M.J. Nigam

New hybrid adaptive neuro-fuzzy algorithms for manipulator control with uncertainties- comparative study.

Control of an industrial robot includes nonlinearities, uncertainties and external perturbations that should be considered in the design of control laws. In this paper, some new hybrid adaptive neuro-fuzzy control algorithms (ANFIS) have been proposed for manipulator control with uncertainties. These hybrid controllers consist of adaptive neuro-fuzzy controllers and conventional controllers. The outputs of these controllers are applied to produce the final actuation signal based on current position and velocity errors. Numerical simulation using the dynamic model of six DOF puma robot arm with uncertainties shows the effectiveness of the approach in trajectory tracking problems. Performance indices of RMS error, maximum error are used for comparison. It is observed that the hybrid adaptive neuro-fuzzy controllers perform better than only conventional/adaptive controllers and in particular hybrid controller structure consisting of adaptive neuro-fuzzy controller and critically damped inverse dynamics controller.

Synergy of evolutionary algorithm and socio-political process for global optimization

This paper proposes a hybrid approach by combining the evolutionary optimization based genetic algorithm (GA) and socio-political process based colonial competitive algorithm (CCA). The performance of hybrid algorithm is illustrated using standard test functions in comparison to basic CCA method. Since the CCA method is newly developed, very little research work has been undertaken to deal with curse of dimensionality and to improve the convergence speed and accuracy of the basic CCA algorithm. The proposed CCA-GA algorithm is then used to tune a PID controller for a real time ball and beam system. Simulation results were reported and the hybrid algorithm indeed has established superiority over the basic algorithms with respect to set of functions considered and it can easily be extended for other global optimization problems.

Fuzzy PD+I control of a six DOF robot manipulator

Purpose – The purpose of this paper is to present the control of a six degrees of freedom (DOF) robot arm (PUMA robot) using fuzzy PD + I controller. Numerical simulation using the dynamic model of six DOF robot arm shows the effectiveness of the approach in trajectory tracking problems. Comparative evaluation with respect to PID and fuzzy PID controls are presented to validate the controller design. The results presented emphasize that a satisfactory tracking precision could be achieved using fuzzy PD + I controller combination than fuzzy PID controller.Design/methodology/approach – Control of a six DOF robot arm (PUMA Robot) using fuzzy PD + I controller.Findings – The performance of fuzzy PD + I controllers improves appreciably compared to their respective fuzzy PID only or conventional PID counterparts.Originality/value – Complexity of the proposed fuzzy PID controller is minimized as possible and only two design variables are used to adjust the rate of variations of the proportional gain and derivati...

Bacterial Foraging Optimized Hybrid Fuzzy Precompensated PD Control of Two Link Rigid-Flexible Manipulator

Light-weight flexible arms will most likely constitute the next generation robots due to their large payload carrying capacities at high speeds and less power demand. Control problem of robots with flexible members is more complex compared to rigid robots due to vibrations during the motion. This paper presents the social foraging behavior of Escherichia coli bacteria to optimize hybrid Fuzzy Precompensated Proportional — Derivative (PD) controller in trajectory control of two link rigid-flexible manipulator. Numerical simulation using the dynamic model of the two link rigid-flexible manipulator shows the effectiveness of the approach in trajectory tracking problems and the use of fuzzy precompensation has superior performance in terms of improvement in transient and steady state response, robustness to variations in loading conditions and ease to use in practice. Comparative evaluation with respect to genetic algorithm based optimization is presented to validate the controller design. The results present...

Hybrid bacterial foraging and particle swarm optimisation for fuzzy precompensated control of flexible manipulator

This paper presents hybrid approach combining the social foraging behaviour of Escherichia coli bacteria and particle swarm optimisation for optimising hybrid fuzzy precompensated proportional-derivative (PD) controller in trajectory control of two-link rigid-flexible manipulator. Numerical simulation using the dynamic model of the two-link rigid-flexible manipulator shows the effectiveness of the approach in trajectory tracking problems. The use of fuzzy precompensation has superior performance in terms of improvement in transient and steady state response, robustness to variations in loading conditions and ease to use in practice. Comparative evaluation with respect to genetic algorithm, particle swarm and bacterial foraging-based optimisation is presented to validate the controller design. The proposed algorithm performs local search through the chemotactic movement operation of bacterial foraging whereas the global search over the entire search space is accomplished by a particle swarm operator and so satisfactory tracking precision could be achieved using the approach.

