Robust adaptive control of robot manipulators using generalized fuzzy neural networks

This chapter presents a robust Adaptive Fuzzy Neural Controller (AFN C) suitable for identification and control of uncertain Multi-Input-Multi-Output (MIMO) nonlinear systems. The proposed controller has the following salient features: (1) Self-organizing fuzzy neural structure, i.e. fuzzy control rules can be generated or deleted automatically; (2) Online learning ability of uncertain MIMO nonlinear systems; (3) Fast learning speed; (4) Fast convergence of tracking errors; (5) Adaptive control, where structure and parameters of the AFNC can be self-adaptive in the presence of disturbances to maintain high control performance; (6) Robust control, where global stability of the system is established using the Lyapunov approach. Two simulation examples are used to demonstrate excellent performance of the proposed controller.

This paper presents a robust adaptive fuzzy neural controller (RAFNC) suitable for identification and control of uncertain MIMO nonlinear systems. The proposed controller has the following salient features: (1) Self-organizing fuzzy neural structure, i.e. fuzzy control rules can be generated or deleted automatically; (2) Online adaptive learning ability of uncertain nonlinear systems; (3) Fast adaptation and learning speed; (4) Ease of incorporating expert knowledge; (5) Adaptive control, where structure and parameters of the RAFNC can be self-adaptive in the presence of disturbances to maintain high control performance; (6) Robust control, where global stability of the system is established using the Lyapunov approach. Simulation studies on an inverted pendulum and a two-link robot manipulator show that the performance of the proposed RAFNC is superior over many existing schemes.

This paper presents the current state of the art in the adaptive control of single rigid robotic manipulators in the constrained motion tasks. A complete mathematical model of a single rigid robotic manipulator in contact with dynamic environment is presented. The basic approaches in deriving the environment model are given. The significance of the dynamic environment in the scope of the stability problem of the whole system robot-dynamic environment is emphasized. A classification of the adaptive contact control concepts in manipulation robotics is presented. The main characteristics of the most important adaptive strategies in constrained manipulation are given. The advantages and the drawbacks of the presented methods are emphasized. The paper covers results published a few years ago, as well as some recent trends in this field. One important result in the stability analysis of robotic manipulators in the constrained motion tasks is reported. Finally, some concluding remarks are given and possible future investigation trends in adaptive control of robotic manipulators are indicated.

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.

This paper presents two kinds of adaptive control schemes for robot manipulator which has the parametric uncertainties. In order to compensate these uncertainties, we use the NN (neural network system) that has the capability to approximate any nonlinear function over the compact input space. In the proposed control schemes, we need not derive the linear formulation of robot dynamic equation and tune the parameters. We also suggest the robust adaptive control laws in all proposed schemes for decreasing the effect of approximation error. To reduce the number of neural of network, we consider the properties of robot dynamics and the decomposition of the uncertainty function. The proposed controllers are robust not only to the structured uncertainty such as payload parameter, but also to the unstructured one such as friction model and disturbance. The validity of the control scheme is shown by computer simulations and experiment of dual-arm robot manipulator.

A robust Sugeno type adaptive fuzzy neural network (RSAFNN) backstepping control for a two-axis motion control system is proposed in this paper. The adopted two-axis motion control system is composed of two permanent magnet linear synchronous motors (PMLSMs). The single-axis motion dynamics with the introduction of a lumped uncertainty, which includes parameter variations, external disturbances, cross coupled interference between the two PMLSMs and fiction force, is derived first. Then, a backstepping control approach is proposed to compensate the lumped uncertainty occurred in the two-axis motion control system. Moreover, to improve the control performance in the tracking of the reference contours, a RSAFNN backstepping control is proposed where a Sugeno type adaptive fuzzy neural networks (SAFNN) is employed to estimate the lumped uncertainty directly. Furthermore, the proposed control algorithms are implemented in a TMS320C32 DSP-based control computer.

