Yesim Oniz

A Grey System Modeling Approach for Sliding-Mode Control of Antilock Braking System

In this paper, a grey sliding-mode controller is proposed to regulate the wheel slip, depending on the vehicle forward velocity. The proposed controller anticipates the upcoming values of wheel slip and takes the necessary action to keep the wheel slip at the desired value. The performance of the control algorithm as applied to a quarter vehicle is evaluated through simulations and experimental studies that include sudden changes in road conditions. It is observed that the proposed controller is capable of achieving faster convergence and better noise response than the conventional approaches. It is concluded that the use of grey system theory, which has certain prediction capabilities, can be a viable alternative approach when the conventional control methods cannot meet the desired performance specifications.

A Dynamic Method to Forecast the Wheel Slip for Antilock Braking System and Its Experimental Evaluation

The control of an antilock braking system (ABS) is a difficult problem due to its strongly nonlinear and uncertain characteristics. To overcome this difficulty, the integration of gray-system theory and sliding-mode control is proposed in this paper. This way, the prediction capabilities of the former and the robustness of the latter are combined to regulate optimal wheel slip depending on the vehicle forward velocity. The design approach described is novel, considering that a point, rather than a line, is used as the sliding control surface. The control algorithm is derived and subsequently tested on a quarter vehicle model. Encouraged by the simulation results indicating the ability to overcome the stated difficulties with fast convergence, experimental results are carried out on a laboratory setup. The results presented indicate the potential of the approach in handling difficult real-time control problems.

Neuro-fuzzy control of antilock braking system using sliding mode incremental learning algorithm

A neuro-fuzzy adaptive control approach for nonlinear dynamical systems, coupled with unknown dynamics, modeling errors, and various sorts of disturbances, is proposed and used to design a wheel slip regulating controller. The implemented control structure consists of a conventional controller and a neuro-fuzzy network-based feedback controller. The former is provided both to guarantee global asymptotic stability in compact space and as an inverse reference model of the response of the controlled system. Its output is used as an error signal by an incremental learning algorithm to update the parameters of the neuro-fuzzy controller. In this way the latter is able to gradually replace the conventional controller from the control of the system. The proposed new learning algorithm makes direct use of the variable structure systems theory and establishes a sliding motion in terms of the neuro-fuzzy controller parameters, leading the learning error toward zero. In the simulations and in the experimental studies, it has been tested on the control of antilock breaking system model and the analytical claims have been justified under the existence of uncertainty and large nonzero initial errors.

A servo system control with time-varying and nonlinear load conditions using type-2 TSK fuzzy neural system

Abstract: A type-2 Takagi-Sugeno-Kang fuzzy neural system is proposed and its parameter update rules are derived using fuzzy clustering and gradient learning algorithms. The proposed type-2 fuzzy neural system is used for the control and the identification of a real-time servo system. Fuzzy c-means clustering algorithm is used to determine the initial places of the membership functions to ensure that the gradient descent algorithm used afterwards converges in a shorter time. A number of different load conditions including nonlinear and time-varying ones are used to investigate the performance of the proposed control algorithm. The control structure has the ability to regulate the servo system with reduced oscillations when compared with the results of its type-1 counterpart around the set point signal in the presence of load disturbances.

Simulated and experimental study of antilock braking system using grey sliding mode control

Antilock braking system (ABS) exhibits strongly nonlinear and uncertain characteristics. To overcome these difficulties, robust control methods should be employed. In this paper, a grey sliding mode controller is proposed to track the reference wheel slip. The concept of grey system theory, which has a certain prediction capability, offers an alternative approach to conventional control methods. The proposed controller anticipates the upcoming values of wheel slip, and takes the necessary action to keep wheel slip at the desired value. The control algorithm is applied to a quarter vehicle model, and it is verified through simulations indicating fast convergence and good performance of the designed controller. Simulated results are validated on real time applications using a laboratory experimental setup.

Control of a direct drive robot using fuzzy spiking neural networks with variable structure systems-based learning algorithm

In this work, a sliding mode theory based supervised training algorithm that implements fuzzy reasoning on a spiking neural network has been developed and tested on the trajectory control problem of a two-degrees-of-freedom direct drive robotic manipulator. To describe the generation of a new spike train from the incoming spike trains Spike Response Model has been utilized and the Lyapunov stability method has been adopted in the derivation of the update rules for the neurocontroller parameters. The results of the real-time experiments indicate that stable online tuning and fast learning speed are the prominent characteristics of the proposed algorithm.

Neuro-Fuzzy Control of Antilock Braking System Using Variable-Structure-Systems-Based Learning Algorithm

A neuro-fuzzy adaptive control approach for nonlinear systems with model uncertainties is proposed. The implemented control scheme consists of a proportional plus derivative controller that is provided both to guarantee global asymptotic stability in compact space and as an inverse reference model of the response of the controlled system. Its output is used as an error signal by an on-line learning algorithm to update the parameters of a neuro-fuzzy feedback controller. The latter is able to gradually replace the conventional controller from the control of the system. The proposed learning algorithm makes direct use of the variable structure systems theory and establishes a sliding motion in terms of the neuro-fuzzy controller parameters. An integrating term has been additionally applied to the overall control signal of the two controllers and the performance of the control scheme has been tested on the wheel slip control problem within an antilock breaking system model. The analytical claims have been justified under the existence of model uncertainties and large initial errors.

Variable-structure-systems based approach for online learning of spiking neural networks and its experimental evaluation

Abstract Raising the level of biological realism by utilizing the timing of individual spikes, spiking neural networks (SNNs) are considered to be the third generation of artificial neural networks. In this work, a novel variable-structure-systems based approach for online learning of SNN is developed and tested on the identification and speed control of a real-time servo system. In this approach, neurocontroller parameters are used to define a time-varying sliding surface to lead the control error signal to zero. To prove the convergence property of the developed algorithm, the Lyapunov stability method is utilized. The results of the real-time experiments on the laboratory servo system for a number of different load conditions including nonlinear and time-varying ones indicate that the control structure exhibits a highly robust behavior against disturbances and sudden changes in the command signal.

Spiking neural networks for identification and control of dynamic plants

In this paper a Spiking Neural Networks (SNN)-based model is developed for identification and control of dynamic plants. Spike Response Model (SRM) has been employed to design the model. The learning of the parameters of SNN is carried out using a gradient algorithm. For its use for identification and control purposes, a coding is applied to convert real numbers into spikes. The SNN structure is tested for the identification and control of the dynamic plants commonly used in the literature. It has been found that the proposed structure results in a good performance despite its smaller parameter space.

An adaptive neuro-fuzzy architecture for intelligent control of a servo system and its experimental evaluation

In this paper the development of an adaptive neuro-fuzzy architecture for the speed control of a servo system with nonlinear load is presented. The synthesis of the structure is described and a learning algorithm for the neuro-fuzzy control system is derived. The supervised learning algorithm is used to train the unknown coefficients of the system, and then the fuzzy rules of the neuro-fuzzy system are generated. A number of simulation studies are carried out, and the results are compared with those obtained with a PI controller tuned using desired time response characteristics. These and the experimental studies presented show that the neuro-fuzzy control system has a better control performance than the conventional PI controller.