The digital implementation of model predictive control (MPC) is fundamentally governed by two design parameters; sampling time and prediction horizon. Knowledge of the properties of the value function with respect to the parameters can be used for developing optimisation tools to find optimal system designs. In particular, these properties are continuity and monotonicity. This paper presents an...

This paper presents a model predictive control (MPC) strategy based on genetic algorithm to solve the boiler–turbine control problem. First, a Takagi–Sugeno (TS) fuzzy model based on gap values is established to approximate the behavior of the boiler–turbine system, then a specially designed genetic algorithm (GA) is employed to solve the resulting constrained MPC problem. A terminal cost is ad...

Time-distributed optimization (TDO) is an approach for reducing the computational burden of model predictive control (MPC) and a generalization real-time iteration scheme. When using TDO, iterations are distributed over time by maintaining running solution estimate updating it at each sampling instant. In this article, TDO applied to input-constrained linear-quadratic MPC studied in detail, ana...

This study presents a new approach to modeling and control the current-fed Dickson voltage multiplier (CF-DVM). The capacitor relation input current are obtained. As all switching intervals considered in detail, highly accurate dynamic model is obtained, which can be easily extended for CF-DVM with an arbitrary number of stages. Using precise extracted model, Takagi-Sugeno fuzzy (TSFM) provided...

A novel accumulated error based event-triggered model predictive control (MPC) algorithm is put forward on the basis of dual-mode strategy and state-feedback for constrained continuous-time linear time-invariant (LTI) systems with constraints bounded disturbances. First, a new mechanism (ETM) constructed (AE) between optimal state trajectory real trajectory. Next, in order to stabilize system, ...

We reconstruct the shape of the primordial power spectrum from the latest cosmic microwave background data, including the new results from the Wilkinson Microwave Anisotropy Probe (WMAP), and large scale structure data from the two degree field galaxy redshift survey (2dFGRS). We tested four parameterizations taking into account the uncertainties in four cosmological parameters. First we parame...

Secure multiparty computation (MPC) as a service is becoming a tangible reality. In such a service, a population of clients wish to utilize a set of servers to delegate privately and reliably a given computation on their inputs. MPC protocols have a number of desired properties including tolerating active misbehavior by some of the servers and guaranteed output delivery. A fundamental result is...

A mobile robot needs to navigate in dynamic and unstructured environments. For this, motion planning algorithms must be able to handle complex, time-varying constraints presented by the environment, and quickly generate high quality trajectories that reaches the goal. Model Predictive Control (MPC) is a receding-horizon control algorithm which optimizes the performance of the constrained system...

We analyze a time-coarsening strategy for model predictive control (MPC) that we call diffusing-horizon MPC. This seeks to overcome the computational challenges associated with optimal problems span multiple timescales. The coarsening approach uses time discretization grid becomes exponentially more sparse as one moves forward in time. design is motivated by recently established property of kno...

In dealing with model predictive controllers (MPC), controller tuning is a key design step. Various tuning methods are proposed in the literature which can be categorized as heuristic, numerical and analytical methods. Among the available tuning methods, analytical approaches are more interesting and useful. This paper is based on a proposed analytical MPC tuning approach for plants can be appr...