A function-based fuzzy PID (F-FPID) controller

This paper describes the concept of a function-based fuzzy proportional-integral-derivative (F-FPID) controller. An important feature of the proposed controller is that its structure comprises only four fuzzy rules each with two antecedents and two consequents. It performs proportional-derivative (PD) and integral (I) control actions using fuzzy elements rather than analytically using delay loops as is the case in input-based FPID (I-FPID). To assess its viability, the developed controller was used to control a bench-scale pH process. The experimental results demonstrated satisfactory performance of the proposed controller in maintaining the pH value at prescribed levels

Optimisation of swing-up control parameters for a robot gymnast using the Bees Algorithm

This paper focuses on using the Bees Algorithm to optimise the parameters of a swing-up control for a robot gymnast (Robogymnast) attached to a freely rotating high bar mounted on ball bearings. Robogymnast, which mimics the basic movements of a human acrobat swinging on a high bar, consists of three links and three joints. Its motion is manipulated by two DC motors mounted at the shoulders and hip joints. The freely rotating high bar represents the third joint to which link 1 (hands and arms as a single rigid part) is firmly attached. Although, this triple pendulum-like structure is difficult to balance at upright posture, its unpowered joint is advantageous during the swing-up phase. The ultimate challenge was to smoothly swing up Robogymnast from the downward (stable) position to the upright (unstable) configuration by finding optimum values of the parameters that regulate the amplitudes and frequencies of the sinusoidal signals applied to the two DC motors. The Bees Algorithm, a novel swarm-based optimization technique, was used as to achieve this. The simulation and experimental results showed successful swing-up of Robogymnast

Recent advances on key technologies for innovative manufacturing

Through the I*PROMS Network of Excellence which originated during the sixth Framework Programme of the European Commission, this paper introduces a European vision of the essential research areas to deliver future innovations in manufacturing. In particular, these areas are identified as Advanced Production Machines, Production Automation and Control, Innovative Design Technologies and Production Organisation and Management. Then, special attention is given to the main findings from the authors’ research programme since the start of I*PROMS in October 2004 for a number of technologies belonging to these four generic research streams

Biped robot locomotion in the sagittal I plane

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

Optimisation of swing-up control parameters for a robot gymnast using the Bees Algorithm

This paper focuses on using the Bees Algorithm for optimising the parameters of the swing-up control for a robot gymnast (Robogymnast) attached to a freely rotating high bar mounted on ball bearings. Robogymnast, which mimics a human acrobat, consists of three links and three joints. Its motion is manipulated by two DC motors mounted at the shoulders and hip joints. The freely rotating high bar represents the third joint to which link 1 (hands and arms as a single rigid part) is firmly attached. Although, this triple pendulum-like structure is difficult to balance at upright posture, its unpowered joint is advantageous during the swing-up phase. The ultimate challenge was to smoothly swing up Robogymnast from the downward (stable) position to the upright (unstable) configuration by finding optimum values of the parameters that regulate the amplitudes and frequencies of the sinusoidal signals applied to the two DC motors. The Bees Algorithm was used as an optimization technique to achieve this. From the randomly obtained set of parameter values, three were selected to simulate the behavior of Robogymnast during the swing-up phase. The results showed successful swing-up of Robogymnast

Inductive fuzzy neural network for multi-input multi-output dynamic systems modelling

This paper presents a systematic inductive fuzzy neural network for multi-input multi-output dynamic systems modeling of a 6-DOF PUMA560® industrial robot arm based on input/output measurements. An inductive learning algorithm is applied to generate the required fuzzy modelling rules from input/output numerical measurements recorded from the dynamic system. Then, a full differentiable fuzzy neural network is developed to construct the dynamic model of the multi-input multi-output system, while back-propagation algorithm or similar techniques can be further applied to tune the network parameters due to the differentiable nature of the developed network

Fuzzy and neuro-fuzzy based co-operative mobile robots

This paper focuses on the development of intelligent multi-agent robot teams that are capable of acting autonomously and of collaborating in a dynamic environment to achieve team objectives. A biologically-inspired collective behaviour for a team of co-operating robots is proposed. A modification of the subsumption architecture is proposed for implementing the control of the individual robots. The paper also proposes a fuzzy logic technique to enable the resolution of conflicts between contradictory behaviours within each robot. Furthermore, the paper proposes a neuro-fuzzy based adaptive action selection architecture that enables team of robot agents to achieve adaptive cooperative control to perform two proof-of-concept co-operative tasks: dynamic target tracking and box-pushing. Simulated and real experiments have been conducted to validate the proposed techniques