In this paper, two different backstepping neural network (NN) control approaches are presented for a class of affine nonlinear systems in the strict-feedback form with unknown nonlinearities. By a special design scheme, the controller singularity problem is avoided perfectly in both approaches. Furthermore, the closed loop signals are guaranteed to be semiglobally uniformly ultimately bounded and the outputs of the system are proved to converge to a small neighborhood of the desired trajectory. The control performances of the closed-loop systems can be shaped as desired by suitably choosing the design parameters. Simulation results obtained demonstrate the effectiveness of the approaches proposed. The differences observed between the inputs of the two controllers are analyzed briefly.

Robust and adaptive backstepping control for nonlinear systems using RBF neural networks

Adaptive position control of electrohydraulic servo systems using ANN

Position control of nonlinear hydraulic system using an improved PSO based PID controller

Electro-hydraulic servo-systems are widely employed in industrial applications such as robotic manipulators, active suspensions, precision machine tools and aerospace systems. They provide many advantages over electric motors, including high force to weight ratio, fast response time and compact size. However, precise control of electro-hydraulic systems, due to their inherent nonlinear characteristics, cannot be easily obtained with conventional linear controllers. Most flow control valves can also exhibit some hard nonlinearities such as dead-zone due to valve spool overlap. This work describes the development of an adaptive fuzzy sliding mode controller for an electro-hydraulic system with unknown dead-zone. The boundedness and convergence properties of the closed-loop signals are proven using Lyapunov stability theory and Barbalat's lemma. Numerical results are presented in order to demonstrate the control system performance.

Sliding Mode Control with Adaptive Fuzzy Dead-Zone Compensation of an Electro-hydraulic Servo-System

A practical method for friction identification in hydraulic actuators

Actual trends, such as highly volatile markets and shorter product lifecycles, in the process industries require new approaches for the control of process plants. These changes are tackled by the approach of modular process plants. Modular process plants promise a shorter time-to-market and a high flexibility regarding the products produced herein. To achieve these advantages, beside the mechanical modularization also the control systems of such plants need to change. Current process control systems are not able to fulfill all requirements of modularization. Therefore, known approaches from information technology are adapted for the automation of modular process plants. Beside service-oriented architectures, the Microservice approach is of current interest in the information technology domain. This approach is presented in this contribution, and the suitability of Microservices as an architecture for modular process automation is analyzed. Furthermore, a Microservice-based approach for the control of modular process plants is presented.

A microservice-based architecture approach for the automation of modular process plants

The adaptive backstepping algorithm is a well-known scheme for the design of nonlinear adaptive controllers. The two main drawbacks associated with this algorithm are that the nonlinear system must be linearly parameterised in the unknown or uncertain parameters and that the nonlinear functions must be exactly known. To avoid these problems, an extension of the backstepping algorithm with a specific type of artificial neural networks (ANN) called radial basis function networks (RBF), is proposed. This extension leads to a new control scheme; namely artificial neural network nonlinear adaptive control (ANNNAC). To further clarify the approach, a simple example is studied and the simulation results demonstrate clearly the power of this extension.

ANNNAC-extension of adaptive backstepping algorithm with artificial neural networks

Most Cyber Physical Production Systems (CPPS) research is devoted to the manufacturing industry, but the need for flexibility is an increasing trend in the process industry, too. Today's process automation systems (PAS) are not well-suited for quick adaptation of the plant, i.e. the addition, removal or change of physical components, such as filters, mixers, or reaction units. The authors have developed a model-based approach, which allows reconfiguring the PAS based on a model, which acts as a representative of the physical component. Thus, the model and the physical component together form a CPPS. In the paper, the requirements, the model structure, and the model content will be described, and a practical example will be presented.

Torsten Knohl

Papers

Adaptive position control of electrohydraulic servo systems using ANN

A microservice-based architecture approach for the automation of modular process plants

ANNNAC-extension of adaptive backstepping algorithm with artificial neural networks

Model-based engineering of CPPS in the process industries

State-based control of process services within modular process plants