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

This paper is concerned with the design of non-fragile H∞ output feedback control of a high-performance aircraft similar to F-16 in executing a large bank-angle tracking manoeuvre. The non-fragile H∞ flight controllers are designed to tolerate multiplicative gain variations in the controller matrices. The designs are based on three linear trim models of the high-performance aircraft. From the linear and non-linear simulations, the results show that the resulting closed-loop systems using the non-fragile H∞ flight controllers are robustly stable and have H∞ disturbance attenuation bounds with respect to some admissible controller gain variations. Corresponding performance comparisons with the standard robust design approach show that the resulting closed-loop systems using the standard H∞ flight controllers are unstable under the same controller gain variations.

A poppet-type electropneumatic servovalve developed in this study utilizes a poppet directly operated by a moving-coil actuator in the metering stage and is controlled by a digital controller. This servovalve is insensitive to air contamination and has no problem of air leakage at null, but it has relatively large friction between the O-rings installed in the peripheral grooves of the balance pistons and the valve sleeve. For friction compensation control, a static friction model that enables simulation of the stick-slip phenomena and a dynamic model that captures the friction behaviour such as presliding displacement and varying break-away force are presented. The parameters for the friction models are identified by utilizing an evolution strategy, one of the evolutionary algorithms, which is a probabilistic global search algorithm based on the model of natural evolution. These friction models are then used in designing a non-linear friction compensation controller. It is found in the experiment that the electropneumatic servovalve has almost no hysteresis and that the friction compensation control significantly improves valve performance. The experimental results of the open loop test on poppet positioning agree well with simulation results of the valve model with identified friction parameters. It is also shown that the experimental results of friction compensation control using a static friction model show a small steady state error but those using a dynamic friction model show almost no such error.

In this paper, the stability of the reduced order minimal controller synthesis (ROMCS) algorithm is investigated by comparative robustness tests. Both, ROMCS control and the proportional plus integral (P +1) controller are implemented in the case of aluminium alloy specimens of 10 mm diameter. Three other different specimens are controlled under the same controller implementations. Hence, the unmodelled dynamics and disturbances are introduced as a natural consequence of these test conditions.

As the demand for robots to perform complex tasks grows, there is an increasing need to utilize robust and stable force control strategies. Most current schemes can only provide adequate force control with the controller tuned to specific task requirements since, if there is a wide variation in the overall compliance at the robot tool/task interface, the performance is rapidly degraded. This paper describes a method for the design of a fuzzy logic controller to replace a conventional controller in a force control loop. The method combines an analytical approach to controller tuning, with the intuitive properties and self-adjusting gain characteristics associated with fuzzy logic systems. It is demonstrated using a model of a single-axis experimental rig and is shown to perform well over a wide range of stiffnesses. The implementation of the controller on the actual rig is also described. Experimental results compare favourably with those obtained from simulation using an accurate model of the system. Issues relating to the implementation of the controller on multi-axis systems are also addressed.

The paper describes a new technique for data entry hardware. It is a high-level interpretation method based on the back-propagation neural network model, in which two aspects of research and development works are illustrated in detail: one is a tactile sensing surface comprising a deformable surface with optical sensing elements; the other is the data acquisition and processing model in which a neural network model, called Tactile Position Recognitron, is programmed to realize the real-time and precise recognition of a contact force position, which enables the contact position of a constant force to be determined within an accuracy less than 4 per cent of full scale in a continuous spatial resolution of 35 zones.

The device described utilizes a simple low-cost and robust mechanical design combined with software to interpret sensory data to measure the contact position of a normal force applied on a planar surface. The high-level interpretation method for this system enables automatic determination of contact position in real time.

A new concept referred to as progression-based prediction of remaining life (PPRL) is proposed in the present paper in order to solve the problem of accurately predicting the remaining bearing life. The basic concept behind PPRL is to apply different prediction methods to different bearing running stages. A new prediction procedure which predicts precisely the remaining bearing life is developed on the basis of variables characterizing the state of a deterioration mechanism which are determined from on-line measurements and the application of PPRL via a compound model of neural computation. The procedure consists of on-line modelling of the bearing running state via neural networks and logic rules and not only can solve the boundary problem of remaining life but also can automatically adapt to changes in environmental factors. In addition, multi-step prediction is possible. The proposed technique enhances the traditional prediction methods of remaining bearing life.

An evolutionary approach for modern robust control oriented system identification and model reduction in the frequency domain is proposed. The technique provides both an optimized nominal model and a ‘worst-case’ additive or multiplicative uncertainty bounding function which is compatible with robust control design methodologies. In addition, the evolutionary approach is applicable to both continuous- and discrete-time systems without the need for linear parametrization or a confined problem domain for deterministic convex optimization. The proposed method is validated against a laboratory multiple-input multiple-output (MIMO) test rig and benchmark problems, which show a higher fitting accuracy and provides a tighter L∞ error bound than existing methods in the literature do.