Erkan Kayacan

Adaptive Neuro-Fuzzy Control of a Spherical Rolling Robot Using Sliding-Mode-Control-Theory-Based Online Learning Algorithm

As a model is only an abstraction of the real system, unmodeled dynamics, parameter variations, and disturbances can result in poor performance of a conventional controller based on this model. In such cases, a conventional controller cannot remain well tuned. This paper presents the control of a spherical rolling robot by using an adaptive neuro-fuzzy controller in combination with a sliding-mode control (SMC)-theory-based learning algorithm. The proposed control structure consists of a neuro-fuzzy network and a conventional controller which is used to guarantee the asymptotic stability of the system in a compact space. The parameter updating rules of the neuro-fuzzy system using SMC theory are derived, and the stability of the learning is proven using a Lyapunov function. The simulation results show that the control scheme with the proposed SMC-theory-based learning algorithm is able to not only eliminate the steady-state error but also improve the transient response performance of the spherical rolling robot without knowing its dynamic equations.

Modeling and control of a spherical rolling robot: A decoupled dynamics approach

This paper presents the results of a study on the dynamical modeling, analysis, and control of a spherical rolling robot. The rolling mechanism consists of a 2-DOF pendulum located inside a spherical shell with freedom to rotate about the transverse and longitudinal axis. The kinematics of the model has been investigated through the classical methods with rotation matrices. Dynamic modeling of the system is based on the Euler-Lagrange formalism. Nonholonomic and highly nonlinear equations of motion have then been decomposed into two simpler subsystems through the decoupled dynamics approach. A feedback linearization loop with fuzzy controllers has been designed for the control of the decoupled dynamics. Rolling of the controlled mechanism over linear and curvilinear trajectories has been simulated by using the proposed decoupled dynamical model and feedback controllers. Analysis of radius of curvature over curvilinear trajectories has also been investigated.

Identification of Nonlinear Dynamic Systems Using Type-2 Fuzzy Neural Networks—A Novel Learning Algorithm and a Comparative Study

In order to achieve faster and more robust convergence (particularly under noisy working environments), a sliding-mode-theory-based learning algorithm has been proposed to tune both the premise and consequent parts of type-2 fuzzy neural networks (FNNs) in this paper. Different from recent studies, where sliding-mode-control-theory-based rules are proposed for only the consequent part of the network, the developed algorithm applies fully-sliding-mode parameter update rules for both the premise and consequent parts of type-2 FNNs. In addition, the responsible parameter for sharing the contributions of the lower and upper parts of the type-2 fuzzy membership functions is also tuned. Moreover, the learning rate of the network is updated during the online training. The stability of the proposed learning algorithm has been proved by using an appropriate Lyapunov function. Several comparisons have been realized and shown that the proposed algorithm has faster convergence speed than the existing methods such as gradient-based and swarm-intelligence-based methods. Moreover, the proposed learning algorithm has a closed form, and it is easier to implement than the other existing methods.

Towards Agrobots: Trajectory Control of an Autonomous Tractor Using Type-2 Fuzzy Logic Controllers

Provision of some autonomous functions to an agricultural vehicle would lighten the job of the operator but in doing so, the accuracy should not be lost to still obtain an optimal yield. Autonomous navigation of an agricultural vehicle involves the control of different dynamic subsystems, such as the yaw angle dynamics and the longitudinal speed dynamics. In this study, a proportional-integral-derivative controller is used to control the longitudinal velocity of the tractor. For the control of the yaw angle dynamics, a proportional-derivative controller works in parallel with a type-2 fuzzy neural network. In such an arrangement, the former ensures the stability of the related subsystem, while the latter learns the system dynamics and becomes the leading controller. In this way, instead of modeling the interactions between the subsystems prior to the design of a model-based control, we develop a control algorithm which learns the interactions online from the measured feedback error. In addition to the control of the stated subsystems, a kinematic controller is needed to correct the errors in both the x- and the y- axis for the trajectory tracking problem of the tractor. To demonstrate the real-time abilities of the proposed control scheme, an autonomous tractor is equipped with the use of reasonably priced sensors and actuators. Experimental results show the efficacy and the efficiency of the proposed learning algorithm.

