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In this paper, a unified control framework is proposed to investigate the multicase finite-time stabilization of stochastic delayed memristor neural networks (SDMNNs). With this framework, stochastic finite-time stabilization (SFTS), stochastic fixed-time stabilization (SFXTS), and stochastic prescribed-time stabilization (SPTS) of SDMNNs are achieved. Subsequently, unlike existing results, a bridge between the proportional-integral (PI) control protocol and the SDMNN is established, allowing the system to perform SFTS/SFXTS/SPTS without a separately designed controller. By appropriately adjusting the control factors of the unified framework, appropriate time of settlement estimates is derived. Furthermore, the controller is improved to the appropriate adaptive controller using the PI control protocol. The SFTS/SFXTS/SPTS of the system are obtained, and the corresponding upper bounds for the estimation of the settling time functions are acquired. Finally, the feasibility of the obtained theoretical results is demonstrated by two examples. [ABSTRACT FROM AUTHOR]

This paper introduces a novel control strategy that merges integral sliding mode control with fuzzy adaptive PI control. This hybrid approach maximizes the benefits of both techniques to ensure voltage stability in DC microgrid. Firstly, a mathematical model characterizes the DC–DC boost converter. Subsequently, a sliding surface, incorporating an integral term, is employed to regulate the converter’s output voltage and current errors. To address uncertainties stemming from factors like input inductance and output capacitance, a dynamic sliding mode controller is formulated. The proposed sliding mode control scheme significantly reduces the time required for voltage stability, curbs system oscillations, and showcases robustness. Furthermore, the inclusion of fuzzy adaptive PI control aids in refining the voltage deviation signal and droop resistance. This enhancement improves the precision of the error tracking system. Finally, the effectiveness of this strategy is demonstrated through MATLAB simulations, supported by experimental validation and analysis. The findings reveal that this control strategy efficiently accelerates the convergence of DC microgrid voltage to a stable state.

To better control the PEMFC air flow, this paper introduces a distributed deep reinforcement learning-based adaptive proportional integral (PI) controller for controlling air flow in a proton exchange membrane fuel cell (PEMFC). In this paper a novel algorithm for tuning the controller is proposed, termed the multi-role exploration strategy distributed deep deterministic policy gradient (MESD-DDPG), which builds upon the deep deterministic policy gradient (DDPG) by employing various trick including a multi-role exploration strategy. In this strategy, explorers and followers with different exploration principles are used for distributed exploration to enhance the exploration efficiency. Moreover, a probability experience replay mechanism is included to foster the training efficiency. In addition, hot start mechanism of experience pool is applied to deal with the reward sparsity problem. The MESD-DDPG also includes a number of innovations for solving the problem of Q-value overestimation. The proposed MESD-DDPG algorithm considers the PI coefficients naturally in the objective and enables the controller better online coefficient adjusting ability via learning, which leads to improved controller adaptability. The simulation results demonstrate the adaptability and the excellent performance of the MESD-DDPG adaptive PI controller.

Optimization of the combustion control process in a tangentially fired pulverized coal boiler, to achieve uniform temperature distribution, is discussed in the paper. This issue is even more critical in those thermal power plants which are not equipped with modern systems for combustion enhancement, such as low NOx burners. Research has shown that the temperature distribution inside the boiler of such power plant can be controlled by adjusting firing, through coal redistribution among the mills. Furthermore, disturbed flame symmetry (i.e. non-uniform temperature distribution in the boiler) is reflected in a large difference between the output temperatures measured on the left and right sides of the boiler. Given the non-stationary conditions typical of thermal power plant boilers, an adaptive control approach is proposed, based on PI controllers which are very popular in industry and widely accepted. Self-tuning of the PI controllers is based on dynamic model parameters derived applying the weighted recursive least squares (WRLS) method to real data recorded at Nikola Tesla B thermal power plant in Serbia, whose nominal power is 650 MW. The same model was later used to test the proposed control approach.

