Continuous Model Predictive Control Research Articles

Three-Dimensional Impact Angle Guidance Laws Based on Model Predictive Control and Sliding Mode Disturbance Observer

This paper presents a suboptimal three-dimensional guidance law to intercept unknown maneuvering targets with terminal angle constraint using multivariable control design. The presented guidance law is essentially a composite control method, which is constructed through a combination of standard continuous model predictive control (MPC) and adaptive multivariable sliding mode disturbance observer (SMDO). More specifically, the MPC method is utilized to obtain optimal line-of-sight (LOS) angle tracking performance for nonmaneuvering targets, while the SMDO technique is used to estimate and compensate for the unknown target maneuver online. By virtue of the adaptive nature, the proposed guidance law does not require any information on the bounds of target maneuver and its gradient except for their existence. The stability of the closed-loop guidance system is also analyzed by using Lyapunov function method. Simulation results clearly confirm the effectiveness of the proposed formulation against a maneuvering target.

Game Theoretic Model Predictive Control for Distributed Energy Demand-Side Management

Distributed energy generation and storage are widely investigated demand-side management (DSM) technologies that are scalable and integrable with contemporary smart grid systems. However, prior research has mainly focused on day-ahead optimization for these distributed energy resources while neglecting forecasting errors and their often detrimental consequences. We propose a novel game theoretic model predictive control (MPC) approach for DSM that can adapt to real-time data. The MPC-based algorithm produces subgame perfect equilibrium strategies for distributed generation and storage with perfect forecasting information, and is shown to be more effective than a day-ahead scheme when mean forecasting errors greater than 10% are present. This robust and continuous MPC approach reduces effective forecasting errors, and in doing so, achieves greater electricity cost savings and peak to average demand ratio reduction than the day-ahead optimization scheme.

Continuous nonlinear model predictive control of a hybrid water system

Incorporating weather forecasts in the control of land surface water levels requires predictions of the net inflow to the water system. This net inflow is the combined flow of an incoming load (rain, evaporation, etc.) and outgoing pump rates. Because the pump costs are considerable, optimal pump schedules have minimal energy consumption. Model predictive control (MPC) is able to compute, revise and apply such optimized schedules by incorporating a model of the water system. The pumps typically cause discontinuities in the model, which leads to mathematical complications. Avoiding advanced solving techniques for these hybrid systems, this paper introduces an alternative that enables pure continuous MPC by smoothing the jumps. Although the resulting underlying model is continuous, it is also highly nonlinear. This requires use of the specialized class of nonlinear model predictive control (NMPC), which is able to cope with the arising nonlinearities. Control inputs computed by these methods can be translated to the original hybrid system by a final post-processing step. This paper presents the outlined scheme, and verifies it by applying an optimized NMPC implementation (the DotX nonlinear predictive controller, DNPC), equipped with the approximated continuous nonlinear model, to a real-life hybrid water system.

Computer-Automated Multiparadigm Modeling in Control Systems Technology

The use of model-based technologies has made it imperative for the development of a feedback control system to deal with many different tasks such as: plant modeling in all its variety; model reduction to achieve a complexity or level of abstraction suitable for the design task at hand; synthesis of control laws that vary from discrete event reactive control to continuous model predictive control, their analyses, and testing; design of the implementation; modeling of the computational platform and its operating system; analysis of the implementation effects; software synthesis for different platforms to facilitate rapid prototyping, hardware-in-the-loop simulation, etc. Throughout these tasks, different formalisms are used that are very domain specific (e.g., tailored to electrical circuits, multibody systems, reactive control algorithms, communication protocols) and that often need to be coupled, integrated, and transformed (e.g., a block diagram control law in the continuous domain has to be discretized and then implemented in software). Significant improvements in many aspects (performance, cost, development time) of the development process can therefore be achieved by: 1) relating and integrating these different formalisms; 2) automatic derivation of different levels of modeling abstractions; and 3) rigorous and tailored design of the different formalisms by capturing the domain (or meta) knowledge. The emerging field of computer automated multiparadigm modeling (CAMPaM), presented in this paper in the context of control system design, aims to develop a domain-independent formal framework that leverages and unifies different activities in each of these three dimensions.