Adaptive state feedback control of robotic manipulators using neural networks

Both dynamic state feedback as well as output feedback tracking control designs are presented in this paper for constrained robot systems under parametric uncertainties and external disturbances. The previous studies on tracking control design, not considering the velocity measurements, address only the unconstrained robot design. In contrast, a dynamic output feedback controller based on a linear and reduced-order observer that uses only position measurements is proposed here for the first time to treat the trajectory tracking control problem of constrained robot systems. Both adaptive state feedback control schemes and adaptive output feedback control schemes with a guaranteed H∞ performance are constructed. It is shown that all the variables of the closed-loop system are bounded and a pre-assigned H∞ tracking performance is achieved, in the sense that the influence of external disturbance on the tracking motion error can be attenuated to any specified level. Moreover, it is also shown that the motion and force trajectories asymptotically converge to the desired ones as the dynamic model of robot systems is well-known and the external disturbance is neglected. Finally, simulation examples are presented to illustrate the tracking performance of a two-link robotic manipulator with a circular path constraint by the proposed control algorithms. © 1998 John Wiley & Sons, Ltd.

This article is concerned with passivity and synchronization for fuzzy coupled reaction-diffusion neural networks (FCRDNNs) with multistate couplings or multiple spatial-diffusion couplings. First, by utilizing an adaptive state feedback controller, several passivity criteria for the FCRDNNs with multistate couplings are obtained. Moreover, a sufficient condition to guarantee the synchronization for the multistate coupled FCRDNNs is also given on the basis of the devised adaptive state feedback control scheme. Additionally, the problems of passivity and synchronization are also addressed for the FCRDNNs with multiple spatial-diffusion couplings by employing the adaptive control technique and Lyapunov functional method. Finally, two numerical examples are presented to validate the effectiveness of the devised adaptive control schemes.

This paper focuses on the adaptive control problems for a class of interconnected systems. The nonLinear uncertainties of the systems under study are bounded by polynomial functions with unknown gains. Firstly, the adaptive feedback controller is proposed to guarantee the stabiLity of the closed-loop system. Then the proposed adaptive idea is extended to robust stabiLizing designing method for a class of large-scale interconnected systems. Based on Lyapunov's theory and LMI method, we can deal with the time-delay term in the system and construct the corresponding adaptive control and state feedback controller. Finally, an illustrative example is given to show the vaLidity of the proposed approach.

Magnetic levitation system is a typical nonlinear and instable system. Based on the complexity and inaccuracy of modelling, in this paper identified magnetic levitation system using the speciality that neural network(NN) can approach any nonlinear function. A Radial Basis Function neural network (RBFNN) controller is designed based on the neural network adaptive control principle. This paper proposes a control method which combine neural network adaptive control method and state feedback control method based on RBFNN. A simulation of the system is proposed, and the result shows that RBFNN could approach magnetic levitation system very well, neural network adaptive state feedback controller has a good effect on this nonlinear system; this control system has a preferable stability and control property.

ABSTRACTThis article proposes an adaptive tracking control scheme for a class of switched stochastic non‐linear systems subject to full state constraints (FSCs). A unified framework is established by integrating the adaptive backstepping technique with barrier Lyapunov functions (BLFs) to address the time‐varying full state constraints (TFSCs). Accordingly, utilizing the theorem of stochastic non‐linear systems, a new adaptive state feedback control scheme is developed for a class of switching signals with the mode‐dependent average dwell time (MDADT) property. The proposed adaptive controller is mode‐dependent while the adaptive laws are mode‐independent so as to facilitate an easy deployment of the controller. It is rigorously proven that all signals in the closed‐loop system are bounded in probability, the tracking error converges to a bounded compact set, and the TFSCs are not violated. Finally, simulation examples are conducted to validate the effectiveness of the controller.

A General Failure Detection, Isolation and Accommodation System with Model Uncertainty and Measurement Noise

Actuator failure compensation problem for nonlinear systems is formulated and adaptive actuator failure compensation control with state feedback is investigated for feedback linearizable systems. Under different structure conditions which characterize different classes of systems, adaptive state feedback control schemes are developed for systems with uncertain actuator failures. It is proved that the proposed control schemes ensure asymptotic output tracking and closed-loop signal boundedness.