A hybrid genetically-bacterial foraging algorithm converged by particle swarm optimisation for global optimisation

The social foraging behaviour of Escherichia coli bacteria and the effectiveness of genetic operators have recently been combined to develop a hybridised algorithm for distributed optimisation and control. The classical algorithms have their importance in solving real-world optimisation problems. Hybridisation of two algorithms is gaining popularity among researchers to explore the area of optimisation. This paper proposes a novel algorithm which hybridises the best features of three basic algorithms, i.e., genetic algorithm (GA), bacterial foraging (BF) and particle swarm optimisation (PSO) as genetically bacterial swarm optimisation (GBSO). The hybridisation is carried out in two phases; first, the diversity in searching the optimal solution is increased using selection, crossover and mutation operators. Secondly, the search direction vector is optimised using PSO to enhance the convergence rate of the fitness function in achieving the optimality. The proposed algorithm is tested on a set of functions which are then compared with the basic algorithms. Simulation results were reported and the proposed algorithm indeed has established superiority over the basic algorithms with respect to the set of functions considered and it can easily be extended for other global optimisation problems.

Genetically‐bacterial swarm optimization: Fuzzy pre‐compensated PD control of two‐link rigid‐flexible manipulator

Purpose – The purpose of this paper is to propose a novel algorithm which hybridizes the best features of three basic algorithms, i.e. genetic algorithm, bacterial foraging, and particle swarm optimization (PSO) as genetically bacterial swarm optimization (GBSO). The implementation of GBSO is illustrated by designing the fuzzy pre‐compensated PD (FPPD) control for two‐link rigid‐flexible manipulator.Design/methodology/approach – The hybridization is carried out in two phases; first, the diversity in searching the optimal solution is increased using selection, crossover, and mutation operators. Second, the search direction vector is optimized using PSO to enhance the convergence rate of the fitness function in achieving the optimality. The FPPD controller design objective was to tune the PD controller constants, normalization, and denormalization factors for both the joints so that integral square error, overshoots, and undershoots are minimized.Findings – The proposed algorithm is tested on a set of mathe...

Genetic Fuzzy Based Tracking Control of 3 DOF Robot Arm

The essential problem in controlling robots is to make the manipulator follow a desired trajectory. This paper presents the genetic algorithm tuned fuzzy PID controller (GAFPID) to follow the desired trajectory for a three degree of freedom (DOF) robot arm. Numerical simulation using the dynamic model of three DOF robot arm shows the effectiveness of the approach in trajectory tracking problems. Comparative evaluation with respect to PD, PID and fuzzy PID controls are presented to validate the controller design. The results presented emphasize that a satisfactory tracking precision could be achieved using the proposed controller than conventional controller.

Particle swarm optimized hybrid fuzzy precompensated trajectory control of rigid-flexible manipulator

Light-weight flexible arms will most likely constitute the next generation robots due to their large payload carrying capacities at high speeds and less power demand. Control problem of robots with flexible members is more complex compared to rigid robots due to vibrations during the motion. This paper presents the optimization technique inspired by social behavior of bird flocking to optimize hybrid Fuzzy Precompensated Proportional - Derivative (PD) controller in trajectory control of two link rigid-flexible manipulator. Numerical simulation using the dynamic model of two link rigid-flexible manipulator shows the effectiveness of the approach in trajectory tracking problems and the use of fuzzy precompensation has superior performance in terms of improvement in transient and steady state response, robustness to variations in loading conditions and ease to use in practice. Comparative evaluation with respect to genetic algorithm based optimization is presented to validate the controller design. The results presented emphasize that a satisfactory tracking precision could be achieved using hybrid Fuzzy Precompensated Proportional - Derivative controller with particle swarm optimization.