In this paper a non-linear model based control (NMBC) scheme for the continuous stirred tank reactor (CSTR) process is proposed. The proposed control law is a function of the model state(s) and the measured output variable(s). Performance comparison of the proposed NMBC scheme with that of non-linear model predictive control scheme (NMPC) is reported. From the extensive simulation studies on the CSTR process, it can be inferred that servo and regulatory performances of the proposed NMBC scheme is found to closely match the NMPC scheme, with the proposed control scheme being computationally less intensive. In addition, a non-linear model based control scheme using an unscented Kalman filter (UKF-NMBC) and an inferential control scheme (INF-UKF-NMBC) for the CSTR process affected by random disturbances and random errors in measurements are formulated. Performance comparisons of the UKF-NMBC with UKF based NMPC and INF-AUKF-NMBC with INF-AUKF-NMPC, highlighting offset free servo performance, rejection of unmeasured disturbance, and robustness to model-plant mismatch are reported.

Cooperative control of networked unmanned aerial vehicles (UAVs) has received significant research interest over the last decade due to its potential applications in military security and surveillance, search and rescue, planetary exploration, precision agriculture, and so on. Many of these practical activities can be formulated as a group formation tracking problem with multiple targets to track. This paper aims to address such problems via designing a cooperative control scheme for networked tri‐rotor UAVs connected with directed graph topology. The proposed methodology consists of a two‐loop control scheme—the inner loop applies a robust feedback linearization technique to linearize the coupled, nonlinear dynamics of the tri‐rotor UAVs; while the outer loop facilitates an ARE‐based cooperative group formation tracking scheme. Tri‐rotor UAVs are considered in this paper instead of quad‐rotor UAVs, which are more common in drone applications, to conquer a major limitation of the quad‐rotor UAVs that it cannot alter its attitude independently while hovering at a particular height. A rigorous theoretical proof is given to establish the two‐loop control scheme exploiting the Lyapunov stability approach and algebraic Riccati equation (ARE)‐based optimal control policy. An in‐depth case study on a multitarget surveillance mission has been performed in this paper using a virtual reality software simulation platform to demonstrate the usefulness and efficacy of the proposed scheme.

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Summary This paper deals with the design and application of nonlinear model‐based control schemes for stable and nonlinear benchmark industrial processes. The primary control objective is to facilitate set‐point (constant/time‐varying) tracking in the presence of external disturbances, process noise, measurement noise, parametric uncertainty, and model mismatch. We first propose a “noninferential‐type” model‐based control scheme which involves a finite‐dimensional, nonlinear, and deterministic process model to generate the model states. Secondly, an “inferential‐type” model‐based control scheme has been introduced particularly to take into account the stochastic uncertainties such as process noise and measurement noise. The second scheme exploits the dual extended Kalman filter for estimating the immeasurable states and the process parameters through which disturbance is injected. Unlike fixed‐parameter controllers, the proposed schemes update the controller gains at each step depending on the real‐time process gains. In order to demonstrate the usefulness of the proposed closed‐loop tracking control schemes, two exhaustive case studies have been carried out on the CSTR and Van de Vusse reactor processes, which are considered to be benchmark industrial processes due to highly nonlinear and unpredictable behaviour and due to nonminimum phase property. Finally, the performance of the proposed schemes are compared with an EKF‐based adaptive PI control framework and the simulation results reveal that the transient performance of the proposed schemes are better than that of the aforementioned PI technique especially in perturbed condition (ie, in presence of model mismatch and measurement noise).

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