Adaptive actuator failure compensation for parametric strict feedback systems and an aircraft application

Lag [formula omitted] synchronization and lag synchronization for multiple derivative coupled complex networks

In this paper, we propose an adaptive fuzzy output feedback control method for trajectory tracking control problem for robotic systems. Using Lyapunov method, we first develop a stable adaptive fuzzy state feedback control algorithm by assuming that the systems output and its derivatives are available for feedback control design. The algorithm combines fuzzy systems with robust adaptive controller. The fuzzy system approximates the certainty equivalent (CE)-based optimal controller while robustifying adaptive control term is used to cope with uncertainties that appeared from the effect of external disturbance, fuzzy approximation errors and other modeling errors. Then, an output feedback form of the position-velocity (state feedback) controller is proposed where unknown velocity signal is replaced by the output of model-free linear estimator. We show via asymptotic analysis that the tracking performance of the output feedback design can recover the performance achieved under the state feedback control design. Finally, the proposed method is implemented and evaluated on a 2-DOF robotic system to demonstrate the theoretical development for the real-time applications.

With the increasing complexity of control systems in industrial process control, traditional controllers are greatly limited in many occasions, so the development of new controller is extremely urgent. This paper combines adaptive feedback control with PID control, and designs a new type of adaptive PID feedback controller. The designed controller is verified by establishing a physical model of the 6-DOF collaborative robot. On the basis of improved adaptive PID feedback control, precise tracking control of multi-DOF cooperative robots is achieved, which improves response speed and eliminates steady-state errors. The designed controller is compared with classical PID control and adaptive control. The steady-state performance, dynamic performance, and anti-interference performance of position control and velocity control are analyzed. The improved adaptive PID feedback control has better control performance. The feasibility and reliability of the adaptive PID feedback controller for collaborative robots are verified.

It is very challenging to improve the dynamic response speed and stability of digital control system, due to the inherent defects of digital controller such as quantization effect and delay in sampling step. This brief proposes an adaptive logarithmic state feedback control (ALSFC) for three-phase grid-tied and off-grid inverters which can be specially applied in digital control system. In the state feedback control system, the feedback coefficient is designed as adaptive logarithmic function of the difference value between the actual state variable and the reference, so as to make the closed-loop system converge to new equilibrium point after about one switching cycle. Comparative experiments verify that the proposed control, carried out by digital controller, has the dynamic response characteristics of quasi one-switching-cycle with its response speed is significantly higher than that of PI control, and has good stability with lower THD.

In this paper, the stabilization problem of a class of continuous-time chaotic systems with unknown parameters is investigated. Based on the adaptive control idea, a novel adaptive state feedback controller is developed. By choosing appropriate controller structure and parametric updating law, the asymptotic stability of the closed-loop system is guaranteed. Simulation results are given to demonstrate the effectiveness and validity of the proposed adaptive control method.

In this paper, the stabilization problems of a class of new hyperchaotic systems with fully unknown parameters are concerned. Based on the adaptive control idea, two novel adaptive state feedback controllers are designed to implement the asymptotic stabilization of this kind of new hyperchaotic system by choosing appropriate controller structure and parametric updating law, respectively. The effectiveness of the proposed adaptive control method is verified by simulation results.

This research tries to stabilise a three degree-of-freedom series-type double inverted pendulum system by employing an adaptive state feedback control approach optimised by a particle swarm optimisation (PSO) algorithm. To this end, the dynamical equations are derived via Lagrangian method and linearised by utilising approximation approaches. Then, the state feedback scheme is implemented to control the system states, that is, the angle and angular velocity of the first and second poles as well as the position and velocity of the cart. In order to timely regulate the control gains, the adaptation rules based on the gradient descent method and sliding surface relations are applied. The particle swarm optimisation algorithm is implemented to optimally tune the design parameters of the controller based on a proper objective function defined as summation of integrals of absolute values of the system errors. The time responses of the angles of the poles and the position of the cart clearly depict the feasibility and efficiency of the introduced control strategy to stabilise the system from different initial conditions to the final desired values.