Receding horizon filtering for a class of discrete time-varying nonlinear systems with multiple missing measurements

This paper is concerned with the receding horizon filtering problem for a class of discrete time-varying nonlinear systems with multiple missing measurements. The phenomenon of missing measurements occurs in a random way and the missing probability is governed by a set of stochastic variables obeying the given Bernoulli distribution. By exploiting the projection theory combined with stochastic analysis techniques, a Kalman-type receding horizon filter is put forward to facilitate the online applications. Furthermore, by utilizing the conditional expectation, a novel estimation scheme of state covariance matrices is proposed to guarantee the implementation of the filtering algorithm. Finally, a simulation example is provided to illustrate the effectiveness of the established filtering scheme.This work was supported in part by the Deanship of Scientific Research (DSR) at King Abdulaziz University in Saudi Arabia [grant number 16-135-35-HiCi], the National Natural Science Foundation of China [grant number 61329301], [grant number 61203139], [grant number 61134009], and [grant number 61104125], Royal Society of the U.K., the Shanghai Rising-Star Program of China [grant number 13QA1400100], the Shu Guang project of Shanghai Municipal Education Commission and Shanghai Education Development Foundation [grant number 13SG34], the Fundamental Research Funds for the Central Universities, DHU Distinguished Young Professor Program, and the Alexander von Humboldt Foundation of Germany

On general systems with network-enhanced complexities

In recent years, the study of networked control systems (NCSs) has gradually become an active research area due to the advantages of using networked media in many aspects such as the ease of maintenance and installation, the large flexibility and the low cost. It is well known that the devices in networks are mutually connected via communication cables that are of limited capacity. Therefore, some network-induced phenomena have inevitably emerged in the areas of signal processing and control engineering. These phenomena include, but are not limited to, network-induced communication delays, missing data, signal quantization, saturations, and channel fading. It is of great importance to understand how these phenomena influence the closed-loop stability and performance properties

Path design and receding horizon control for collision avoidance system of cars

The paper deals with path design and control realization problems of collision avoidance systems (CAS) of cars (ground vehicles). CAS emergency path design is based on the principle of elastic band with improved reaction forces for road borders and static obstacles allowing quick computation of the force equilibrium. The CAS path (reference signal) is smoothed and realized using receding horizon control (RHC). The car can be modelled by full (non-affine) or simplified (input affine) nonlinear models. The nonlinear predictive control problem is solved by using time varying linearization along appropriately chosen nominal control and state sequences, and analytical solution of the minimization of a quadratic criterion satisfying end-constraint. Differential geometric approach (DGA), known from control literature for the input affine nonlinear model, has been used for control initialization in the first horizon. For state estimation Kalman filters and measurements of two antenna GPS and Inertial Navigation System (INS) are used. A stand-alone software has been been developed using the C Compiler of MATLAB R2006a satisfying real time expectations

On control of discrete-time state-dependent jump linear systems with probabilistic constraints: A receding horizon approach

In this article, we consider a receding horizon control of discrete-time state-dependent jump linear systems, particular kind of stochastic switching systems, subject to possibly unbounded random disturbances and probabilistic state constraints. Due to a nature of the dynamical system and the constraints, we consider a one-step receding horizon. Using inverse cumulative distribution function, we convert the probabilistic state constraints to deterministic constraints, and obtain a tractable deterministic receding horizon control problem. We consider the receding control law to have a linear state-feedback and an admissible offset term. We ensure mean square boundedness of the state variable via solving linear matrix inequalities off-line, and solve the receding horizon control problem on-line with control offset terms. We illustrate the overall approach applied on a macroeconomic system