Harnessing monotonicity to design an adaptive PI passivity-based controller for a fuel-cell system

With the diversified growth of power system, though there are many key issues which must be focused, addressing the power quality issues has become a pivotal role of an electrical engineer. Power quality issues are mainly concerned with maintaining the waveform and amplitude of the source voltage to the required reference level, mitigating the harmonics in the load current waveforms, etc., The resolving of power quality issues has been made feasible by the development of Power Electronics based devices. Though there are a wide range of controllers, Unified Power Quality Conditioner (UPQC) is a capable device used for the upgrading the quality of power due to its speedy response, high steadiness and little cost. A UPQC is used to compensate voltage sags, voltage swell and harmonics. It can protect sensitive loads against the voltage variations or disturbances. UPQC has two converters that are joined to a common DC link with an energy storage capacitor. In this paper, the proposed system consists of wind energy coupled UPQC being employed for power quality enhancement viz. Voltage sag, Voltage swell on the source voltage and current harmonics mitigation on the source current. Adaptive PI controller has been used for creation of reference signals for both shunt and series APFs and Park's transformation has been implemented for the control strategy which aids the adaptive PI controller. Both open loop and closed loop controller using adaptive PI controller have been studied in this paper and simulation results observed for the proposed system using MATLAB SIMULINK software also have been presented as sustenance for the control strategy proposed. Therefore, this paper aims at proposing a system with a control strategy that can give a fast and reliable wanted response during the operating condition of the system varies. It means, any change made in the external condition of the proposed system will not produce any abnormal effects. Consequently, an adaptive PI (A-PI) control based UPQC is used for voltage regulation and mitigation of current harmonics and the results have been presented.

Brushless direct current motor(BLDCM) are used to drive many systems in numerous fields. The control of BLDCM with basic drive techniques is required to obtain the desired output. Although basic drive techniques may be unsatisfactory in meeting these demands, they have found an invariable place for themselves due to their easy-to-use advantages. Due to these reasons, many researchers have focused on how innovative solutions are developed. In this paper, an adaptive PI controller is proposed to control the current of BLDCM drives. This paper aims to design a PI controller with time-varying gains for current regulation. The adaptive PI, improving the steady-state response, is constructed by one adaptation rule and a classical PI. In addition, the stability analysis is proved with Lyapunov theory. To demonstrate the effectiveness of the proposed controller, several simulations are performed with comparisons. The simulations with a classical PI and high-gain current controller comparisons are presented for set-point and sinusoidal references, and 500 rpm and 1500 rpm motor speeds. Comparing the classical PI with adaptive controller, the adaptive controller improves the current performance from 0.3442 to 0.0656 for 500 rpm, and from 0.4703 to 0.1552 for 1500 rpm in RMS of the current errors for 2A reference current. Similarly, the outcomes of comparing the high-gain controller to the adaptive PI show that the designed controller reduces RMS of the currents errors from 0.1853 to 0.1611 for 1500 rpm with 2A reference current, and from 0.1879 to 0.1720 for 1500 rpm with a sinusoidal reference current.

<div class="htmlview paragraph">To compete with internal combustion engines fuel cell system must operate and function at least as well as conventional engines. The fuel cell system power properties depends on the air and hydrogen feed, flow and pressure regulation, and heat and water management. Choosing an air flow control method is very important decision that defines many characteristics of the fuel cell system. The air supply method of the cathode side of the fuel cell can contribute to improvement of the system performance. A fuel cell is a stochastic object. Application an adaptive extremum control with bi-parameter identification provide to automatical adjusting the parameters of a controller to the changing characteristics of an object. The aim of control algorithm was air flow control of the fuel cell in order to find and hold the maximal value of net power produced by the fuel cell stack, regardless of changes of the parameters of the object of control and its outer environment. Presented algorithm was capable of reaching the aim of control. The structure of an adaptive controller was simplified on purpose. Since such a simple controller, armed only with an ability to learn, a more complex structure of an algorithm can only improve the result. The presented adaptive control system of the PEM fuel cell is a general solution and can be used for other types of fuel cells systems of both high and low power.</div>

