Predictive functional control (PFC) is a popular alternative to PID because it exploits model information better and enables systematic constraint handling while also being cheap and computationally efficient. A recent overview paper reviewed some recent proposals for improving the tuning efficacy. This paper extends and develops upon that review paper by introducing some exciting new proposals for how to making tuning more intuitive and, thus, easier for unskilled operators. Moreover, there are early indications that these proposals are easily modified for use in nonlinear cases while maintaining a very low cost and a simple and fast online computation.

Predictive functional control (PFC) is a fast and effective controller that is widely used in preference to PID for single-input single-output processes. Nevertheless, the core advantages of simplicity and low cost come alongside weaknesses in tuning efficacy. This paper summarises and consolidates the work of the past decade, which has focused on proposing more effective tuning approaches while retaining the core attributes of simplicity and low cost. The paper finishes with conclusions on the more effective approaches and links to context.

Predictive functional control (PFC) has emerged as a popular industrial choice owing to its simplicity and cost-effectiveness. Nevertheless, its efficacy diminishes when dealing with challenging dynamics because of prediction mismatch in such scenarios. This paper presents a proposal for reducing prediction mismatch and thus improving behaviour for simple unstable processes; a two-stage design methodology pre-stabilises predictions via proportional compensation before introducing the PFC component. It is demonstrated that pre-stabilisation reduces the dependency of the closed-loop pole on the coincidence point and also improves robustness to uncertainty. Simulation results verify the improved performance as compared to conventional PFC.

Temperature is an important control variable in industrial processes. In this paper, an adaptive PID control algorithm has been discussed to track the process temperature. The presented control algorithm employs Lyapunov function based artificial neural networks for online tuning of proportional, integral and derivative actions. This algorithm has been successfully tested on the laboratory temperature control process trainer. For comparative analysis, the results have been contrasted with the conventional PID scheme. The experimental findings show that improved and stable tracking is achieved with the proposed adaptive PID controller.

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