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The robot offers interesting capabilities, but suffers from a lack of stiffness. The proposed solution is to introduce redundancies for the overall improvement of different capabilities. The management of redundancy associated with the definition of a set of kinematic, mechanical and stiffness criteria enables path planning to be optimized.

The resolution method is based on the projection onto the kernel of the Jacobian matrix of the gradient of an objective function constructed by aggregating kinematic, mechanical and stiffness weighted criteria. Optimized redundancy management is applied to the 11-DoF (degrees of freedom) cells to provide an efficient placement of turntable and track. The final part presents the improvement of the various criteria applied to both 9-DoF and 11-DoF robotic cells.

The first application concerns the optimized placement of a turntable and a linear track using 11-DoF architecture. Improved criteria for two 9-DoF robotic cells, a robot with parallelogram closed loop and a Tricept are also presented. Simulation results present the contributions of redundancies and the leading role of the track.

The redundancy-based optimization presented and the associated simulation approach must be completed by the experimental determination of the optimization criteria to take into account each machining strategy.

This work in robotics machining relates to milling operations for automotive and aerospace equipment. The study is carried out within the framework of the RobotEx Equipment of Excellence programme.

The resolution method to optimized path planning is applied to 9- and 11-DoF robotic cells, including a hybrid robot with a parallelogram closed loop and a Tricept PKM.

This paper aims to propose a reliable local search algorithm having steepest descent pivot rule for computationally expensive optimization problems. In particular, an application to the design of Permanent Magnet Synchronous Motor (PMSM) drives is shown.

A surrogate assisted Hooke‐Jeeves algorithm (SAHJA) is proposed. The SAHJA is a local search algorithm with the structure of the Hooke‐Jeeves algorithm, which employs a local surrogate model dynamically constructed during the exploratory move at each step of the optimization process.

Several numerical experiments have been designed. These experiments are carried out both on the simulation model (off‐line) and at the actual plant (on‐line). Moreover, the off‐line experiments have been considered in non‐noisy and noisy cases. The numerical results show that use of the SAHJA leads to a saving in terms of computational cost without requiring any extra hardware components.

The surrogate approach in the design of electric drives is novel. In addition, implementation of the proposed surrogate model allows the algorithm not only to reduce computational cost but also to filter noise caused by the sensors and measurement devices.

Recent research has considered robotic machining as a dextrous alternative to traditional CNC machine tools for complex sculptured surfaces. One challenge in using robotic machining is that the stiffness is lower than traditional machine tools, due to the cantilever design of the links and low‐torsional stiffness of the actuators. This paper seeks to examine this limitation, using optimization algorithms to determine the best trajectories for the manipulators such that the stiffness is maximized.

The issue of low stiffness is addressed with an integrated off‐line planner and real‐time re‐planner. The available manipulator stiffness is maximized during off‐line planning through a trajectory resolution method that exploits the nullspace of the robot machining system. In response to unmodeled disturbances, a real‐time trajectory re‐planner utilizes a time‐scaling method to reduce the tool speed, thereby reducing the demand on the actuator torques, increasing the robot's dynamic stiffness capabilities. During real‐time re‐planning, priorities are assigned to conflicting performance criteria such as stiffness, collision avoidance, and joint limits.

The algorithms developed were able to generate trajectories with stiffer configurations, which resulted in a reduction in the actuator torques. The real‐time re‐planner successfully allowed the process plan to continue when disturbances were encountered.

Simulations are presented to demonstrate the effectiveness of the approach.

Addressing the limitation of stiffness in serial‐link manipulators will enable robots to become more suitable for machining tasks. The real‐time re‐planning approach will allow robots to become more autonomous during the execution of a given task.

An integrated off‐line and real‐time planning approach has been applied to robotic machining.

The purpose of this paper is to propose a method of optimal control of current commutation of switched reluctance motor drive.

