In the last decades, model-free control (MFC) has become an alternative for complex processes whose models are not available or are difficult to obtain. Among the model-free control techniques, intelligent PID (iPID) algorithms, which are based on the ultralocal model parameterized with the constant α and including a classical PID, are used in many applications. This paper presents a new method for tuning iPID controllers based on the iterative feedback tuning (IFT) technique. This model-free tuning technique iteratively optimizes the parameters of a fixed structure controller using data coming from the closed-loop system operation. First, the discrete transfer functions of the iPID are deduced, considering the first and second order derivatives of the output variable from the ultralocal model. Using the discrete transfer functions, the iPID controllers become the fixed structure type, and the IFT parameter tuning method can be applied. Thus, in addition to the classical gains of the PID algorithm, the value of the parameter α is also obtained, which is usually determined by trial-and-error. The performances of the IFT-tuned iPID controllers were experimentally tested and validated in real-time using Quanser AERO 2 laboratory equipment with a one degree of freedom (1-DOF) configuration.

The heat treatment temperature must precisely meet the required profile to ensure the quality of the workpiece. The model of the heat treatment furnace is not precisely known because it is nonlinear and complex. Considering this aspect, model-free temperature control methods are proposed, which are not based on a model of the process, but only on the input and output data acquired from the control system. Since the controlled temperature must accurately track the reference formed by the ramp and soak signals in the presence of disturbances, model-free iPI controllers were chosen due to the included double-integral component that ensures the steady-state performance for ramp signals. For the tuning of the iPI controller parameters, the iterative feedback tuning method is proposed, and the performance is compared with that obtained using the sliding mode control iPI variant. The proposed iPI control laws are tested for ramp and soak scenarios, using temperature suitable to thermal treatment technological processes, and performance is analyzed using both process-specific indices and classical criteria.

Against the background of the development of control systems, Data Driven Control (DDC) methods are becoming more and more popular, given the system’s independence from physical models and the possibility of quickly tuning the controller. The usefulness of such tuning algorithms increases with the complexity of the plants. Nonlinear models are the main class of processes for which such laws are amenable. According to the literature, a class of DDC methods exist that perform online estimation of plant behavior with an unknown structure, which is generically called Model Free. This title is assumed by two types of algorithms, which contain it in the name. One is the gradient-based algorithm, Model Free Adaptive Control, defined by Hou, which uses the concept of dynamic linearization through pseudo partial derivatives (PPD) and pseudo gradient (PG). The other is a non-gradient based algorithm, Model Free Control, defined by Fliess and Join, which uses the concept of the ultralocal model and intelligent PID controllers (iPID). For the gradient-based methods, in the compact form of dynamic linearization (CFDL), i.e., partial form dynamic linearization (PFDL), two algorithms are proposed to determine the initial value of the time-varying parameters PPD and PG from the dynamic performance perspective as they offer the best responses. The CFDL and PFDL variants of the MFAC control law, which have parameters that result from the application of the proposed algorithms, are compared with iP and iPD controllers on nonlinear control systems.

Applications of systems engineering in the automotive field have grown over the past two decades. This paper aims at a Model-Free Control (MFC) strategy for this type of application. It is a control method in the Data Driven Control (DDC) category that is suitable for systems with complex dynamics, uncertainties or dynamics that are difficult to model or with an unavailable model. Considering these reasons, MFC is an appropriate control method applicable to the complex dynamics of automotive systems. This paper presents a comparative analysis of two types of MFC controllers: intelligent proportional (iP) and intelligent proportional derivative (iPD) for a mechatronic system application. The system takes into account the longitudinal speed control of the vehicle. First, some authors results on the design of iP and iPD controllers are reviewed. Then, the intelligent controllers are tested in simulation considering the requirement to maintain the desired vehicle speed under different traffic conditions such as urban and extra-urban driving cycles. The control performances of the iP and iPD controllers are compared using different performance indices, with a classical PID controller added to the comparison.