Optimization of the combustion control process in a tangentially fired pulverized coal boiler, to achieve uniform temperature distribution, is discussed in the paper. This issue is even more critical in those thermal power plants which are not equipped with modern systems for combustion enhancement, such as low NOx burners. Research has shown that the temperature distribution inside the boiler of such power plant can be controlled by adjusting firing, through coal redistribution among the mills. Furthermore, disturbed flame symmetry (i.e. non-uniform temperature distribution in the boiler) is reflected in a large difference between the output temperatures measured on the left and right sides of the boiler. Given the non-stationary conditions typical of thermal power plant boilers, an adaptive control approach is proposed, based on PI controllers which are very popular in industry and widely accepted. Self-tuning of the PI controllers is based on dynamic model parameters derived applying the weighted recursive least squares (WRLS) method to real data recorded at Nikola Tesla B thermal power plant in Serbia, whose nominal power is 650 MW. The same model was later used to test the proposed control approach. [ABSTRACT FROM AUTHOR]

This paper proposes a decentralized current-sharing control strategy to endow fast transient response to paralleled dc–dc converters systems, such as dc microgrids or distributed power systems. The proposed controller consist of two main control loops: an external voltage droop control for current-sharing proposes and an internal current loop. The external droop control loop is designed as a voltage loop with embedded virtual impedance, which avoids the use of a slow voltage loop and a separate extra virtual impedance loop that may limit the system bandwidth. The internal current loop, thanks to the external control loop simplification, plays a major role in the system bandwidth, so that an adaptive proportional-integral (PI) controller is proposed for this matter. In the paper, two different droop control methods have been modeling, designed, simulated, and tested: The conventional virtual-impedance-loop based V–I droop and the proposed embedded-virtual-impedance based I–V droop. In order to compare the dynamic response performances between two droop controllers, their state-space models have been developed and analyzed in this paper. The results show that the dynamic response of the I–V droop control is faster than that of the conventional V–I droop control. Furthermore, by analyzing the effects from I–V droop control parameters, the errors can be reduced faster by enlarging the proportional terms, but with no fluctuations, and then completely eliminated by restoring back to small proportional values. Meanwhile, there exists a tradeoff phenomenon between the fast dynamic response and good steady-state performance; thus, an adaptive PI controller is proposed to both improve dynamic response and guarantee good steady-state performance simultaneously. Experimental results are shown to verify the accuracy of the models and the effectiveness of the proposed control framework. [ABSTRACT FROM PUBLISHER]

Owing to the intermittent nature of the renewable energies employed in smart grids, large frequency fluctuations occur when the load frequency control (LFC) capacity is not enough to compensate for the imbalance of generation and demand. This problem may become intensified when the system is working in an island operation mode. Meanwhile, electric vehicles (EVs) are growing in popularity, being used as dispersed energy storage units instead of small batteries in the systems. Accordingly, the vehicle-to-grid (V2G) power control can be applied to compensate for the inadequate LFC capacity and thereby to improve the frequency stability of smart grids, especially in the island operation mode. On the other hand, large scale and complex power systems encounter many different uncertainties. In order to handle these uncertainties, this study proposes a combination of the general type-2 fuzzy logic sets (GT2FLS) and the Modified Harmony Search Algorithm (MHSA) technique, as a novel heuristic algorithm, to adaptively tune the proportional-integral (PI) controller for LFC in islanded MicroGrids (MGs). Although implementing general type-2 fuzzy systems is generally computationally cumbersome, by using a recently introduced plane representation, GT2FLS can be regarded as a combination of several interval type-2 fuzzy logic systems (IT2FLS), each with its own corresponding α level and linguistic rules can directly be incorporated into the controller. This paper further presents a new modified optimization algorithm to tune the scaling factors and the membership functions of general type-2 fuzzy PI (GT2FPI) controller and thereby to minimize the frequency deviations of the MG system against load disturbances more effectively. To evaluate the efficiency of the proposed controller, the obtained results are compared with those of the proportional integral derivative (PID), Fuzzy-PID (FPID), and Interval Type II fuzzy based PI (IT2FPI) controllers, which are the most recent methods applied in this respect. Simulation results demonstrate the perfection and efficacy of proposed controller. [ABSTRACT FROM AUTHOR]

