Proceedings of the Institution of Mechanical Engineers,Part I: Journal of Systems and Control Engineering

Many efforts have been made for model predictive control MPC to enlarge terminal region where the stability is guaranteed by including a proper terminal weighting. This paper investigates how to maximize the ellipsoidal terminal region by using a more general method to design MPC: calculating terminal region and terminal weighting separately. With Jordan canonical form, the new ellipsoid-based method can result in terminal regions which are infinite (in certain directions) even in the case of open-loop unstable systems. Some most popular methods are just special cases of this new method. Simulation results illustrate the effectiveness of the proposed algorithms.

Hydraulic systems play an important role in modern industry owing to the fact that hydraulic actuator systems have many advantages over other technologies with electric motors, high durability, and the ability to produce large force at high speeds. Therefore, the hydraulic actuator has a wide range of application fields such as hydraulic pressing machines, moulding technology, etc. where controlled forces or pressures with high accuracy and fast response are the most significant demands. Consequently, many hybrid actuator models have been developed for research on how to control forces or pressures with the best results. The current paper presents a new kind of hydraulic load simulator for conducting performance and stability tests for control forces of hydraulic hybrid systems. In the dynamic loading process, disturbance makes the control performance (such as stability, frequency response, loading sensitivity, etc.) decrease or turn bad. In order to improve the control performance of a loading system and to eliminate or reduce the disturbance, an online self-tuning fuzzy proportional—integral—derivative (PID) controller is designed. Experiments are carried out to evaluate the effectiveness of the proposed control method applied for hydraulic systems with varied external disturbance as in real working conditions.

The dynamics of rotor/magnetic bearing systems are speed dependent due to gyroscopic effects and other aerodynamic influences. This paper introduces a linear matrix inequality (LMI)-based design method that enables the speed dependence to be embedded within robust gain-scheduled controllers. By utilizing an augmented plant that closely resembles the physical system, it allows for the formulation of multiple design objectives in a unified framework. In particular, a control design for minimized rotor displacements or bearing transmitted forces is considered and the importance of achieving performance over the whole speed range is addressed. If speed dependence is ignored in the controller design, system instability may occur at particular rotational speeds. However, the LMI-based gain-scheduling approach is shown to maintain closed-loop stability over the full speed range. The results are demonstrated using a system model and through measurements taken from a flexible rotor/magnetic bearing facility.

This paper investigates a new modelling and control issue: the tracking control problem of underactuated dynamic systems by using a special example – a pendulum-driven cart-pole system. A dynamic model of a pendulum-driven cart-pole system is firstly presented. Then, a six-step motion strategy of the pendulum-driven cart-pole system is proposed. After that, a desired profile of the pendulum joint velocity is developed based on the proposed six-step motion strategy. The desired joint position and acceleration can be computed using the desired joint velocity profile. A closed-loop control approach is proposed using the partial feedback linearization technique. Finally, extensive simulation studies are conducted to demonstrate the proposed approaches.

There are numerous methods for solving the inverse kinematic equations for a robotic arm. This paper proposes a novel, adaptive approach based on multiagent systems (MASs). An MAS employs a distributed, decentralized approach to problem solving that is not commonly employed in conventional robotic arm control. The MAS uses patterns abstracted from various configurations of the robotic arm to provide a means of solving inverse kinematic equations where there is a changing kinematic model. Such an approach is beneficial in applications such as the maintenance of power transmission lines, welding, and providing support to handicapped people. The method is demonstrated using a case study utilizing a six-degree-of-freedom Kawasaki FS02 industrial robotic arm. The results from the case study demonstrate a solution for 95 per cent of all attainable Cartesian coordinates.

Looper—tension control among various control problems in hot rolling mills (HRMs) has significance, in particular, because it affects both operational stability of the process and dimensional quality of the products. The control target in this control loop is to maintain looper angle and strip tension simultaneously at their desired values. The most difficult challenge in the controller design arises from the interaction, between strip tension and looper angle, and uncertainty coming from disturbances and a model mismatch. Recently, there has been some research to investigate the potential benefits of model predictive control (MPC) for this control loop. However, most of them used constrained optimization based on nominal models, and this often caused constraint violations for the uncertain case. The purpose of this paper is to develop robust MPC algorithms for the hot rolling mill process, in the presence of disturbance uncertainty, in order to improve the constraint handling performance.