Showing papers by "Ahmad Nor Kasruddin Nasir published in 2013"

Composite Fuzzy Logic Control Approach to a Flexible Joint Manipulator

Abstract: The raised complicatedness of the dynamics of a robot manipulator considering joint elasticity makes conventional model‐based control strategies complex and hard to synthesize. This paper presents investigations into the development of hybrid intelligent control schemes for the trajectory tracking and vibration control of a flexible joint manipulator. To study the effectiveness of the controllers, a collocated proportional‐derivative (PD)-type Fuzzy Logic Controller (FLC) is first developed for the tip angular position control of a flexible joint manipulator. This is then extended to incorporate a non‐collocated Fuzzy Logic Controller, a non‐collocated proportional‐integral‐derivative (PID)and an input-shaping scheme for the vibration reduction of the flexible joint system. The positive zero‐vibration‐derivative‐derivative (ZVDD) shaper is designed based on the properties of the system. The implementation result of the response of the flexible joint manipulator with the controllers are presented in time and frequency domains. The performances of the hybrid control schemes are examined in terms of input tracking capability, level of vibration reduction and time response specifications. Finally, a comparative assessment of the control techniques is presented and discussed.

A novel adaptive spiral dynamic algorithm for global optimization

Abstract: This paper presents a novel adaptive spiral dynamic algorithm for global optimization. Through a spiral model, spiral dynamic algorithm has a balanced exploration and exploitation strategy. Defining suitable value for the radius and displacement in its spiral model may lead the algorithm to converge with high speed. The dynamic step size produced by the model also allows the algorithm to avoid oscillation around the optimum point. However, for high dimension problems, the algorithm may easily get trapped into local optima. This is due to the incorporation of a constant radius and displacement in the model. In order to solve the problem, a novel adaptive formulation is proposed in this paper by varying the radius and displacement of the spiral model. The proposed algorithm is validated with various dimensions of unimodal and multimodal benchmark functions. Furthermore, it is applied to parameter optimization of an autoregressive with exogenous terms dynamic model of a flexible manipulator system. Comparison with the original spiral dynamic algorithm shows that the proposed algorithm has better accuracy. Moreover, the time domain and frequency domain responses of the flexible manipulator model shows that the proposed algorithm outperforms its predecessor algorithm.

Novel adaptive spiral dynamics algorithms for global optimization

Abstract: This paper presents adaptive versions of spiral dynamics algorithm (SDA) referred to as adaptive SDA (ASDA). SDA is known as fast computing algorithm due to its simplicity in the structure and it has stable convergence response when approaching the optimum point in the search space. However, the performance of SDA is still poor due to incorporation of single radius value during the whole search process. In ASDA, the spiral radius is made dynamic by employing novel mathematical equations and incorporating non-mathematical fuzzy logic strategy establishing the relationship between fitness value and spiral radius. This results in better performance in terms of convergence speed, accuracy, and total computing time while retaining the simple structure of SDA. Several uni-modal and multi-modal benchmark functions are employed to test the algorithm in finding the global optimum point. The results show that ASDA outperforms SDA in all test functions considered.

Novel hybrid bacterial foraging and spiral dynamics algorithms

Abstract: This paper presents three novel hybrid optimization algorithms based on bacterial foraging and spiral dynamics algorithms and their application to modelling of flexible maneuvering systems. Hybrid bacteria-chemotaxis spiral-dynamics algorithm is a combination of chemotaxis strategy in bacterial foraging algorithm and linear adaptive spiral dynamics algorithm. Chemotactic behaviour of bacteria is a good strategy for fast exploration if large value of step size is defined in the motion. However, this results in oscillation in the search process and bacteria cannot reach optimum fitness accuracy in the final solution. On the contrary, spiral dynamics provides good exploitation strategy due to its dynamic step size. However, it suffers from getting trapped at local optima due to poor exploration in the diversification phase. Employing the chemotaxis and spiral dynamics strategies at the initial and final stages respectively will thus balance the exploration and exploitation. Hybrid spiral-bacterial foraging algorithm and hybrid chemotaxis-spiral algorithm, on the other hand are developed based on adaptation of spiral dynamics model into chemotaxis phase of bacterial foraging with the aim to guide bacteria movement globally. The proposed algorithms are used to optimize parameters of a linear parametric model of a flexible robot manipulator system. The performances of the proposed hybrid algorithms are presented in comparison to their predecessor algorithms in terms of fitness accuracy, time-domain and frequency-domain responses of the models. The results show that the proposed algorithms achieve better performance.

A novel hybrid spiral-dynamics random- chemotaxis optimization algorithm with application to modelling of a flexible robot manipulator

Abstract: This paper presents a hybrid spiral-dynamics algorithm with random-chemotaxis; a synergy between chemotactic strategy of bacteria and spiral dynamics algorithm. Bacterial foraging algorithm has a good local search technique through random tumble and swim action. However it has slow convergence speed and high processing time. On the contrary, spiral dynamics algorithm is relatively simple and has very good global search technique based on its spiral model. This results in fast convergence speed and short computation time. However, the insufficient local search strategy may possibly trap the algorithm to local optima thus reduced accuracy. Incorporating bacteria chemotactic strategy into spiral dynamics algorithm can effectively solve the problems. Moreover, the strengths of both original algorithms can be preserved hence improving their performances. In this work, the proposed algorithm is used as an optimization tool to estimate parameters of high order dynamic model of a flexible manipulator system. The results show that the proposed algorithm has faster convergence speed and higher accuracy in comparison to its predecessor algorithms. On the other hand, time-domain and frequency-domain responses show that the algorithm can predict better model for the flexible manipulator system.

A dual phase modular fuzzy control structure for an auto-mode wheelchair in ascending and descending stairs

Abstract: Climbing a flight of stairs using a wheelchair is not as simple as balancing it on two wheels. Stairs climbing involves several phases, to ensure stability and safety in an auto-mode system. The subtle design of a stair climbing wheelchair system consists of three main actuators; a pair of motors, one for acting on each wheel and one for seat position. The operation of the system is based on the wheel rotation by lifting and bringing the other pair of wheels over one another on the staircases and vice versa. The challenge resides in an appropriate mechanical design and implementation of robust controller for the system to guarantee stability of the overall wheelchair while performing stair ascending and descending task automatically, with an assistant-free mode. A dwi-phase fuzzy logic control is introduced in this work and evaluated through intensive visual simulation to verify the proposed control approach.

Hybrid spiral dynamic bacterial chemotaxis optimisation for hybrid fuzzy logic control of a novel two wheeled robotic vehicle

Abstract: A hybrid spiral dynamic bacterial chemotaxis (HSDBC) optimisation algorithm is used to optimise the parameters of a fuzzy logic control system of a new structure of two - wheeled robotic vehicle. The vehicle has a novel structure based on double inverted pendulum with a movable payload and 5 degrees of freedom. HSDBC algorithm combines the strengths of bacterial foraging algorithm (BFA) and spiral dynamic algorithm (SDA) for a fast and accurate convergence to the optimum point. This paper demonstrates the efficiency of the HSDBC with fast convergence speed, better accuracy and stability compared to BFA optimisation.