In this paper, an industrial grade adaptive control scheme is proposed for a micro-grid integrated dual active bridge driven battery management system (DIBMS). A benchmark industrial grade adaptive control scheme depends on two factors namely robustness and computational resource utilization when such controllers are implemented over processors. The mathematical model of DIBMS system is nonlinear, thus for desired response, non-linear controllers based on sliding mode variable structure control theory suits it well for the state regulation problem of DIBMS, however such controllers utilize high computational resources when practically implemented over processors. Keeping in view the above performance indices, this paper proposes an industrial grade computationally efficient and finite time adaptive robust convergent control for DIBMS system. A proportional integral (PI) scheme is used as central control unit and Hebbian algorithm with double integration of the state error is introduced for online tuning the gains of central control unit. The robustness and computational resource efficiency of the proposed control paradigm is validated using a laboratory scale test bench through TI Launchpad (TMS320F28379D). The superiority of the proposed AI based PI control paradigm is compared with classical PI, integer order sliding mode control (SMC), and fractional order SMC (FOSMC) in terms of computational resource utilization and robustness under all test conditions.

Single phase Dual Active Bridge (DAB) has found numerous applications in modern energy architectures such as direct current (DC) microgrid, electrical vehicle charging and high voltage direct current (HVDC) system. Due to the model complexities of DAB, this work proposes a model free adaptive control method based on artificial neural network (AANN) which is capable of adjusting the weights online in finite time. The finite time learning property of the proposed controller makes it perfectly robust for the compensation of the disturbances due to source and load side variations. A proportional integral (PI) controller is used to stabilize the nominal dynamics of the system along with the AANN controller. The structure of the proposed controller is as simple as PID controller and as robust as any nonlinear control method. The AANN-PI controller is implemented on TI Launchpad (TMS320F28379D) with a 50 Watts laboratory scale DAB test bench. Finally, the performance of the AANN-PI method is compared experimentally with classical PI and sliding mode controllers. INDEX TERMS Artificial neural network, control system, dual active bridge, dc-dc converters.

The majority of marine current conversion technologies are based on permanent magnet synchronous generators (PMSG) due to its numerous advantages such as high-power density, low cost, and favorable electricity production. However, nonlinear properties of the generator and parameter uncertainties, makes the controller design more than a simple challenge. This paper proposes a new adaptive passivity-based (PB) modified super twisting algorithm (PBSTA) for control performance improvement (low tracing errors, fast convergence response, robustness) of a PMSG based marine current energy conversion system under swell effect and parameter uncertainties. The proposed approach combines a new PB current control (PBCC) with a new adaptive modified super twisting algorithm through a fuzzy logic supervisor. A new adaptive fractional order PID (FO-PID) controller is introduced to design the desired dynamics of the system. The main contributions and motivation of this work include the extraction of maximum power from the tidal current, integrating it to the grid and making the closed loop system passive. This is possible by reshaping system energy and introducing a damping term that compensates the nonlinear terms by a damped way and not by cancellation. Two steps are needed to design the proposed controller: the first step includes the derivation of reference current based on the reference torque using adaptive FO-PID. In the second step, the overall control law is computed by the proposed PBSTA. The exponential stability and error convergence of the proposed controller are analytically proven. The developed controller is tested under parameter variations and it is compared to benchmark nonlinear control methods such as sliding mode. Extensive investigation under MATLAB/Simulink, demonstrates clearly that the proposed technique provides higher efficiency and robustness over the benchmark nonlinear control methods.

No abstract

No abstract