The efficient control of nonlinear processes is generally considered to be challenging. The development of digital computers promotes the study of nonlinear process control technology. Due to the discrete sampling of digital computer, it is necessary to develop the corresponding control algorithms for nonlinear processes. In this paper, a new equivalent control-based discrete-time sliding mode control is proposed for a class of nonlinear process with uncertainty and external disturbance. An adaptive law and a disturbance observer are designed to estimate the uncertainty and the disturbance, respectively. By combining with them, the new discrete-time sliding mode control is developed with good performance. The corresponding theoretical analysis is well verified by using Lyapunov function. Finally, the proposed approach is demonstrated by case studies in light of MATLAB.

This paper proposes a case study in the control of a heavy oil pyrolysis/cracking furnace with a newly extended U-Model based Pole Placement Controller (U-PPC). The major work of the paper includes 1) establishing a control oriented nonlinear dynamic model with Naphtha cracking and thermal dynamics, 2) analysing a U-model (i.e. control oriented prototype) representation of various popular process model sets, 3) designing the new U-PPC to enhance the control performance in pole placement and stabilisation, 4) taking computational bench tests to demonstrate the control system design and performance with a user-friendly step by step procedure.

This paper develops a distributed model predictive control strategy for the atmospheric and vacuum distillation tower, which constitutes a key process involved in refining petroleum. When considering an MPC implementation, it is known that computational complexity can be reduced if the system is first decomposed into multiple smaller dimensional subsystems. Optimally exploiting the modern computer networks available in industry, a distributed model predictive control implementation is developed for the atmospheric and vacuum tower system, which is assumed to be part of a wider petroleum refining process comprised of a number of sub-systems connected in series. For each subsystem, given the availability of mutual communication channels between subsystems and by using an iterative calculation approach, it will be seen that Nash optimality can be achieved. A low-cost solution that is readily implementable online is seen to achieve the control objective. The effectiveness of the approach presented in the paper is validated by the results of nonlinear simulation experiments. Keywords-Distributed model predictive control; atmospheric and vacuum distillation tower；subsystem.

The parallel structure is one of the basic system architectures found in process networks. In order to achieve robust control of complex process networks, it is necessary to formulate control strategies that specifically accommodate the characteristics of such parallel systems. In this paper, the competitive coupling and competitive constraints in parallel systems are initially defined. A novel robust distributed model predictive control algorithm is then developed for such parallel systems which deals explicitly with competitive couplings, competitive constraints and uncertainties. The Lyapunov Method is used for the theoretical analysis which produces tractable linear matrix inequalities (LMI). Two simulation studies and an experimental trial are provided to validate the effectiveness of the proposed approach. These consider control of 40 user and 100 user gas boiler heating systems as well as control of two continuous stirred tank reactors (CSTRs) which are connected in parallel.

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