Ahmad B. Rad

Indirect adaptive fuzzy sliding mode control: Part I: fuzzy switching

In this paper, by using the concept of sliding mode control design and Lyapunov synthesis approach, we propose an indirect adaptive fuzzy sliding mode control (IAFSMC) scheme for a class of nonlinear systems. In contrast to the existing sliding mode fuzzy control system designs, where the sliding mode control law is directly substituted by a fuzzy controller, in our approach both the equivalent control term and switching-type control term in the sliding mode control law are approximated by fuzzy systems, respectively. An on-line adaptive tuning algorithm for the consequent parameters in the fuzzy rules is also designed. The sliding mode-based design procedure ensures the stability and robustness of the proposed IAFSMC. We prove that the proposed adaptive scheme can achieve asymptotically stable tracking of a reference input with a guarantee of the bounded system signals. The chattering phenomena in the sliding mode control is attenuated and the steady error is also alleviated. Simulation studies have shown that the presented adaptive design of fuzzy sliding mode controller performs very well in the presence of an unknown disturbance.

Adaptive fuzzy sliding mode control with chattering elimination for nonlinear SISO systems

Abstract This work presents an adaptive fuzzy sliding mode controller (AFSMC) that combines a robust proportional integral control law for use in designing single-input single-output (SISO) nonlinear systems with uncertainties and external disturbances. The fuzzy logic system is used to approximate the unknown system function and the AFSMC algorithm is designed by used of sliding mode control techniques. Based on the Lyapunov theory, the proportional integral control law is designed to eliminate the chattering action of the control signal. The simplicity of the proposed scheme facilitates its implementation and the overall control scheme guarantees the global asymptotic stability in the Lyapunov sense if all the signals involved are uniformly bounded. Simulation studies have shown that the proposed controller shows superior tracking performance.

Indirect adaptive fuzzy sliding mode control: Part II: parameter projection and supervisory control

The objective of this paper is to show that the error dynamics of the Lyapunov synthesis is similar to the sliding surface of sliding mode control (SMC). We compare and evaluate the role and performance of sliding surface, boundary layer and Lyapunov synthesis. Finally, the indirect adaptive fuzzy sliding mode control (IAFSMC) is equipped with state boundedness supervisory controller and parameter projection. Simulation studies illustrate the application of the proposed method.

Identification and control of continuous-time nonlinear systems via dynamic neural networks

In this paper, we present an algorithm for the online identification and adaptive control of a class of continuous-time nonlinear systems via dynamic neural networks. The plant considered is an unknown multi-input/multi-output continuous-time higher order nonlinear system. The control scheme includes two parts: a dynamic neural network is employed to perform system identification and a controller based on the proposed dynamic neural network is developed to track a reference trajectory. Stability analysis for the identification and the tracking errors is performed by means of Lyapunov stability criterion. Finally, we illustrate the effectiveness of these methods by computer simulations of the Duffing chaotic system and one-link rigid robot manipulator. The simulation results demonstrate that the model-based dynamic neural network control scheme is appropriate for control of unknown continuous-time nonlinear systems with output disturbance noise.

Online identification of continuous-time systems with unknown time delay

In this note, we present a recursive algorithm for online identification of systems with unknown time delay. The proposed algorithm can be interpreted as an approximate nonlinear least-squares, corresponding to modified normalized or un-normalized least-squares when the normalizing factor /spl gamma/>0 or /spl gamma/=0, respectively. Both algorithms are essentially extensions to general least-squares methodology. Simulation studies demonstrate the characteristics and performance of these algorithms.

Indirect adaptive tracking control of a nonholonomic mobile robot via neural networks

This paper presents the design and implementation of a novel adaptive trajectory tracking controller for a nonholonomic wheeled mobile robot (WMR) with unknown parameters and uncertain dynamics. The learning ability of neural networks is used to design a robust adaptive backstepping controller that does not require the knowledge of the robot dynamics. The kinematic controller gains are tuned on-line to minimize the velocity error and improve the trajectory tracking characteristics. The performance of the proposed control algorithm is verified and compared with the classical backstepping controller through simulation and experiments on a commercial mobile robot platform.

Intelligent system for process supervision and fault diagnosis in dynamic physical systems

In recent years, the increasing complexity of process plants and other engineered systems has extended the scope of interest in control engineering, which was previously focused on the development of controllers for specified performance criteria such as stability and precision. Modern industrial systems require a higher demand of system reliability, safety, and low-cost operation, which in turn call for sophisticated and elegant fault-detection and isolation algorithms. This paper develops an intelligent supervisory coordinator (ISC) for process supervision and fault diagnosis in dynamic physical systems. A qualitative bond graph modeling scheme, integrating artificial-intelligence techniques with control engineering, is used to construct the knowledge base of the ISC. A supervisor provided by the ISC utilizes the knowledge in the knowledge base to classify various system behaviors, coordinates different control tasks (e.g., fault diagnosis), and communicates system states to human operators. The ISC provides a robust semiautonomous system to assist human operators in managing dynamic physical systems. The proposed ISC has been successfully applied to supervise a laboratory-scale servo-tank liquid process rig.

Fuzzy adaptive internal model control

In this paper, a fuzzy adaptive internal model controller (FAIMC) for open-loop stable plants is presented. The control scheme consists of two parts: a fuzzy dynamic model and a model-based fuzzy controller. A fuzzy dynamic model, which serves as the internal model of the FAIMC is identified online by using the input and output measurement of the plant. Based on the identified fuzzy model, the fuzzy controller is designed to minimize an H/sub 2/ performance objective. This FAIMC scheme has been successfully applied to control the flow rate in a laboratory-scale process control unit from Bytronic. The experimental results demonstrate that this class of control scheme is appropriate for control of time-varying stable plants with time delay. The control system is also shown to possess satisfactory robust performance.

An approach to tune fuzzy controllers based on reinforcement learning for autonomous vehicle control

In this paper, we suggest a new approach for tuning parameters of fuzzy controllers based on reinforcement learning. The architecture of the proposed approach is comprised of a Q estimator network (QEN) and a Takagi-Sugeno-type fuzzy inference system (TSK-FIS). Unlike other fuzzy Q-learning approaches that select an optimal action based on finite discrete actions, the proposed controller obtains the control output directly from TSK-FIS. With the proposed architecture, the learning algorithms for all the parameters of the QEN and the FIS are developed based on the temporal-difference (TD) methods as well as the gradient-descent algorithm. The performance of the proposed design technique is illustrated by simulation studies of a vehicle longitudinal-control system.

An Intelligent Longitudinal Controller for Application in Semiautonomous Vehicles

We present a neuro-fuzzy controller for intelligent cruise control of semiautonomous vehicles. This paper addresses the problem of longitudinal control that aims at regulating the speed of the controlled vehicle in order to maintain constant time headway with respect to the vehicle in front. A fuzzy radial basis function network (FRBFN) longitudinal controller is designed to incorporate the merits of fuzzy logics as well as neural networks. The FRBFN is prestructured, and its parameters are configured such that they are associated with their physical meaning. The parameters of the output layer are learned online via gradient algorithm. An attractive feature of the proposed method is that it does not require the training data and the vehicle longitudinal dynamic model. Simulation results on a vehicle theoretical model are provided to demonstrate the effectiveness of this controller. In order to investigate the proposed control algorithms in real-life situations, a small-scaled vehicle with computer and sensors onboard is developed. Experimental results of a conventional PID controller and the FRBFN controller are provided for comparison.