This paper studies the multiple-model based adaptive control of a robot manipulator moving in varying environments. The research problem is divided into two parts: the modeling of the system consisting of the robot manipulator and the varying environment, and the multiple-model based adaptive control of the robot manipulator. This paper considers the added mass, added moment of inertia, drag, and buoyancy as the environmental factors. In these dynamic models, the environmental factors and the mass, moment of inertia, and gravity of the robot are unknown parameters. By the linearity in the parameters property, we can write these parameters independently of the robot joint variables and thus can be estimated using an adaptive control law. After obtaining the system model, we adopt a multiple-model based adaptive control scheme. When the model of the robot changes, the parameter estimates can rapidly convert to a relatively closer one for the new true values. With a multiple-model based adaptive controller, the asymptotic tracking of the robot and the parameter boundedness are achieved, and the tracking is not disturbed by the variance of the environment parameters in the multiple model control case, which has better performance than the single model case.

Real-time motion planning and control of multi-joint robot manipulators are studied using neural networks. The proposed neural network approach consists of two modules: one for real-time collision-free motion planning, the other for real-time fine control of the robot manipulators. The motion planning module is a biologically inspired, parallel connected, topologically organised neural network, where each neuron is characterised by a shunting equation, whose state space is the robot configuration joint space. This module is capable of planning a real-time optimal path for robot manipulators through the dynamic activity landscape of the neural network. The motion control module involves of a feedforward neural network together with a traditional PD feedback loop. It is capable of achieving real-time fine motion control of robot manipulators under significant uncertainties and without any prior knowledge of the robot dynamics. This module can quickly compensate sudden changes in the robot dynamics. The real-time fine control of robot manipulators is achieved through the online learning of the neural network. Both the motion planning and motion control modules are computationally efficient, and their global stabilities are proved using Lyapunov stability theory.

Adaptive control of robot manipulator is reviewed in this paper. General structure of the adaptive algorithms is presented with reference to the research carried out in recent years. General approach to the stability analysis of algorithms is highlighted. Extensive simulation study confirms the potential of adaptive control theory in dealing with the highly nonlinear, uncertain and complex robot arm dynamics.

In recent years, robotic systems have been widely used in different applications, and this has motivated researchers to develop different control methods. A model-free, intelligent, robust control method for a nonlinear robotic manipulator system is proposed in this work. This paper presents a novel solution for the major drawbacks of the sliding mode control scheme, which are chattering. Prior knowledge is needed about the dynamic model of the controlled system and the upper bound of uncertainty. In this paper, a fuzzy-like PD controller with SMC (FLPDSM) is proposed. The fuzzy-like PD controller was designed according to fuzzy rules and membership functions based on the nominal model of the robot manipulator. A robust control term was added to the control signal to compensate for the system uncertainty, and external disturbances are compensated by adding an auxiliary robust term to the SMC control law. Two methods for designing robust control terms are proposed. The first proposed method assumes that the upper bound of system uncertainty is known although it cannot be exactly determined due to external disturbances and uncertainty. Hence, a second method was proposed that assumes this bound to be unknown, and an adaptive gain based on Lyapunov theory was used to derive the adaptation law. The Lyapunov second method was used to ensure the stability of the closed loop system. Performance tests on the proposed methods were implemented through simulation studies for the two-link robotic manipulator, and the test results were compared with the standard SMC to verify the effectiveness of the proposed method. A good trajectory tracking with a high robustness against parameter variations and external disturbances was observed under the presented control scheme.

Radial basis function network (RBFN) is used in this paper for predefined trajectory control of both one-link and two-link robotic manipulators. The updating equations for the RBFN parameters were derived using the gradient descent principle. The other advantage of using this principle is that it shows the clustering effect in distributing the radial centres. To increase the complexity, the dynamics of robotic manipulator is assumed to be unknown, and hence, simultaneous control and identification steps were performed using the RBFNs. The performance of the RBFN is compared with the multilayer feed-forward neural network (MLFFNN) in terms of mean square error, tolerance to disturbance and parameter variations in the system. The efficacy of RBFN as a controller and identification tool is verified by performing the simulation study, and the results obtained reveal the superior performance of RBFN over MLFFNN in both identification and control aspects for one-link and two-link robotic manipulators.

