. The results implicate that IMC based PI controller using PSO provides better performance than the IMC based PI controller using GA. Also, it is observed that the disturbance introduced in one loop will not affect the other loop due to feedback-feed forward linearization and decoupling. Such a control scheme used for GHS would result in better yield in production of crops such as tomato, lettuce and broccoli.

The article focuses on the issues of Load Frequency Control (LFC) under non-linear strategies in multi source multi area hydro thermal system.On practical perspective dead band, boiler dynamics, reheat steam turbine along with hydro turbine operating under two different area capacities are considered in the system.When subjected to random load variations in both the areas, the system exhibits higher oscillations.The speed governor matches the generation with the demand.The offset in the area frequencies and tie-line power is removed by using secondary Proportional Integral (PI) controller.The PI controller is tuned using Ziegler Nichols' (ZN) and Fuzzy Gain Scheduling (FGS) method.The influence of high Proportional (P) controller gain during steady state and high Integral (I) controller gain during transient affects the system performance.Variable Structure System (VSS) helps to switch from P to PI controller during transient to steady state based on control error.The concept of VSS is applied to Fuzzy Gain Scheduling (FGS) PI controller.The performance of the optimal Variable Structure Fuzzy Gain Scheduled (VSFGS) controller under non-linear environment is judged and validated using performance indices.

The rising demand for high-performance permanent magnet synchronous motors (PMSMs) is responsible for the development of PMSM speed control. Although the proportional-integral controller is often used in field-oriented control (FOC) for motor speed regulation, it has drawbacks like slow response and instability. This paper proposed an enhanced sliding mode controller with a modified sliding surface to achieve better speed control performance. In comparison to proportional-integral or PI controller, fuzzy logic controller, conventional sliding mode controller, the proposed control approach uses a reaching law that incorporates a fuzzy logic controller. A smoother and faster response time is targeted by the proposed approach compared to conventional sliding mode control. Practical small-scale PMSM experiments certify the effectiveness of our proposed enhanced sliding mode control.

The main objective of this paper is to control the speed of separately excited DC Motor using a Sliding mode controller based on VSS approach. This controller is based on variable structure systems which aim at reducing the peak overshoot, steady state error and settling time of a DC Motor. In the first stage, PI controller is used to control the speed of DC Motor. A model is developed and simulated using MATLAB/SIMULINK. Later on the same is done with sliding mode controller. The speed control of DC Motor using both PI and Sliding mode controllers is studied and the results are compared. The simulation results show that Sliding mode controller is superior than PI for speed control of DC motor. Since the SMC is robust in presence of disturbances, the desired speed is perfectly tracked.

In various industries, the high efficiency and endurance of a Brushless DC (BLDC) motor. It is an important component in control system of electromechanics speed. A Brushless DC motor has been commonly used in various industries because of its high efficiency, endurance. Appropriate controller design in the response mode of BLDC motor speed enhances the efficiency in controlling motor speed. The objective of this research is to propose the Proportional Integral (PI) controller to stabilize the motor speed. A PI controller can be formulated in terms of K P and K I , a proportional and an integral gain parameters, respectively. The increase in proportional gain results in the system’s response is increasing. For suitable integral gain parameter of system, the exceeding steady-state error in the system can be eliminated by the increase of integral gain. The increase of high efficiency of motor’s response to control the motor can be applied to DSP TMS320F28335 board. For testing and simulation of controlling BLDC motor speed, the experiment’s parameters were determined as follows: 24 V, 350 W, and rotational speed of motor 3,000 rpm. The experiment of the system was based on state space model. The experimental result showed that controlling non-load motor speed would be adjusted by PI controller during steady state with reference signal varying with time. When the load was supplied the motor, the rotational speed of motor would decrease and return to its steady state in which the motor response was faster active in the reference signal system. Consequently, the PI controller of controlling BLDC motor’s rational speed is an alternative and efficiency.