The goal of this study is to introduce a novel robust load frequency control (LFC) strategy for micro-grid(s) (MG(s)) in islanded mode operation. Admittedly, power generators in MG(s) cannot supply steady electric power output and sometimes cause unbalance between supply and demand. Battery energy storage system (BESS) is one of the effective solutions to these problems. Due to the high cost of the BESS, a new idea of Vehicle-to-Grid (V2G) is that a battery of Electric-Vehicle (EV) can be applied as a tantamount large-scale BESS in MG(s). As a result, a new robust control strategy for an islanded micro-grid (MG) is introduced that can consider electric vehicles׳ (EV(s)) effect. Moreover, in this paper, a new combination of the General Type II Fuzzy Logic Sets (GT2FLS) and the Modified Harmony Search Algorithm (MHSA) technique is applied for adaptive tuning of proportional-integral (PI) controller. Implementing General Type II Fuzzy Systems is computationally expensive. However, using a recently introduced α -plane representation, GT2FLS can be seen as a composition of several Interval Type II Fuzzy Logic Systems (IT2FLS) with a corresponding level of α for each. Real-data from an offshore wind farm in Sweden and solar radiation data in Aberdeen (United Kingdom) was used in order to examine the performance of the proposed novel controller. A comparison is made between the achieved results of Optimal Fuzzy-PI (OFPI) controller and those of Optimal Interval Type II Fuzzy-PI (IT2FPI) controller, which are of most recent advances in the area at hand. The Simulation results prove the successfulness and effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]

Electro-mechanical valve actuators (EMVA) are a solution for implementing variable valve actuation in internal combustion engines. Their use can increase engine power, reduce fuel consumption and pollutant emissions, while significantly improving engine efficiency. The control of this actuator is a complex task since non-smooth nonlinearities, parameter variations and external forces strongly affect plant dynamics. In addition, the impact of the valve at its end-strokes translates into mechanical wear and unacceptable noise, and in the worst case the electromagnet may also fail to catch the valve, causing system failure. The design of effective control strategies to ensure valve capture with low impact velocities is therefore essential for the correct functioning of such a mechatronic device. In this paper, the control problem of reducing the impact velocity at 'landing' known in the literature as soft landing control, is tackled via novel cycle-by-cycle adaptive force compensation control algorithms. Two schemes are presented: a discrete adaptive proportional integral controller to regulate landing velocity to a preassigned set-point, and a gradient descent method based controller to automatically achieve the minimum admissible impact velocity. The effectiveness of both methods in limiting landing velocities is shown numerically using a high predictive simulator of the EMVA system, when considering unknown varying environmental conditions, such as internal friction and external gas pressure forces. [ABSTRACT FROM AUTHOR]

Controller design remains an elusive and challenging problem for uncertain nonlinear dynamics. Interval type-2 fuzzy logic systems (IT2FLS) in comparison with type-1 fuzzy logic systems claim to effectively handle system uncertainties especially in the presence of disturbances and noises, but lack a formal mechanism to guarantee performance. In contrast, adaptive sliding mode control (ASMC) provides a robust mechanism to provide system stability against parameter changes and uncertainties, but suffers from chattering phenomenon. In this paper, a stable indirect adaptive interval type-2 fuzzy PI sliding mode controller (AIT2FSMC) is investigated for a class of nonlinear systems in the presence of system's unmodeled dynamics and external disturbances. The added Proportional Integral (PI) structure is used to further attenuate the chattering problem that is common in sliding mode control systems. The interval type-2 fuzzy adaptation law adjusts the consequent parameters of the rules based on a Lyapunov synthesis approach. Mathematical analysis proves the closed loop asymptotic stability, while benefiting from human expert knowledge to improve transient response of the system. Application to two nonlinear systems verifies the robustness of the proposed AIT2FSMC approach in the presence of uncertainties and bounded external disturbances, especially when disturbances have fast changes and large magnitudes. [ABSTRACT FROM AUTHOR]