Optimum robot manipulator path generation using Differential Evolution

This paper deals with real-time implementation of visual-motor control of a 7 degree of freedom (DOF) robot manipulator using self-organized map (SOM) based learning approach. The robot manipulator considered here is a 7 DOF PowerCube manipulator from Amtec Robotics. The primary objective is to reach a target point in the task space using only a single step movement from any arbitrary initial configuration of the robot manipulator. A new clustering algorithm using Kohonen SOM lattice has been proposed that maintains the fidelity of training data. Two different approaches have been proposed to find an inverse kinematic solution without using any orientation feedback. In the first approach, the inverse Jacobian matrices are learnt from the training data using function decomposition. It is shown that function decomposition leads to significant improvement in accuracy of inverse kinematic solution. In the second approach, a concept called sub-clustering in configuration space is suggested to provide multiple solutions for the inverse kinematic problem. Redundancy is resolved at position level using several criteria. A redundant manipulator is dexterous owing to the availability of multiple configurations for a given end-effector position. However, existing visual motor coordination schemes provide only one inverse kinematic solution for every target position even when the manipulator is kinematically redundant. Thus, the second approach provides a learning architecture that can capture redundancy from the training data. The training data are generated using explicit kinematic model of the combined robot manipulator and camera configuration. The training is carried out off-line and the trained network is used on-line to compute the joint angle vector to reach a target position in a single step only. The accuracy attained is better than the current state of art.

This article studies the suitability of modern population based algorithms for designing combination cancer chemotherapies. The problem of designing chemotherapy schedules is expressed as an optimization problem (an optimal control problem) where the objective is to minimize the tumor size without compromising the patient's health. Given the complexity of the underlying mathematical model describing the tumor's progression (considering two types of drugs, the cell cycle and the immune system response), analytical and classical optimization methods are not suitable, instead, stochastic heuristic optimization methods are the right tool to solve the optimal control problem. Considering several solution quality and performance metrics, we compared three powerful heuristic algorithms for real-parameter optimization, namely, CMA evolution strategy, differential evolution, and particle swarm pattern search method. The three algorithms were able to successfully solve the posed problem. However, differential evolution outperformed its counterparts both in quality of the obtained solutions and efficiency of search.

SUMMARYIn most dynamic adaptive control simulation of robotic manipulators, the Langrange–Euler (L–E) dynamic equations are first piecewise linearized about the desired reference and then discretized and rewritten in a state space form. This makes things very complicated and it is easy to make errors. What is more is that with a different reference this work must be done again. A new simulation scheme – Backward Recursive Self-Tuning Adaptive (BRSTA) – as it will be called, is suggested in this paper for adaptive controller design of robot manipulators. A two degree of freedom robot manipulator is used to verify the scheme in the condition of highly nonlinear and highly coupled system. A one degree of freedom robot manipulator is used for comparing both the forward and backward methods. The main advantages of this scheme include that it can be used for evaluating the self-tuning adaptive control laws and provide the initial process parameters for real-time control. And it is concluded here that the Newton–Euler (N–E) dynamic equations are equally well qualified as the Langrange–Euler (L-E) equations for the simulation of self-tuning adaptive control of robot manipulators.

In the impedance control scheme in robotic manipulation applications, the interaction force can be indirectly controlled by acting on the manipulator reference position trajectory. However, the reference position depends on the environment parameters, like location and stiffness, which in practice are not exactly known. To deal with those uncertainties, this paper presents a fuzzy adaptive approach which determines the appropriate reference (target) positions to an impedance controller in order to obtain the desired force/position trajectory. Moreover, only an estimate of the environment location is necessary, and so the fuzzy adaptive controller corrects this estimated position based on the force error. The fuzzy adaptive controller is a kind of self organizing controller, which consists of a direct fuzzy controller and an adaptation mechanism, (the fuzzy inverse model) which adjusts the membership functions of the consequent parts of the direct fuzzy controller. To validate the proposed control scheme, simulation results with a two degree of freedom robot manipulator are presented where it is shown a good force/tracking performance with uncertainties on the environment location and on the stiffness value.

In the paper fuzzy logic controller for rehabilitation robot manipulator is presented. The control object is two degree of freedom robot manipulator. In this case there are two main problems. The first is, that the object is for rehabilitation process so the quality of control system is very important problem. The next problem is with the highly nonlinear dynamics of a robot manipulator. Suggest fuzzy logic control system for that reasons. The model-based Takagi-Sugeno fuzzy control strategy is applied. Described model have two inputs (error and derivation) and one output. The first part of research, was the simulation tests. For this part simulation model was prepare. The dynamic equations was used to build a model in Matlab. During this research the main problem was suitable control quality. It is very important because control quality is connected with rehabilitation process. The control algorithms enable to movement realization and obtain set position given by the physiotherapist. The tests results show that the fuzzy logic control have good control quality.

In this study, trajectory tracking control of six–degrees of freedom robotic manipulator has been performed using the proposed fractional-order adaptive integral sliding mode control scheme. The proposed method is first composed by fractional-order and adaptive integral sliding mode control to achieve the finite-time convergence, chattering-free control inputs, better tracking performance and robustness for the robotic manipulator. Furthermore, an adaptive method has been utilized to evaluate and compensate the uncertain and unknown dynamics of the system without relying on the prior knowledge of the upper bounds. To illustrate the efficiency of the proposed fractional-order adaptive integral sliding mode control method, the real-time experimental studies have been performed for the six–degrees of freedom industrial robot manipulator. The experimental outcomes strongly verified that the proposed controller gives quite well trajectory tracking response and faster convergence compared with the classical integral sliding mode control under the external payload.

