Adaptive neural network tracking control for manipulators with uncertain kinematics, dynamics and actuator model

This paper investigated two novel robust adaptive NN-based backstepping algorithms for the tracking problem of robotic rigid-link manipulators with uncertain kinematics, dynamics and actuator model. The controllers are both designed based on the backstepping scheme. Uncertainties and external disturbances in the manipulator dynamics are compensated by radial basis function in every step of backstepping. The first control algorithm could guarantee the tracking error converges to a small neighborhood of zero; the second improved control algorithm demanding link accelerations information could make sure the tracking error goes to zero quickly and precisely. The feasibility and effectiveness of the proposed methods are verified by simulation studies.

With the development of robot control theory and the requirement for robot to provide better performance in uncertain working environment, more and more research interests have been attracted to the two research topics of dealing with kinematic uncertainty existing in the robot system and considering actuator dynamics into the controller design during the past one decade. However, the studies in these two research areas have been developing separately in the literature. This thesis is dedicated to solve the adaptive control problems of robot in the presence of all uncertainties in the kinematics, manipulator dynamics and actuator dynamics. The main contribution of the thesis is that a novel adaptive regressor design method is proposed which enables the adaptation of different kinds of uncertainties simultaneously during the control process. Based on the novel regressor design, adaptive control methods have been proposed for regulation and tracking control of robot with uncertain kinematics, manipulator and actuator dynamics. These approaches constitute the first results in the research area of robot control to deal with both kinematic and actuator dynamic uncertainties. Through Lyapunov analysis, the stability of the closed-loop systems are shown to be guaranteed and the sufficient conditions to be satisfied are explicitly presented.

In this paper, backstepping strategy is used to design an adaptive tracking controller for rigid-link electrically driven robots in the presence of uncertainties in kinematics, manipulator dynamics and actuator dynamics. First, the armature current vector is regarded as the control variable for the manipulator subsystem and a desired armature current is designed based on the inverse manipulator dynamics. Then, an input control voltage is designed so that armature current tracks the desired armature current. Simulation results of applying the proposed controller on a two link RLED robot are presented to show the effectiveness of the proposed control scheme.

This paper studies the region tracking control problem of robot manipulator systems modeled by Lagrangian dynamics with uncertain kinematics and dynamics. By introducing a novel sliding mode, an adaptive controller is constructed to make the robot manipulator reach and track a desired dynamic region. Furthermore, a simple yet generic criterion on the region tracking control problem for robot manipulator systems is derived. It is shown that the robot end-effector can be able to reach in the desired dynamic region with the proposed controller in the presence of the uncertain kinematics and dynamics parameters. Based on the investigation, we revisit the region tracking problem with different control methodologies, to gain a brand-new understanding in the region tracking control problem. Finally, simulation results are presented to demonstrate the effectiveness of the theoretical results.

Indirect adaptive neural tracking control of USVs under injection and deception attacks

This paper addresses the adaptive neural network tracking control problem for a class of strict-feedback systems with unknown non-linearly parameterised and time-varying disturbed function of known periods. Radial basis function neural network and Fourier series expansion are combined into a new function approximator to model each suitable disturbed function in systems. Dynamic surface control approach is used to solve the problem of ‘explosion of complexity’ in backstepping design procedure. The uniform boundedness of all closed-loop signals is guaranteed. The tracking error is proved to converge to a small residual set around the origin. A simulation example is provided to illustrate the effectiveness of the control scheme designed.

An adaptive neural-network tracking control with a guaranteed H/sup /spl infin// performance is proposed for robotic systems with plant uncertainties and external disturbances. A neural-network system is introduced to learn these unknown (or uncertain) dynamics by an adaptive algorithm, Moreover, the effects on the tracking error due to the approximation error via the adaptive neural network must be attenuated to a prescribed level, i.e. an H/sup /spl infin// tracking performance is achieved. Hence, in this study, both the H/sup /spl infin// tracking theory and adaptive neural-network control scheme are combined together to achieve the nonlinear adaptive H/sup /spl infin// tracking control design for uncertain or unknown robotic systems. The developed control scheme is smooth and semiglobal as well as very simple and computationally efficient, since it does not require a knowledge of either the mathematical model or the parameterization of the robotic dynamics. Finally, extensive simulations are given to illustrate the tracking performance of a two-link robotic manipulator with the proposed adaptive neural H/sup /spl infin// control design.

