Multi-model Predictive Control Strategy Research Articles

Formula omitted]-gap metric based multi-model predictive control of an ocean current turbine system with blade pitch failures

In this article, a method of combining multiple linear models is developed and used by an advanced model predictive control algorithm for ocean current turbine flight control. The developed model bank consists of faulty and healthy linear models created for the ocean current turbine system. Each linear model is used to compute individual control actions using the model predictive control framework, which are combined to generate the aggregate control action that is applied to the system. A weighted average defined by the ν-gap distance between the current linear approximation of system dynamics and each individual linear system is used to compute the aggregate control action. The multi-model predictive control strategy is applied to control the ocean current turbine to mitigate performance degradation that may occur due to failures in the system. The proposed control framework’s effectiveness is shown with the renewable power generation improvement during a faulty case.

Multiple model predictive control of perching maneuver based on guardian maps

Considering the strong nonlinearity of Unmanned Aerial Vehicles (UAVs) resulting from high Angle of Attack (AOA) and fast maneuvering, we present a multi-model predictive control strategy for UAV maneuvering, which has a small amount of online calculation. Firstly, we divide the maneuver envelope of UAV into several sub-regions on the basis of the gap metric theory. A novel algorithm is then developed to determine the ploytopic model for each sub-region. According to this, a Robust Model Predictive Control based on the Idea of Comprehensive optimization (ICE-RMPC) is proposed. The control law is designed offline and optimized online to reduce the computational expense. Then, the ICE-RMPC method is applied to design the controllers of sub-regions. In addition, to guarantee the stability of whole closed-loop system, a multi-model switching control strategy based on guardian maps is put forward. Finally, the tracking performance of proposed control strategy is demonstrated by an illustrative example.

Supervised Model Predictive Control for Discrete-time Nonlinear Systems with Time-varying Delay

The present paper deals with a decoupled multimodel predictive control based on multi-observer for the control of discrete-time nonlinear systems with time-varying delay. For each local model, a controller based on partial predictor/observer is synthesized. A switching algorithm is established to yield the adequate partial controller ensuring the closed-loop desired performances. Simulation results are given to illustrate the significance of the proposed decoupled multimodel predictive control strategy.

Research of Reheated Steam Temperature Multi-Model Predictive Control for 1000MW Ultra-Supercritical Unit

In this paper, targeted at reheated steam temperatures large time delay and strong nonlinearity characteristics in power plant, a multi-model predictive control strategy is proposed in light of the nonlinear characteristics of steam temperature control. This strategy utilizes the linearized model of nonlinear process at multiple working points to divide the process into several subspaces. A relatively accurate fixed model can be found in each subspace. Based on this, a global approximation model of complex object could be obtained. Simulation and application results show no matter for stable or load varying process, reheated steam temperature can be maintained in the required control range. It is much superior to the traditional PID control method.

Stability analysis of a multi-model predictive control algorithm with application to control of chemical reactors

We study a stabilizing multi-model predictive control strategy for controlling nonlinear process at different operating conditions. The control algorithm is a receding horizon scheme with a quasi-infinite horizon objective function that has finite and infinite horizon cost components. The finite horizon cost consists of free input variables that direct the system towards a terminal region which contains the desired operating point. The infinite horizon cost has an upper bound and steers the system to the desired operating point. The system is represented by a sequence of piecewise linear models. Based on the condition of the system states, the sequence of piecewise linear models is updated and the controller’s objective function switches form quasi-infinite to infinite horizon objective function. This results in a hybrid control structure. A recent approach in the analysis of hybrid systems that uses multiple Lyapunov functions is employed in the stability analysis of the closed-loop system. The stabilizing hybrid control strategy is illustrated on two examples and their closed-loop stability properties are studied.