The design of robust computer control systems for balancing and attitude control of double and triple inverted pendulums is considered in this paper. For the double inverted pendulum, a DC motor mounted at the upper hinge is used to balance and control attitude of the upper link. For the triple inverted pendulum a DC motor mounted at the middle hinge is used to control the middle link, whereas proportional position control applied to a motor at the upper hinge is utilised to maintain the upper link in alignment with the middle link. In both cases the lower hinge is left free to rotate.

This paper describes the control system for an eight-degree-of-freedom biped robot built at the University of Salford. The controller enables the robot to walk in the sagittal plane on smooth level terrain and is essentially an observer-based controller utilising state feedback, integral action and feedforward control. The robot posture is controlled by selecting constant reference set points for the control system. Locomotion is achieved by suitably modifying the reference set points. The robot walks with a step length of approximately 0.3 m and a speed of about 0.03 m/s.

This paper focuses on using the Bees Algorithm in both its basic and enhanced forms to tune the parameters of a fuzzy logic controller developed to stabilise and balance an under-actuated two-link acrobatic robot (ACROBOT) in the upright position. A linear quadratic regulator (LQR) was first developed to obtain the scaling gains needed to design the fuzzy logic controller. Simulation results confirmed that using the Bees Algorithm to optimise the membership functions and the scaling gains of the fuzzy system improved the controller performance.

In this work, we propose a Selective Fine-Tuning algorithm for Bayesian Networks (SFTBN). The aim is to enhance the accuracy of Bayesian network classifiers by finding better estimations for the probability terms used by the classifiers. The algorithm augments a Bayesian Network (BN) learning algorithm with a fine-tuning stage that aims to more accurately estimate the probability terms used by the BN.

Through the I*PROMS Network of Excellence which originated during the sixth Framework Programmeof the European Commission, this paper introduces a European vision of the essential research areas to deliver future innovations in manufacturing. Inparticular, these areas are identified as Advanced Production Machines, Production Automation and Control, Innovative Design Technologies and Production Organisation and Management.