It is essential to implement effective compliance control measures to ensure the safe and reliable operation of robots and automated systems. In this paper, zone barrier Lyapunov function is presented to the practical nonlinear system with adaptive fuzzy neural network control design. Output-constraint is considered by zone barrier Lyapunov function (zBLF), which is designed to enforce safety constraints and offer flexibility when the system states remain within a safe region of operation, even in the presence of nonlinear uncertainties. Adaptive fuzzy neural control is adopted to approximate the nonlinear uncertainties for practical stability analysis and design, which aims to adjust the control parameters online according to the system's behaviour. The fuzzy control based on zBLF symmetric output constraint is further extended to the asymmetric case. The output state converges to the trajectory and moves freely in a region around the origin, and then it is demonstrated the developed design method guarantees the boundedness of the states of a closed-loop system. Moreover, the system's residual set for each condition can be identified respectively. The simulation results on euler-lagrange system are to validate the effectiveness of the proposed scheme.

In the paper, based on a combination of the robust control approach and an adaptive control technique, a design procedure of a new robust adaptive controller is proposed for power system load-frequency control with system parametric uncertainties. The system parametric uncertainties are obtained by changing parameters by 30% to 50% simultaneously from their typical values. In the design of the proposed controller, only the bounds of system parameters are required. The motivation of combining the robust control with an adaptive control is to use the robust control approach to deal with the small parametric uncertainties and to use the adaptive control for large parametric uncertainties so that stability of the overall system and good performance can both be achieved for all admissible uncertainties. The simulation results demonstrate that for the example system the proposed load-frequency controller can achieve good performance even in the presence of the generation rate constraint (GRC).

Since the computed torque or inverse dynamics control techniques are based on a good understanding of the controlled system dynamics and even its environment; the objection for the real-time application is unrealizable for the unknown systems. To tackle this problem, this study developed a robust adaptive fuzzy sliding-mode control system based on the H/sup /spl infin// tracking design technique. The developed control system is comprised of a fuzzy controller and a robust controller. The fuzzy controller is utilized to approximate an ideal controller by the tuning algorithms based on the Lyapunov sense, and the robust controller is designed to achieve H/sup /spl infin// robust tracking performance. To investigate the effectiveness of the developed control system, it is applied to a wing rock motion control system. Simulation results demonstrate that the proposed control system can achieve favorable tracking performances with unknown system dynamics.

This paper proposes a robust adaptive fuzzy control scheme for the uncertain nonlinear time-delay systems preceded by an unknown dead-zone. It is well known that time-delay and dead-zone characteristics are frequently encountered in various engineering systems. The dead-zone phenomena may occur in various components of control systems including sensors, amplifiers and actuators, especially in pneumatic valves, in electric servo motors and in hydraulic components. On the other hand, the time-delay phenomena are quite commonly found in chemical process, hydraulic systems and pneumatic systems. In most practice motion systems, the dead-zone characteristics are usually poorly known and may vary with time. Therefore, by using a description of a dead-zone and exploring the properties of this dead-zone model intuitively and mathematically, a robust adaptive fuzzy control method is presented without constructing the dead-zone inverse. According to some adaptive laws, the unknown nonlinear functions of the plant are approximated by the fuzzy logic system. Based on Lyapunov stability theorem, the proposed robust adaptive fuzzy control scheme can guarantee the robust stability of the whole closed-loop nonlinear time-delay system with an unknown dead-zone in the actuator and obtain good tracking performance as well. Finally, an example and simulation results are provided to show the efficiency of the proposed control method.