The problem of optimal current commutation control is solved by off‐line selection of switching‐on and switching‐off angles. Selection of optimal values of angles is provided on computer model of the drive with help of particle swarm optimisation method. The optimal angle values are detected as functions of phase current and rotor speed. These calculated optimal values are stored in microcomputer control system memory in form of two‐input look‐up tables. The results are validated on laboratory set up.

Three different criteria of optimal control, which are taken into account: the maximum electromagnetic torque for given reference current, the maximum ratio of electromagnetic torque to root mean square value of phase current and the minimum electromagnetic torque ripples, gave a good results validated by simulation and experimental investigations.

A simple control method is proposed to optimise switched reluctance motors drive behaviour. Such an approach can be recommended for practical implementations.

The off‐line optimisation of switching angles, which is realised on computer model, is sufficient to obtain a good control effect.

The purpose of this paper is to address a fractional order fuzzy PID (FOFPID) control approach for solving the problem of enhancing high precision tracking performance and robustness against to different reference trajectories of a 6-DOF Stewart Platform (SP) in joint space.

For the optimal design of the proposed control approach, tuning of the controller parameters including membership functions and input-output scaling factors along with the fractional order rate of error and fractional order integral of control signal is tuned with off-line by using particle swarm optimization (PSO) algorithm. For achieving this off-line optimization in the simulation environment, very accurate dynamic model of SP which has more complicated dynamical characteristics is required. Therefore, the coupling dynamic model of multi-rigid-body system is developed by Lagrange-Euler approach. For completeness, the mathematical model of the actuators is established and integrated with the dynamic model of SP mechanical system to state electromechanical coupling dynamic model. To study the validness of the proposed FOFPID controller, using this accurate dynamic model of the SP, other published control approaches such as the PID control, FOPID control and fuzzy PID control are also optimized with PSO in simulation environment. To compare trajectory tracking performance and effectiveness of the tuned controllers, the real time validation trajectory tracking experiments are conducted using the experimental setup of the SP by applying the optimum parameters of the controllers. The credibility of the results obtained with the controllers tuned in simulation environment is examined using statistical analysis.

The experimental results clearly demonstrate that the proposed optimal FOFPID controller can improve the control performance and reduce reference trajectory tracking errors of the SP. Also, the proposed PSO optimized FOFPID control strategy outperforms other control schemes in terms of the different difficulty levels of the given trajectories.

To the best of the authors’ knowledge, such a motion controller incorporating the fractional order approach to the fuzzy is first time applied in trajectory tracking control of SP.

The mechanization of the meat cutting companies has become essential due to the lack of skilled workers and to working conditions. This paper deals with the analysis of human gestures in order to improve the performance of a redundant robotic cell. The aim is to define optimization criteria linked to the process and the human gesture analysis to improve the cutting process with a redundant robotic cell.

This paper deals with an optimized path planning of complex tasks based on the human arm analysis. The first part details the operator's manual work. The robotized cutting strategy using bones as a guide associated with an industrial force control leads to the tasks redefinition. Thus, the analysis of the arm during the tasks is presented. With a robotic model, the authors evaluate the relevance of two criteria (kinematic and mechanical) that the operator naturally manages. These criteria are used to improve the robotized cutting process by using redundancy. Simulation work and experimentation are presented to show the enhanced performance.

The paper explains how to define optimization criteria based on human arm analysis to realize cutting operations which require force or dexterity performance. It presents a study on the criteria weighting on a robotic arm model established through human arm analysis. The optimized cutting process clearly shows improvement.

The scalability of the ham implied the definition of iterative trajectories to follow the curvature of the bone. Due to the use of an industrial force control, no online optimization can be achieved. The off-line optimization implies that the boundary of the trajectory space is technically feasible. Nevertheless, more information has to be extracted from the deboning process such as vision data in order to improve cutting quality.

This study was carried out within the framework of several national and European projects (FUI SRDViand, ANR ARMS, FP7 Echord Dexdeb) in collaboration with ADIV (Meat Institute Development Agency). The redundant robotic cell was developed and implemented at ADIV and used for feasibility studies in connection with SME/SMI French sector.