This paper investigates the adaptive neural network (NN) tracking control problem for stochastic nonlinear systems with multiple actuator constraints and full-state constraints. The issue of system full-state constraints is tackled by a generalized barrier Lyapunov function (GBLF), and the output constraints of the system are considered to be in the form of time-varying functions, which are more in line with the needs of real physical systems. The NN approximation technique is utilized to overcome the influence of the uncertainty term on controller design due to randomness. Based on the backstepping technique, a neural adaptive fixed-time tracking control strategy is designed. Under the designed control strategy, the tracking accuracy of the controlled system can reach the expectation in a fixed time. The multi-actuator constraints are converted into a generalized mathematical model to simplify the controller design process. Using the characteristics of the hyperbolic tangent function, a new function called practical virtual control signal is designed using the virtual control signal as the input. Due to the saturation constraint property of the hyperbolic tangent function, it is theoretically ensured that no state of the system exceeds the constraints through to the new form of the virtual controller. Using the adaptive controller constructed in this paper, the controlled system is semi-global fixed-time stabilized in probability (SGFSP). Finally, the effectiveness of the proposed control strategy is further verified by simulation examples.

SummaryThis study examines the fixed‐time adaptive neural network tracking control problem for a class of unknown multi‐input and multi‐output (MIMO) nonlinear pure‐feedback systems. The introduction of the radial basis function resolves uncertain problems of unknown MIMO systems. The mean value theorem is introduced to overcome the controller design problem attributed to the nonaffine structure in pure‐feedback systems. Moreover, a novel fixed‐time virtual controller and an actual controller are designed to solve the issue of previous single‐input and single‐output and MIMO systems that have no solution in the negative domain and at the origin in finite‐ and fixed‐time controls. Furthermore, a design method is proposed. The final designed controller ensures that all signals in the system are bounded. Simulation experiments show that the designed fixed‐time controller facilitates smaller tracking error of the system compared with other finite‐ or fixed‐time controllers. Furthermore, the selection of appropriate design parameters allows the tracking error to converge on a small neighborhood of the origin in a fixed time.

This paper is concerned with the problem of globally stable adaptive neural network tracking control for a class of output feedback systems with unknown functions. Unknown functions are approximated via online radial basis function neural network, continuously differentiable functions are introduced into Lyapunov functions to realize parameter estimation. Barbalat's lemma is used to prove that all closedloop signals are globally uniformity ultimately bounded and tracking error can reach prior accuracy. A simulation example is given to verify the effectiveness of the control method.

This brief addresses the problem of designing adaptive neural network tracking control for a class of strict-feedback systems with unknown time-varying disturbances of known periods which nonlinearly appear in unknown functions. Multilayer neural network (MNN) and Fourier series expansion (FSE) are combined into a novel approximator to model each uncertainty in systems. Dynamic surface control (DSC) approach and integral-type Lyapunov function (ILF) technique are combined to design the control algorithm. The ultimate uniform boundedness of all closed-loop signals is guaranteed. The tracking error is proved to converge to a small residual set around the origin. Two simulation examples are provided to illustrate the feasibility of control scheme proposed in this brief.

Adaptive neural network tracking control for a class of unknown nonlinear time-delay systems

An adaptive neural network tracking controller is designed, capable of stabilizing multi-input multi-output nonlinear, affine in the control dynamical systems with measurement error. Uniform ultimate boundedness of the tracking error of the actual system state is guaranteed, as well as boundedness of all other signals in the closed loop. Possible division by zero is avoided with the use of a novel resetting procedure, capable of guaranteeing the boundedness away from zero of certain signals.

Adaptive neural network control for nonlinear output-feedback systems under disturbances with unknown bounds

Adaptive inverse dynamics control of robots with uncertain kinematics and dynamics