Model predictive control (MPC) is a multivariable control algorithm commonly used when tracking a reference trajectory is the primary goal. It minimizes the steady-state tracking error, increases the closed-loop bandwidth, and enables the controller to track a reference signal, the major requirements for nanopositioning applications. These capabilities motivated this research on improving the positioning performance of the piezoelectric tube scanner (PTS) in an atomic force microscope (AFM) to achieve better imaging at high scanning speed. This paper surveys the effectiveness of an MPC controller for AFM imaging by considering its different features and comparing its results with those obtained from the in-built proportional integral (PI) controller.
We consider the optimal flows of intra-day gas supply for the production and pipeline network for Adelaide, Melbourne and Sydney. We model the demand in the network of three major cities as a system of connected three state Markov chains subject to shortfall, pipeline capacity and production capacity constraints. A comparison with a static optimisation model is provided to show the improved capacity for the analysis of demand satisfaction in this model.
A variable-gain model-independent control law is proposed to solve the problem of rendezvous to a leader for a directed network of Euler-Lagrange agents. A sufficiency condition for stability is developed, requiring centralised design of the two control gains. Compared to existing results which use constant-gain model-independent controllers for directed networks, our work has two key differences. Firstly, the damping term may begin at zero, and increases only if rendezvous has not been achieved. Constant-gain controllers use conservative gain values which negatively impact convergence speed. Secondly, we introduce a novel method of analysing the Lyapunov derivative, which provides useful and unique insight into analysis of variable-gain controllers for multi-agent systems. Simulations are provided to show the effectiveness of the controller.
A Network control system (NCS) is a control system in which communication between subsystems takes place over a digital network. Numerous results exist in the literature on modelling, analysis and design of NCSs in the presence of specific communication constraints such as packet dropouts, delays, data rates, quantization, etc. However, when analysing NCSs implemented on real physical networks, the existing results are based on restrictive assumptions. We consider NCSs over WirelessHART, the first international standard for industrial process control. With the goal of closing the gap between theory and practice, we propose for the first time a hybrid control-oriented model of WirelessHART NCSs under source routing configuration. Moreover, asymptotic and exponential stability results are presented under reasonable conditions.
In this paper, a self-tuning linear quadratic supervisory regulator using a large-signal state estimator for a diesel driven generator set is proposed. The regulator improves operational efficiency, in comparison to current implementations, by (i) automating the initial tuning process and (ii) enabling automated retuning capabilities. Utilizing a first principles-based nonlinear model detailed in [1], the procedure is demonstrated through simulations after real system measurements have been used for parameter identification. The regulator is able to suppress load-induced variations successfully throughout the operating range of the diesel generator.