The paper deals with the cutting of soft bodies such as meat and complex human gesture analysis, which constitute an innovative challenge for the coming years in order to help or replace humans in industrial meat companies with difficult working conditions.

An off‐line optimization method is described which allows the determination of “optimal” control strategies in a behavioural systems analysis. The method is based on the theory of learning systems, using a phenomenalistic model of the behavioural process for control experiments. There are no restrictions concerning the degree or order of this model. An outline of an investigation in social psychology demonstrates an application of the proposed method. The analysed data are the same as those used in a report on “System identification in behavioural science” previously published in this journal. This allows comparison of two different approaches to behavioural systems analysis by means of a phenomenalistic model.

The purpose of this paper is to present a reduced order computational strategy for a multi-physics simulation involving a fluid flow, electromagnetism and heat transfer in a hot-wall chemical vapour deposition reactor. The main goal is to produce a multi-parametric solution for fast exploration of the design space to perform numerical prototyping and process optimisation.

Different reduced order techniques are applied. In particular, proper generalized decomposition is used to solve the parameterised heat transfer equation in a five-dimensional space.

The solution of the state problem is provided in a compact separated-variable format allowing a fast evaluation of the process-specific quantities of interest that are involved in the optimisation algorithm. This is completely decoupled from the solution of the underlying state problem. Therefore, once the whole parameterised solution is known, the evaluation of the objective function is done on-the-fly.

Reduced order modelling is applied to solve a multi-parametric multi-physics problem and generate a fast estimator needed for preliminary process optimisation. Different order reduction techniques are combined to treat the flow, heat transfer and electromagnetism problems in the framework of separated-variable representations.

The study has been aimed to search an appropriate process environment for simultaneous optimization of quality‐productivity favorably. Various surface roughness parameters (of the machined product) have been considered as product quality characteristics whereas material removal rate (MRR) has been treated as productivity measure for the said machining process.

In this study, three controllable process parameters, cutting speed, feed, and depth of cut, have been considered for optimizing material removal rate (MRR) of the process and multiple surface roughness features for the machined product, based on L₉ orthogonal array experimental design. To avoid assumptions, limitation, uncertainty and imprecision in application of existing multi‐response optimization techniques documented in literature, a fuzzy inference system (FIS) has been proposed to convert such a multi‐objective optimization problem into an equivalent single objective optimization situation by adapting FIS. A multi‐performance characteristic index (MPCI) has been defined based on the FIS output. MPCI has been optimized finally using Taguchi method.

The study demonstrates application feasibility of the proposed approach with satisfactory result of confirmatory test. The proposed procedure is simple, and effective in developing a robust, versatile and flexible mass production process.

In the proposed model it is not required to assign individual response weights; no need to check for response correlation. FIS can efficiently take care of these aspects into its internal hierarchy thereby overcoming various limitations/assumptions of existing optimization approaches.

Design and optimisation of many practical electromechanical devices involve intensive field simulation studies and repetitive usage of time‐consuming software such as finite elements (FEs), finite differences of boundary elements. This is a costly, but unavoidable process and thus a lot of research is currently directed towards finding ways by which the number of necessary function calls could be reduced. New algorithms are being proposed based either on stochastic or deterministic techniques where a compromise is achieved between accuracy and speed of computation. Four different approaches appear to be particularly promising and are summarised in this review paper. The first uses a deterministic algorithm, known as minimal function calls approach, introduces online learning and dynamic weighting. The second technique introduced as ES/DE/MQ – as it combines evolution strategy, differential evolution and multiquadrics interpolation – offers all the advantages of a stochastic method, but with much reduced number of function calls. The third recent method uses neuro‐fuzzy modelling and leads to even further economy of computation, although with slightly reduced accuracy of computation. Finally, a combined FE/neural network approach offers a novel approach to optimisation if a conventional magnetic circuit model could also be used.