In this paper, a networked control-system laboratory for the remote control of processes is presented. The laboratory allows the students to develop network-based control systems with the use of an architecture consisting of I/O devices, communication modules, and server-client applications implemented with supervisory control and data acquisition environment facilities, and to operate on real pilot plants through an intranet and the Internet. Three examples were presented, demonstrating the potentiality of the laboratory to remotely control the processes and to develop new network-based control-system structures.

The visual servoing architectures with an eye-in-hand configuration, composed of a 6-d.o.f. manipulator robot and a video camera, are categorised as high-coupled and nonlinear systems. For this reason, in order to have a stable and convergent behaviour, advanced techniques like predictive control are needed. These types of techniques can solve the potential problems that can appear in classical (image-based visual servoing) while different type of constraints for visual servoing system can be fulfilled easier. Three different visual predictive control architectures based on image moments are proposed. The model for the image moments predictor used in the first architecture is an extension of a local model point features predictor. The second predictive approach uses an image moment-based predictor to describe the future behaviour of the system over a certain camera velocity scenario directly in the image moments space. In order to have a faster convergence and in the same time a desired behaviour of the servoing system, a reference trajectory is employed. This represents the main contribution of the last predictive control technique proposed in this study. A Matlab simulator was developed and all the visual servoing architectures described in the study were implemented. The simulation results showed that all the proposed visual predictive control approaches are stable and convergent when dealing with visual servoing tasks, but the best performances were assigned to the reference trajectory-based image moments architecture.

Multi-plate wet clutches and theirs actuators are an important part of the automatic transmission gearboxes. Modern electronic controls operate the clutch by an electro-hydraulic actuator. In this paper a new model for a wet clutch actuated by an electro-hydraulic valve used in a real vehicle for automatic transmission is developed. The model captures the essential dynamics of the electro-hydraulic valve and clutch circuit, which operates on a given pressure range commanded by a transmission shifting signal. The model is validated against the data obtained from a test bench which includes the DQ250 wet clutch actuated by the electro-hydraulic valve DQ500 and the comparison shows good agreement. Based on the developed model, a useful clutch prediction control scheme was designed. Simulation results obtained with predictive control strategy are presented and discussed.

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.

In networked control systems there are many network-induced imperfections and constraints: quantisation errors, packet dropouts, variable sampling periods, time-varying delays and communication constraints. The goal of this study is to provide a control design methodology that can assure the closed-loop performances of a network-controlled application, while compensating the time-varying delays introduced by the communication network. To this end, firstly, the error caused by the time-varying delays is modelled as a disturbance and a novel method of bounding the disturbances is proposed. Secondly, an one-step ahead predictive controller based on flexible control Lyapunov functions is designed, which explicitly takes into account the bounds of the disturbances caused by time-varying delays and guarantees also the input-to-state stability of the system. The methodology is then applied for a network-controlled direct current (DC) motor and the TrueTime simulations illustrate the effectiveness and robustness of the proposed delay modelling and control strategy.