This paper presents a decentralized control scheme, based on a recurrent neural identifier with a block control structure, and its application to robotics manipulators. A local joint controller is proposed for each joint, using only local angular position and velocity measurements. These very simple local joint controllers allow trajectory tracking, with reduced computations. The applicability of the proposed scheme is illustrated, via simulations, first by the applications to a two degree of freedom robotic manipulator and then to a seven degree of freedom one.

In this paper, the application of direct parameter optimization method to reentry vehicle three-dimension skipping trajectory optimization is introduced. Firstly, the model of trajectory optimization control problem to reentry vehicle skipping trajectory is established, while motion equation of the state variables is a three degree-of-freedom model. Performance is selected to minimize the sum of heat, overload and dynamic pressure. The control variables are angle-of-attack and bank angle. During the flight, reentry vehicle is subjected to the heat rate and overload constraints. Terminal state variables constraints are path angle, altitude and latitude constraints. Then, the optimal control problem is discretized into nonlinear programming problem (NLP) using direct parameter optimization method. The control variables are selected as optimal parameters at all nodes. Parameter optimization problem is solved using POWELL algorithm. The simulation result shows that the optimal solutions of skipping trajectory optimization problem are in real-time using direct parameter optimization method. Therefore, direct parameter optimization method is a viable approach to the reentry vehicle skipping trajectory optimization problem.

A Gauss pseudospectral method is proposed in this study to solve the optimal trajectory-planning problem for satellite rapid large-angle maneuvers. In order to meet the requirement of rapid maneuver capability of agile small satellites, Single Gimbal Control Moment Gyros (SGCMGs) are adopted as the actuators for the attitude control systems (ACS). Because the singularity problem always exists for SGCMGs during the large-angle maneuvering of the satellites, a trajectory optimization method for the gimbal rates is developed based on the Gauss pseudospectral method. This method satisfies the control requirement of satellite rapid maneuvers and evades the singularity problem of SGCMGs. The method treats the large-angle maneuver problem as an optimization problem, which meets the boundary condition and a series of the physical constraints including the gimbal angle constraint, the gimbal rates constraint, the singularity index constraint, and some other performance criteria. This optimization problem is discretized as a nonlinear programming problem by the Gauss pseudospectral method. The optimal nonsingularity gimbal angle trajectory is obtained by the sequence of quadratic programming (SQP). This approach avoids the difficulties in solving the boundary value problem. The simulations reveal that the Gauss pseudospectral method effectively plans an optimal trajectory and satisfies all the constraints within a short time.

A robust controller is proposed for the accurate trajectory control of a general n degree of freedom robotic manipulator, using feedback of task space coordinates, in the presence of large perturbations of the manipulator dynamic parameters. The controller is shown to consist of two separate devices: 1) a robust compensator which makes the system insensitive to parameter perturbation and causes asymptotic regulation to occur for a certain class of reference input/disturbance signals, and 2) a stabilizing compensator which stabilizes the closed-loop system. Asymptotic stability of the closed-loop system is demonstrated, using a Lyapunov approach, for large perturbations of the plant parameters. A numerical simulation of a two degree of freedom manipulator, with a large unknown payload mass is used to demonstrate the effectiveness of the controller.

Predictive Control Architecture for Visual Servoing of Robot Manipulators

The authors analyse the contour error dynamics in contouring control of dual‐arm robotic manipulators with holonomic constraints. With the modified dynamics of robot, the dynamics of dual‐arm robot with holonomic constraints is symetrically transformed into dynamics error problem by considering the equivalent error method. It becomes the control problem of stabilisation such that it is suitable for robust control approach to improve control performance in terms of contouring control, i.e. contour accuracy with high speed. The proposed method can deal with both analytic and non‐analytic functions of desired path in task space. The experimental results reveal that the proposed method yields excellent performance in comparison to conventional distributed control. As a result, the proposed method can lead to the development of a modified distributed control for high degree of freedom robot manipulators.

SUMMARYIn this work, we present a continuous observer and continuous controller for a multiple degree of freedom robot manipulator with hysteretic joint friction. The fictitious hysteresis state is of course unknown to the controller and must be estimated. The joint velocities are assumed measured here. For this considered plant, we propose and present a continuous observer/controller that estimates or observes the hysteresis state and drives the position tracking error to zero. We prove that the combined tracking error and observer error converges to zero globally exponentially.

Performing tracking tasks in robotic manipulators presents many challenges for the controller design, especially in presence of external disturbances that affects the dynamical behavior of the robotic system. This paper presents the design of a Fractional-Order PID controller with the computed torque control strategy for the tracking control of a two degree of freedom robotic manipulator. The proposed technique is contrasted against the classical PID controller with the computed torque control strategy. To validate the proposed controllers, the robotic system is simulated using an MSC-ADAMS/MATLAB co-simulation model, which is employed for identification and control tasks. The proposed model is tested in presence of external disturbances in the applied torque, random noise in the feedback loop and payload variations. Obtained results show that the Fractional-Order PID controller with the computed torque control strategy has a better performance in presence of the analyzed external disturbances for tracking tasks.

The use ofrobotic devices for drug delivery in sensitive area of the human body is an innovative and reliable solution. Most of them use magnetics fields, to steer micro-nano-robots into diseases locations. In this study, we use a magnetic actuator based on two permanent magnets as an end-effector of a robotic manipulator. The actuator offers the possibility to generate both pushing and pulling forces on the magnetic actuator axis in an open-loop control way. We describe in this paper the robotic drug delivery strategy that we implemented in a 6 degree of freedom robotic manipulator to push and to steer a magnetic microparticle from the round window to the apex of the human cochlea. Different experiments have been conducted in order to demonstrate the effectiveness and robustness of the navigation proposed strategy on a human phantom cochlea. The results demonstrate clearly that precise and reliable drug administration is rendered possible.