This study addresses two major single‐input single‐output control problems involving both feedback and feedforward actions: (i) the model matching in reference tracking and (ii) the rejection of measurable disturbances. Its aim is to overcome the limitations of inversion‐based feedforward design methods when system uncertainty is considered, and to find a control engineering solution based on the quantitative feedback theory (QFT). The proposed methodology leads to minimum cost of feedback by limiting the feedback action to the strictly necessary amount that enables the use of a feedforward controller. Although the model matching problem had drawn some attention of the QFT community in the last few years, the measurable disturbance rejection problem remained unaddressed. This study provides a novel solution for both of them in which the need for feedback is linked to the existence of a common feedforward solution for all plants within the model uncertainty. This work also deals with the generation of the corresponding quadratic inequalities and new QFT bounds for the mentioned feedback demand. A practical and well‐known benchmark example illustrates the main details and advantages of the new methodology.

This paper presents a robust feedback control solution for systems with multiple manipulated inputs and a single measurable output. A structure of parallel controllers achieves robust stability and robust disturbance rejection. Each controller uses the least possible amount of feedback at each frequency. The controller design is carried out in the Quantitative Feedback Theory framework. The method pursues a smart load sharing along the frequency spectrum, where each branch must either collaborate in the control task or be inhibited at each frequency. This reduces useless fatigue and saturation risk of actuators. Different examples illustrate the ability to deal with complex control problems that current MISO methodologies cannot solve. Main control challenges arise due to the uncertainty of plant and disturbance models and when a fast‐slow hierarchy of plants cannot be uniquely established.

Unmanned aerial vehicles (UAV) in the civil sector have recently evolved from being devices for recreational uses to becoming aircraft for professional uses. Professional applications of these devices require the aircraft to ship more and more complex sensors for reasons of safety in the event of failures. However, controlling all these systems is a challenge for flight controllers at the hardware and software levels. In this context, this work proposes a new aircraft real-time flight control system. The flight controller hardware is made up of two systems: a core board houses common sensor and flight devices; a flexible unit, isolated from body vibrations, houses redundant sensors to improve accuracy and reliability. The application functions are driven by the microcontroller running a new real-time operating system (RTOS) to better schedule works on limited computation power. To design a simplified RTOS for embedded systems, a hybrid scheduler (first-come-first-serve scheduling and earliest-deadline-first scheduling) with dynamic priority is proposed. The results obtained show the effectiveness of the system using schedulability tests for uni-processor systems. A set of real data supports the performance of the flight controller.

This study focuses on the modeling and control optimization of the pitch–roll angles orientation in a quad-rotor UAV (unmanned aerial vehicle) drone system. To address the control challenge, PID (proportional–integral–derivative) controllers are used, which have been tuned through a model-based optimization approach. A non-linear model of the system is obtained and further validated experimentally. The model is implemented in Simulink to conduct model-based optimization. The controller design uses an IMC (internal model control) model-based approach to establish the initial parameters for a PID controller. In the subsequent design phase, a multiobjective approach is taken, incorporating weighted cost functions that account for IAE (integral absolute error) and ITAE (integral of time-weighted absolute error) forms, along with battery usage. The outcomes of this design are showcased through simulations and real-world platform testing.

Autothermal Thermophilic Aerobic Digestion (ATAD technology) is a promising alternative to conventional digestion systems. Aeration is a key factor in the performance of these kinds of reactors, in relation to effluent quality and operating costs. At present, the realisation of automatic control in ATADs is in its infancy. Additionally, the lack of robust sensors also makes the control of these processes difficult: only redox potential and temperature sensors are reliable for operation in full-scale plants. Based as it is on the existing simulation protocols for benchmarking of control strategies for wastewater treatment plants (WWTP), this paper presents the definition and implementation of a similar protocol but specifically adapted to the needs of ATAD technology. The implemented simulation protocol has been used to validate two different control strategies for aeration (ST1 and ST2). In comparison to an open-loop operation for the ATAD, simulation results showed that the ST1 strategy was able to save aeration costs of around 2-4%. Unlike ST1, ST2 achieved maximum sludge stabilisation but at the expense of higher aeration costs.