Discrete adaptive fuzzy control for asymptotic tracking of robotic manipulators

This paper presents a novel discrete adaptive fuzzy controller for electrically driven robot manipulators. It addresses how to overcome the nonlinearity, uncertainties, discretizing error and approximation error of the fuzzy system for asymptotic tracking control of robotic manipulators. The proposed controller is model-free in the form of discrete Mamdani fuzzy controller. The parameters of fuzzy controller are adaptively tuned using an adaptive mechanism derived by stability analysis. A robust control term is used to compensate the approximation error of the fuzzy system for asymptotic tracking of a desired trajectory. The controller is robust against all uncertainties associated with the robot manipulator and actuators. It is easy to implement since it requires only the joint position feedback. Compared with fuzzy controllers which employ all states to guarantee stability, the proposed controller is very simpler. Stability analysis and simulation results show its efficiency in the tracking control.     

Adaptive fuzzy voltage-based backstepping tracking control for uncertain robotic manipulators subject to partial state constraints and input delay

Nonlinear Dynamics - The aim of this paper is to tackle the problem of adaptive fuzzy voltage-based tracking control for uncertain electrically driven robotic manipulators subject to input delay...     

Fuzzy neural adaptive tracking control of unknown chaotic systems with input saturation

The contribution of this work is to study the control of unknown chaotic systems with input saturation, and the backstepping-based an adaptive fuzzy neural controller (AFNC) is proposed. In many practical dynamic systems, physical input saturation on hardware dictates that the magnitude of the control signal is always constrained. Saturation is a potential problem for actuators of control systems. It often severely limits system performance, giving rise to undesirable inaccuracy or leading instability. To deal with saturation, we construct a new system with the same order as that of the plant. With the error between the control input and saturation input as the input of the constructed system, a number of signals are generated to compensate the effect of saturation. Finally, simulation results show that the AFNC can achieve favorable tracking performances.     

Practical tracking control under actuator saturation for a class of flexible-joint robotic manipulators driven by DC motors

Nonlinear Dynamics - This paper is devoted to the practical tracking control for a class of flexible-joint robotic manipulators driven by DC motors. Different from the related literature where...     

Distributed adaptive neural control for uncertain multi-agent systems with unknown actuator failures and unknown dead zones

Nonlinear Dynamics - In actuality, the dead zones and failures often occur in actuators, but the existing algorithms have difficulty simultaneously tolerating dead zones and actuator failures in...     

Distributed adaptive tracking control for multi-agent systems with uncertain dynamics

Nonlinear Dynamics - In this paper, we investigate a general integrable three-component coupled nonlocal nonlinear Schrödinger system with the parity-time symmetry. The general Nth Darboux...     

Single neural network approximation based adaptive control for a class of uncertain strict-feedback nonlinear systems

A new adaptive control design approach is presented for a class of uncertain strict-feedback nonlinear systems. In the controller design process, all unknown functions at intermediate steps are passed down, and only one neural network is used to approximate the lumped unknown function of the system at the last step. By this approach, the designed controller contains only one actual control law and one adaptive law, and can be given directly. Compared with existing methods, the structure of the designed controller is simpler and the computational burden is lighter. Stability analysis shows that all the closed-loop system signals are uniformly ultimately bounded, and the steady state tracking error can be made arbitrarily small by appropriately choosing control parameters. Simulation studies demonstrate the effectiveness and merits of the proposed approach.     

Approximate continuous fixed-time terminal sliding mode control with prescribed performance for uncertain robotic manipulators

This paper studies an approximate continuous fixed-time terminal sliding mode control (CFTSMC) with prescribed performance for uncertain robotic manipulators. A transformation concerning tracking error using a fixed-time prescribed performance function is proposed to guarantee the transient and steady-state performance of trajectory tracking control for uncertain robotic manipulators within fixed time. Utilizing the transformed error, a smooth fixed-time sliding mode surface is designed. Then, based on the proposed sliding mode surface, an approximate CFTSMC scheme is presented to achieve inherent chattering-free control for uncertain robotic manipulators. According to the Lyapunov stability theory, it is proved that the position tracking error can be bounded in the prescribed performance boundaries and globally converges to a defined small region within fixed time and then approaches exponentially to the origin. Several numerical simulation results demonstrate the effectiveness and superiority of the proposed control strategy for uncertain robotic manipulators.

     

Finite-time synchronization control for uncertain Markov jump neural networks with input constraints

This paper is concerned with the problem of finite-time synchronization control for uncertain Markov jump neural networks in the presence of constraints on the control input amplitude. The parameter uncertainties under consideration are assumed to belong to a fixed convex polytope. By using a parameter-dependent Lyapunov functional and a simple matrix decoupling method, a sufficient condition is proposed to ensure that the considered networks are stochastically synchronized over a finite-time interval. The desired mode-independent controller parameters can be computed via solving a convex optimization problem. Finally, two chaos neural networks are employed to demonstrate the effectiveness of our proposed approach.     