In this paper, we propose adaptive nonlinear controllers for the Single-Ended Primary Inductance Converter (SEPIC). We also consider four distinct situations: AC-DC, DC-DC, Continuous Conduction Mode (CCM) and Discontinuous Conduction Mode (DCM). A comparative analysis between classic linear and nonlinear approaches to regulate the control loop is made. Three adaptive nonlinear control laws are designed: Feedback Linearization Control (FLC), Passivity-Based Control (PBC) and Interconnection and Damping Assignment Passivity-Based Control (IDAPBC). In order to compare the performance of these control techniques, numerical simulations were made in Software and Hardware in the Loop (HIL) for nominal conditions and operation disturbances. We recommend adaptive controllers for the two different situations: Adaptive Passivity-Based Feedback Linearization Control (APBFLC) for the PFC (Power Factor Correction) AC-DC system and IDAPBC-BB (IDAPBC Based on Boost converter) for the regulator DC-DC system.

The passivity-based control (PBC) is not robust and it relies upon the system model. Moreover, partial differential equations (PDE) are encountered during its designing process which are difficult to be solved and in some cases unfeasible. In this article, reinforcement learning (RL) designs the PBC parameters via solving PDE online. RL and adaptive control are employed in order to make the nonlinear closed-loop system robust against the disturbance and model uncertainty. Through the utilisation of adaptive control technique, the passivity-based controller design along with learning could be executed as though the disturbance within the system could also be eliminated. The simulations and the comparison made with the previous methods manifest the greater advantage and superiority of the proposed method.

Passivity-based control of cascaded multilevel converter based D-STATCOM integrated with distribution transformer

Wind energy is considered as one of the rapidest rising renewable energy systems. Thus, in this paper the wind energy performance is enhanced through using a new adaptive fractional order PI (AFOPI) blade angle controller. The AFOPI controller is based on the fractional calculus that assigns both the integrator order and the fractional gain. The initialization of the controller parameters and the integrator order are optimized using the Harmony search algorithm (HSA) hybrid Equilibrium optimization algorithm (EO). Then, the controller gains (Kp,Ki) are auto-tuned. The validation of the new proposed controller is carried out through comparison with the traditional PID and the Adaptive PI controllers under normal and fault conditions. The fractional adaptive PI improved the wind turbine's electrical and mechanical behaviors. The adaptive fractional order PI controller has been subjected to other high variation wind speed profiles to prove its robustness. The controller showed robustness to the variations in wind speed profile and the nonlinearity of the system. Also, the proposed controller (AFOPI) assured continuous wind power generation under these sharp variations. Moreover, the active power statistical analysis of the AFOPI showed increase in energy captured of around 25 %, and reduction in the standard deviation and root mean square error of around 10% compared to the other controllers.

An adaptive proportional and integral controller for digital speed control in electrical drives is derived and analysed. Adaptation of the controller parameter is gradient descent procedure. The adaptive controller is derived from the discrete-time model of a digitally controlled speed servomechanism. Discrete-time model is obtained from the Z-transforms of the system signals and elements. Applied controller has distributed structure, i.e. integral action is placed in the direct path, while proportional action is positioned in the feedback path of the system. Thus, it provides improved system accuracy, while it maintains sufficient level of system relative stability. Due to presence of feedback in the system and controller structures, gradient of criterion function is computed through a recurrent procedure. Performance analysis of the speed servo-mechanism with the adaptive proportional and integral controller is performed in complex domain, using root locus techniques. The analysis indicates controller parameter influence on the system stability and reference tracking capability. Further, it shows that the adaptive controller yields zero steady state error and step disturbance rejection, as well as good system performance in the presence of modelling errors. Experiments, performed as reference tracking tasks, support the analysis.

