Quanmin Zhu

A framework of neural networks based consensus control for multiple robotic manipulators

Abstract A framework for neural networks (NN) based consensus control is proposed for multiple robotic manipulators systems (MRMS) under leader–follower communication topology. Two situations, that is, fixed and switching communication topologies, are studied by using adaptive and robust control principles, respectively. Radial basis function (RBF) NN enhances estimator and observer are developed to estimate system uncertainty and obtain the leader manipulator׳s control torque online. By using the Lyapunov stability theory, an adaptive consensus control algorithm is designed to tune the weight of the RBF NN online, which can stabilize the consensus error to a small residual set. On this basis, a novel robust control algorithm is presented to eliminate the estimating errors caused by RBF NN, which can achieve asymptotical stability. The stability of the proposed approaches is analyzed by using Lyapunov methods. Finally numerical bench tests are conducted to validate the effectiveness of the proposed approach.

Neural network-based adaptive tracking control for nonlinearly parameterized systems with unknown input nonlinearities

This paper presents tracking control problem of the unmatched uncertain nonlinearly parameterized systems (NLP-systems) with unknown input nonlinearities. Two kinds of nonlinearities existing in the control input are discussed, which are non-symmetric dead-zone input and continuous nonlinearly input. The smooth controller is proposed in either of these two cases by effectively integrating adaptive backstepping technique and neural networks. Some assumptions, in which the parameters with respect to the input nonlinearities are available in advance in previous works, are removed by adaptive strategy. The researches also take the arbitrary unmatched uncertainties and nonlinear parameterization into account without imposing any condition on the system. It is shown that the closed-loop tracking error converges to a small neighborhood of zero. Finally, numerical examples are initially bench tested to show the effectiveness of the proposed results.

Letters: A generalized indirect adaptive neural networks backstepping control procedure for a class of non-affine nonlinear systems with pure-feedback prototype

This study presents a generalized procedure for designing recurrent neural network enhanced control of time-varying-delayed nonlinear dynamic systems with non-affine triangle structure and pure-feedback prototype. Under the framework, recurrent neural network is developed to accommodate the on-line approximation, which the weights of the neural network are iteratively and adaptively updated through system state vector. Based on the neural network online approximation model, an indirect adaptive neural network controller is designed, by means of dynamic compensation, to deal with some of the challenging issues encountered in such complex nonlinear control systems. Taking consideration of the correctness, rigorousness, and generality of the new development, the Lyapunov stability theory is referred to prove that the closed-loop control system is uniformly ultimately bounded stable and the output of the system is converged to a small neighborhood of the desired trajectory. Two bench mark tests are simulated to demonstrate the effectiveness and efficiency of the procedure. In addition these could be the show cases for potential readers/users to digest and/or apply the procedure to their ad hoc problems.

Development of a new EDRNN procedure in control of human arm trajectories

In this paper the trajectory tracking control of a human arm moving on the sagittal plane is investigated by an interdisciplinary approach with the combination of neural network mapping, evolutionary computation, and dynamic system control. The arm in the study is described by a musculoskeletal model with two degrees of freedom and six muscles, and the control signal is applied directly in the muscle space. A new control system structure is proposed to manipulate the complicated nonlinear dynamical arm motion. To design the intelligent controller, an evolutionary diagonal recurrent neural network (EDRNN) is integrated with proper performance indices, in which genetic algorithm (GA) and evolutionary program (EP) strategy are effectively integrated with the diagonal recurrent neural network (DRNN). The hybrid GA with EP strategy is applied to optimize the DRNN architecture and an adaptive dynamic back-propagation (ADBP) algorithm with momentum for the multi-input multi-output (MIMO) systems is used to obtain the network weights. The effectiveness of the control scheme is demonstrated through a simulated case study.

A generalized procedure in designing recurrent neural network identification and control of time-varying-delayed nonlinear dynamic systems

In this study, a generalized procedure in identification and control of a class of time-varying-delayed nonlinear dynamic systems is developed. Under the framework, recurrent neural network is developed to accommodate the on-line identification, which the weights of the neural network are iteratively and adaptively updated through the model errors. Then indirect adaptive controller is designed based on the dichotomy principles and neural networks, which the controller output is designed as a neuron rather than an explicit input term against system states. It should be noticed that including implicit control variable in design is more challenging, but more generic in theory and practical in applications. To guarantee the correctness, rigorousness, generality of the developed results, Lyapunov stability theory is referred to prove the neural network model identification and the designed closed-loop control systems uniformly ultimately bounded stable. A number of bench mark tests are simulated to demonstrate the effectiveness and efficiency of the procedure and furthermore these could be the show cases for potential users to apply to their demanded tasks.

An evolutionary neural network based tracking control of a human arm in the sagittal plane

In this paper trajectory tracking control of a human arm moving in sagittal plane is investigated. The arm is described by a musculoskeletal model with two degrees of freedom and six muscles, and the control signal is applied directly in muscle space. To design the intelligent controller, an evolutionary diagonal recurrent neural network (EDRNN) is integrated with proper performance indices, which a genetic algorithm (GA) and evolutionary program (EP) strategy are effectively combined with the diagonal neural network (DRNN). The hybrid GA with EP strategy is applied to optimize the DRNN structure and a dynamic back-propagation algorithm (DBP) is used for training the network weights. The effectiveness of the control scheme is demonstrated through a simulated case study.

Sliding mode control for discrete time switched systems with uncertain parameters and time delay

This paper studies discrete nonlinear switched systems with uncertain parameters and time-varying delays, the new robust stability criterion is provided in the form of linear matrix inequality based on Lyapunov stability theory, the systems uncertainties were disposed by matrix inequality technique, the new sliding mode surface without time delay term is designed, the robust sliding mode controller designed based on Lyapunov stability theory, the controller input obtained by Newton-Raphson Algorithm in order to achieve uniformly asymptotic stability of the closed-loop system. Finally, a numerical example is given to illustrate the effectiveness of the proposed theory.

A Trajectory Tracking Control Scheme of a Human Arm in The Sagittal Plane

This paper presents a trajectory tracking control scheme for the human arm moving in sagittal plane. The arm is described by a musculoskeletal model with two degrees of freedom and six muscles, and the control signal is applied directly in muscle space. To design the intelligent controller, an evolutionary diagonal recurrent neural network (EDRNN) is integrated with proper performance indices, which a genetic algorithm (GA) and evolutionary program (EP) strategy are effectively combined with the diagonal neural network (DRNN). The hybrid GA with EP strategy is applied to optimize the DRNN structure and a dynamic back-propagation algorithm (DBP) is used for training the network weights. The effectiveness of the control scheme is demonstrated through a simulated case study.

Indirect Fuzzy Adaptive Control of Robotic Manipulator Based on Sliding Mode Scheme

An indirect fuzzy adaptive controller is designed in this paper based on sliding mode scheme to tackle the tracking control issue of a 2D SCARA robot manipulator with system dynamic model uncertainties and external disturbances. The stability of the system is ensured by the controller designed under the Lyapunovs stability theorem, and the effectiveness of the proposed controller is verified by simulation studies with MatLab for the trajectory tracking control.