Learning in Centralized Nonlinear Model Predictive Control: Application to an Autonomous Tractor-Trailer System

One of the most critical tasks in tractor operation is the accurate steering during field operations, e.g., accurate trajectory following during mechanical weeding or spraying, to avoid damaging the crop or planting when there is no crop yet. To automate the trajectory following problem of an autonomous tractor-trailer system and also increase its steering accuracy, a nonlinear model predictive control approach has been proposed in this paper. For the state and parameter estimation, moving horizon estimation has been chosen since it considers the state and the parameter estimation within the same problem and also constraints both on inputs and states can be incorporated. The experimental results show the accuracy and the efficiency of the proposed control scheme in which the mean values of the Euclidean error for the tractor and the trailer, respectively, are 6.44 and 3.61 cm for a straight line trajectory and 49.78 and 41.52 cm for a curved line trajectory.

Robust Tube-Based Decentralized Nonlinear Model Predictive Control of an Autonomous Tractor-Trailer System

This paper addresses the trajectory tracking problem of an autonomous tractor-trailer system by using a decentralized control approach. A fully decentralized model predictive controller is designed in which interactions between subsystems are neglected and assumed to be perturbations to each other. In order to have a robust design, a tube-based approach is proposed to handle the differences between the nominal model and real system. Nonlinear moving horizon estimation is used for the state and parameter estimation after each new measurement, and the estimated values are fed to robust tube-based decentralized nonlinear model predictive controller. The proposed control scheme is capable of driving the tractor-trailer system to any desired trajectory ensuring high control accuracy and robustness against neglected subsystem interactions and environmental disturbances. The experimental results show an accurate trajectory tracking performance on a bumpy grass field.

Robust Trajectory Tracking Error Model-Based Predictive Control for Unmanned Ground Vehicles

This paper proposes a new robust trajectory tracking error-based control approach for unmanned ground vehicles. A trajectory tracking error-based model is used to design a linear model predictive controller and its control action is combined with feedforward and robust control actions. The experimental results show that the proposed control structure is capable to let a tractor-trailer system track both linear and curvilinear target trajectories with low tracking error.

Towards agrobots

Modelling the yaw dynamics of an autonomous tractor.The NLS frequency domain identification is used to obtain the model parameters.An MPC controller for the yaw dynamics.A kinematic controller for trajectory tracking. More efficient agricultural machinery is needed as agricultural areas become more limited and energy and labor costs increase. To increase their efficiency, trajectory tracking problem of an autonomous tractor, as an agricultural production machine, has been investigated in this study. As a widely used model-based approach, model predictive control is preferred in this paper to control the yaw dynamics of the tractor which can deal with the constraints on the states and the actuators in a system. The yaw dynamics is identified by using nonlinear least squares frequency domain system identification. The speed is controlled by a proportional-integral-derivative controller and a kinematic trajectory controller is used to calculate the desired speed and the desired yaw rate signals for the subsystems in order to minimize the tracking errors in both the longitudinal and transversal directions. The experimental results show the accuracy and the efficiency of the proposed control scheme in which the euclidean error is below 40cm for time-based straight line trajectories and 60cm for time-based curved line trajectories, respectively.

Intelligent control of a tractor-implement system using type-2 fuzzy neural networks

Automatic guidance of agricultural vehicles would lighten the job of the operator, while accuracy is needed to obtain an optimal yield. Accurately navigating a tractor consists of controlling different dynamic subsystems (steering and speed). Instead of modeling the subsystem interaction prior to model-based control, we have developed a control algorithm which learns the interactions on-line from the measured feedback error. In this approach, a PD controller is working in parallel with a type-2 fuzzy neural network. While the former ensures the stability of the related subsystem, the latter learns the system dynamics and becomes the leading controller. In this study, two combinations of a PD controller with a type-2 fuzzy neural network are implemented: one for the yaw dynamics and one for the traction dynamics. The interactions between these subsystems are thus not taken into account explicitly, but considered as disturbances to be handled by the subsystem controllers. A novel sliding mode control theory-based learning algorithm is used to train the type-2 fuzzy neural networks, and the convergence of the parameters is shown by using a Lyapunov function.

Velocity Control of a Spherical Rolling Robot Using a Grey-PID Type Fuzzy Controller with an Adaptive Step Size

This paper proposes a grey-PID type fuzzy controller (GPIDFC) with an adaptive step size to control the velocity of a spherical rolling robot characterized by a highly nonlinear system model. The proposed control structure consists of a grey predictor and a PID type fuzzy controller (PIDFC). Another fuzzy controller is proposed to tune the step size of the grey predictor. The simulation results show that the PIDFC coupled to a grey predictor with an adaptive step size is able to control the spherical rolling robot with a better transient response, e.g. lower overshoot, when compared to a conventional PIDFC.