This paper proposes an idea for designing a continuously tuned adaptive PI controller for a non-linear process such as conical tank. In this paper, a simple tuning system is used to continuously tune the controller parameters in correspondence with the change in operating points. For each stable operating point, a FOPTD model was identified using process reaction curve method. The estimated model parameters are used to calculate the controller parameters for each operating points. Based on these calculated controller parameters and its operating points, a tuning system was created. The tuning system will able to interpolate and extrapolate the relation between control variable and the controller parameters over entire span of control variables. Finally, a detailed time-domain modeling of the conical tank was performed. Then the adaptive PI controller was implemented in Matlab and was simulated to verify its performance. Thus the adaptive controller was able to produce a consistent response regardless of parametric variations with minimum overshoots and minimum settling time.

Passivity-based adaptive trajectory control of an underactuated 3-DOF overhead crane

Passivity-Based Iterative Learning Control for Spacecraft Attitude Tracking on SO(3)

Adaptive control for robust air flow management in an automotive fuel cell system

In this paper, a passive-based adaptive robust super-twisting nonlinear controller (PBARSNC) is proposed for high accuracy torque tracking control of the novel electro-hydraulic loading system with disturbances and uncertainties. The construction of the stability of this electro-hydraulic control system is given using passivity theory that results in a passivity-based controller (PBC). Considering parameter uncertainties and constant or slowly varying disturbances, adaptive law is adopted in the passivity-based controller. Furthermore, super-twisting second-order sliding mode control is used to reject modeling uncertainties and matched disturbances. Passivity theory, adaptive method and super-twisting algorithm are synthesized via the recursive design method. The proposed passive-based adaptive robust super-twisting nonlinear control can guarantee the torque tracking performance in the presence of various uncertainties, which is very important for high-accuracy tracking control of hydraulic servo systems. Extensive simulations are carried out to verify the high-accuracy tracking performance of the proposed control strategy.

As road traffic develops, energy-saving and efficient street lights have become a key research field for relevant professionals. To reduce street lights energy consumption, a fireworks algorithm is used to optimize the membership function parameters of fuzzy control and the initial parameters of PI control. A fireworks algorithm improved adaptive fuzzy PI solar LED street light control system is designed. The results showed that in the calculation of Root-mean-square deviation and Mean absolute error, the Root-mean-square deviation of the adaptive fuzzy PI control system improved by the fireworks algorithm was 0.213, 0.258, 0.243, 0.220, and the Mean absolute error was 0.143, 0.152, 0.154, 0.139, respectively, which proved that the prediction accuracy was high and the stability was good. In the calculation of the 1-day power consumption of the solar LED intelligent control system, the average power consumption of the designed solar LED intelligent control system was about 2000W, which was 25.9%, 47.4%, and 42.9% lower than the other three control methods, respectively. This proves that its energy consumption is low, and its heat generation is low, and the battery service life is long. The research and design of an adaptive fuzzy PI control solar LED street light intelligent control system has good performance, which can effectively achieve intelligent management and energy conservation and emission reduction in smart cities.

Presents the design of an adaptive fuzzy PI controller to control the speed of a separately excited DC motor by self-tuning. The speed of the motor can be changed by controlling the armature and field voltages. In the paper, the adaptive fuzzy PI controller is designed to control the armature voltage while the field voltage is fixed as a constant. The fuzzy system tunes the gains of the PI controller according to error and change of error of the speed control system to meet the step response specifications of the motor speed. The experimental results show that the step responses of the speed of the DC motor controlled by the adaptive fuzzy PI controller have step responses with small overshoot when compared to the usual PI cascade controller. That is, the proposed controller gives the controlled system small overshoot and zero steady-state error when the reference speed is 2000 rpm at no load, half load and full load. The effect of the applied step full load and released load at steady state is also rapidly rejected.