This paper presents a MPC (Model Predictive Control) algorithm for MEMS vibratory gyroscopes based on force-balancing control strategy. In the proposed MPC method, using a set of orthonormal basis functions named Laguerre functions, a new prediction and optimization technique is designed. To enhance the capability of proposed MPC method for tracking time-varying reference trajectories, first a ...

This paper discusses neural multi-models based on Multi Layer Perceptron (MLP) networks and a computationally efficient nonlinear Model Predictive Control (MPC) algorithm which uses such models. Thanks to the nature of the model it calculates future predictions without using previous predictions. This means that, unlike the classical Nonlinear Auto Regressive with eXternal input (NARX) model, t...

This paper describes current work on framing the model predictive control (MPC) of cyber-physical systems as synthesis from signal temporal logic (STL) specifications. We provide a case study using a simplified power grid model with uncertain demand and generation; the model-predictive control problem here is that of the ancillary service power flow from the buildings. We show how various relev...

Two new contributions are presented here. This paper proposes using a Model Predictive Control (MPC) incorporating a Radial Basis Function (RBF) Network Observer for the fuel injection problem. Firstly a RBF Network is used as an observer for the volumetric efficiency of the air system. This allows for gradual adaptation of the observer, ensuring the control scheme is capable of maintaining goo...

With the advent of computer control, supervisory controllers such as simple cascade control, model predictive control (MPC), dynamic matrix control (DMC), etc are increasingly being used in process industries. In this study, performance of three such controllers namely simple cascade controller, ‘MPC cascaded to PID’ and ‘PID free MPC’ are compared on a continuous stirred tank heater (CSTH) sys...

This paper studies computational efficiency of suboptimal Model Predictive Control (MPC) with neural models. The algorithm requires solving on-line only a quadratic optimisation problem. Considering a nonlinear polymerisation process, for which the linear MPC algorithm is inadequate, it is shown that the suboptimal algorithm results in closedloop control performance similar to that obtained in ...

This paper focuses on the synthesis of computationally friendly sub-optimal nonlinear Model Predictive Control (MPC) algorithms with guaranteed robust stability. The input-to-state stability framework is employed to analyze the robustness of the resulting MPC closed-loop systems. Two new sub-optimal nonlinear MPC schemes are proposed, based on a contraction argument and an artificial Lyapunov f...

One of the main reasons for the development of space manipulators is to replace astronauts to perform tasks that involving long, repetitive operations and unhealthy, hazardous environment. Due to the particular harsh environment of space and the increasing demands of satellite maintenance, on-orbit refueling and assembly etc., the application of space robot has received significant attention. S...

Model predictive control (MPC) is a very popular controller design method in the process industry. Usually MPC uses linear discrete-time models. In this paper we extend MPC to a class of discrete-event systems with both hard and soft synchronization constraints. Typical examples of such systems are railway networks, subway networks, and other logistic operations. In general the MPC control desi...

Model predictive control (MPC) is a well-known control technique, which has been applied to complex and nonlinear processes. Fuzzy predictive control incorporates fuzzy goals and constraints in MPC, by combining predictive control with fuzzy decision making. In this paper, we propose the integration of weighted criteria in fuzzy predictive control, where the decision-maker can specify the prefe...