Dual Predictive Control Research Articles

Hybrid LLC Resonant Converter With Partial-Power Auxiliary Unit for Improved Performance

DC-DC converters used in on-board chargers (OBC) of electric vehicles need to meet the requirements of high efficiency, wide output range, and electrical isolation. In this paper, a partial-power processing topology with a wide output range is proposed to overcome the drawbacks of traditional single-stage and two-stage LLC converters. The LLC resonant converter operates near the resonant frequency to maintain high efficiency. While only the partial power is processed by the Buck converter to offer the wide-range output voltage capability. The triangular current mode (TCM) operation is implemented in the Buck converter to minimize the efficiency sacrifice. In addition, to reduce the adverse effect of the second harmonic voltage ripple (SHVR) generated by the AC port on the LLC resonant converter in OBC. A ripple compensation method is proposed to eliminate the SHVR of the LLC resonance converter. Through the dual predictive control for the Buck converter, the SHVR of the LLC resonant converter is offset by the Buck converter. A 2kW rated experimental prototype is designed to convert 400V wide input to 200-500V output. The prototype demonstrates more than 96% efficiency from 30% load to full load at 60-120V input and a 98.2% peak efficiency.

Development of adaptive dual predictive control schemes based on Wiener–Hammerstein models

Adaptive MPC schemes often employ local linear approximation of the system dynamics, which may not be adequate to capture the dynamic behavior of systems exhibiting strongly nonlinear dynamics. In particular, it is challenging to control a system exhibiting input multiplicity behavior at its optimum operating point as the steady-state gain matrix can smoothly become singular at the optimum operating point. In this work, with the aim of controlling systems with fading memory that exhibit such strongly nonlinear dynamics, a novel adaptive dual nonlinear MPC (ADNMPC) formulation is developed based on discrete-time block-oriented models. The block-oriented models are parameterized using the generalized orthonormal basis filters (GOBF). The nonlinear black-box model is further used to formulate a stochastic optimal control problem. A deterministic approximation to the stochastic optimal control problem is then systematically derived. Subsequently, an ADNMPC scheme is developed using the deterministic approximation as a basis. The efficacy of the proposed approach is demonstrated by simulating the problem of controlling a continuously operated fermenter system at its optimum operating point. Analysis of the simulation study shows that in contrast to the conventional nonlinear MPC (NMPC), the proposed ADNMPC scheme injects perturbation to the process whenever a model update is needed while meeting the control objectives. Moreover, with reference to a non-adaptive NMPC controller, the proposed ADNMPC schemes achieve better servo control performance while controlling the fermenter at the singular optimum operating point.

Dual-Predictive Control With Adaptive Error Correction Strategy for AC Microgrids

For AC microgrids with three-phase unbalanced loading conditions, the four-wire voltage source inverters (FW-VSIs) have become an advisable interfaced converter between the source and loads. Predictive control has been applied to FW-VSIs in recent years, but model errors are not solved well, which is caused through parameter mismatch, sampling error and time delay. Thus, the accurate following between reference value and predicted value is difficult to achieve. To this end, a dual-predictive control based on adaptive error correction (DPCEC) for the FW-VSIs is presented. Firstly, the state-space function of FW-VSIs under <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\alpha$</tex-math></inline-formula> <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\beta$</tex-math></inline-formula> 0 reference frame is built. Then, the DPCEC strategy is proposed based on the above issue. Therein, an adaptive error correction strategy is severally embedded into both the outer and inner prediction loop. Noting that the impacts of different negative factors can be simultaneously processed and corrected through adaptive error correction strategy. Not only does the proposed control strategy achieve better performance in steady state, but also retains fast dynamic response in transient state. Finally, simulation and experimental results which verify the high performance of the proposed control techniques are provided.

Dual Predictive Control (Simultaneous Optimization of Identification and Control) and its Periphery

Dual control for uncertain systems considers the dual roles of control inputs for control and online estimation, adding probing signals to control inputs. The control inputs can be precisely optimized by using stochastic dynamic programming, considering all the uncertainties in the control system. This is called “optimal adaptive control”, but this requires a vast online computation. Several years ago, some researchers proposed “dual predictive control”, which approximately optimizes control inputs with nonlinear optimization, requiring a reasonable amount of computation. Recently, the control has been improved to consider the uncertainties in future inputs and output references. In dual control, probing signals behave chaotic, and they should not be too large. In this sense, dual predictive control can be considered to be along, or to move towards, “the edge of chaos”, in terms of the complexity science.

将来の入出力の不確かさを考慮した二元予測制御の評価関数とその計算法

将来の入出力の不確かさを考慮した二元予測制御の評価関数とその計算法