Decentralized adaptive neural network control for mechanical systems with dead-zone input

We propose a decentralized adaptive robust controller for trajectory tracking of mechanical systems with dead-zone input in this paper. The considered mechanical systems are with high-order interconnections and unknown non-symmetric nonlinear input. In each local controller, the neural network control is introduced to estimate the uncertainties and disturbances, meanwhile the siding mode control and adaptive technical are designed to compensate for the approximation errors. A nonlinear function is chosen to deal with the interconnections. Following, the stability and robustness are verified by using Lyapunov stability theorem. Finally, simulations are provided to support the theoretical results     

Neural adaptive control of uncertain chaotic systems with input and output saturation

Nonlinear Dynamics - This paper is concerned with the neural adaptive control design problem of a class of chaotic systems with uncertain dynamics, input and output saturation. To attenuate the...     

Decentralized sliding mode quantized feedback control for a class of uncertain large-scale systems with dead-zone input

This paper is concerned with the robust quantized feedback stabilization problem for a class of uncertain nonlinear large-scale systems with dead-zone nonlinearity in actuator devices. It is assumed that state signals of each subsystem are quantized and the quantized state signals are transmitted over a digital channel to the controller side. Combined with a proposed discrete on-line adjustment policy of quantization parameters, a decentralized sliding mode quantized feedback control scheme is developed to tackle parameter uncertainties and dead-zone input nonlinearity simultaneously, and ensure that the system trajectory of each subsystem converges to the corresponding desired sliding manifold. Finally, an example is given to verify the validity of the theoretical result.     

Adaptive neural network consensus tracking control for uncertain multi-agent systems with predefined accuracy

Nonlinear Dynamics - This paper proposes the consensus tracking control problem for a class of uncertain nonlinear multi-agent systems. By using a group of nonnegative functions, an adaptive neural...     

Low-complexity differentiator-based decentralized fault-tolerant control of uncertain large-scale nonlinear systems with unknown dead zone

This paper investigates a low-complexity robust decentralized fault-tolerant prescribed performance control scheme for uncertain larger-scale nonlinear systems with consideration of the unknown nonlinearity, actuator failures, dead-zone input, and external disturbance. Firstly, a new simple finite-time-convergent differentiator is developed to obtain the unmeasurable state variables with arbitrary accuracy. Then, a time-varying sliding manifold involving the output tracking error and its high-order derivatives is constructed to tackle the high-order dynamics of subsystems. Sequentially, a robust decentralized fault-tolerant control scheme is proposed for each sliding manifold with prescribed convergence rate. The prominent advantage of the proposed fault-tolerant control scheme is that any specialized approximation technique, disturbance observer, and recursive procedure of backstepping technique are avoided, which dramatically alleviates the complexity of controller design. Finally, two groups of illustrative examples are employed to demonstrate the effectiveness of the low-complexity decentralized fault-tolerant control scheme under the developed finite-time-convergent differentiator.     

Neural network-based adaptive control of a class of uncertain nonlinear systems

I.ProblemF0muIationandAssumptionsConsiderthenonlinearsystemfwhereeeR-isthestate,andavailableformeasurement,'.ERistheinput,geRistheoutput.hisasmoothfunctiononR-withh(0)=O,andthevectorfieldsI',g"aresmo0thonR-withg'(x)*O,VxeR--andI'(O)-O.ForcIarity0fexpositi…     

Trajectory tracking control using velocity observer and disturbances observer for uncertain robot manipulators without tachometers

This paper deals with trajectory tracking control for rigid robot manipulators with model uncertainty and subject to external disturbances. The approach suggested herein does not require velocity measurement, because these robots are not equipped by tachometers for velocity measurement. For this purpose, two observers are proposed. The first is a velocity observer to estimate the missing velocity, and the second one is a disturbance observer to estimate the disturbance. Thereafter, these observers are integrated with the controller. Furthermore, semi-global asymptotic stability conditions of the composite controller consisting of a nonlinear controller, the velocity observer and the disturbance observer are established, and an estimate region of attraction is also given. This proof is based on Lyapunov theory. Finally, simulation results on two-links manipulator are provided to illustrate the effectiveness of the velocity observer based control using disturbance estimation (namely VOBCDE), when the Coulomb and viscous friction is considered as an external disturbance.     

Adaptive neural tracking control for a class of perturbed pure-feedback nonlinear systems

This paper focuses on the problem of the adaptive neural control for a class of a perturbed pure-feedback nonlinear system. Based on radial basis function (RBF) neural networks’ universal approximation capability, an adaptive neural controller is developed via the backstepping technique. The proposed controller guarantees that all the signals in the closed-loop system are bounded and the tracking error eventually converges to a small neighborhood around the origin. The main advantage of this note lies in that a control strategy is presented for a class of pure-feedback nonlinear systems with external disturbances being bounded by functions of all state variables. A numerical example is provided to illustrate the effectiveness of the suggested approach.