Andon V. Topalov

Sliding Mode Neuro-Adaptive Control of Electric Drives

An innovative variable-structure-systems-based approach for online training of neural network (NN) controllers as applied to the speed control of electric drives is presented. The proposed learning algorithm establishes an inner sliding motion in terms of the controller parameters, leading the command error towards zero. The outer sliding motion concerns the controlled electric drive, the state tracking error vector of which is simultaneously forced towards the origin of the phase space. The equivalence between the two sliding motions is demonstrated. In order to evaluate the performance of the proposed control scheme and its practical feasibility in industrial settings, experimental tests have been carried out with electric motor drives. Crucial problems such as adaptability, computational costs, and robustness are discussed. Experimental results illustrate that the proposed NN-based speed controller possesses a remarkable learning capability to control electric drives, virtually without requiring a priori knowledge of the plant dynamics and laborious startup procedures

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.

Fuzzy-net control of non-holonomic mobile robot using evolutionary feedback-error-learning

Standard approaches to non-holonomic control design deal only with the kinematic steering system, ignoring the actual vehicle dynamics. Recently a stable control algorithm that considers the complete vehicle dynamics has been developed using back stepping kinematics into dynamics. According to this approach the dynamics of the actual cart has to be completely known. However, exact knowledge of the mobile robot dynamics in many cases is unattainable. A solution to this problem requires implementation of robust-adaptive control methods combining conventional approaches with new learning approaches in order to achieve good performance. This paper deals with the control problem of a non-holonomic wheeled mobile robot in the tracking mode. A locomotion control structure based on the integration of a kinematic controller and an adaptive fuzzy-net torque controller is presented. An evolutionary feedback-error-learning method for automatic elicitation of knowledge in the form of fuzzy if-then rules is developed. The proposed adaptive fuzzy-net torque controller can deal with unmodeled bounded disturbances and/or unstructured unmodeled dynamics of the vehicle. The control architecture developed is simulated and its effect on the trajectory tracking performance of a non-holonomic mobile robot cart is evaluated.

Comparative Results on Stabilization of the Quad-rotor Rotorcraft Using Bounded Feedback Controllers

In the recent years autonomous flying vehicles are being increasingly used in both civil and military areas. With the advancement of the technology it has become possible to test efficiently and cost-effectively different autonomous flight control concepts and design variations using small-scale aerial vehicles. In this paper the stabilization problem of the quad-rotor rotorcraft using bounded feedback controllers is investigated. Five different types of nonlinear feedback laws with saturation elements, previously proposed for global control of systems with multiple integrators, are applied and tested to control the quad-rotor rotorcraft roll and pitch angles. The results obtained from autonomous flight simulations and real time experiments with the Draganflyer V Ti four-rotor mini-rotorcraft are analyzed with respect to the structural simplicity of the control schemes and the transient performance of the closed-loop system.

Sliding mode incremental learning algorithm for interval type-2 Takagi–Sugeno–Kang fuzzy neural networks

Type-2 fuzzy logic systems are an area of growing interest over the last years. The ability to model uncertainties and to perform under noisy conditions in a better way than type-1 fuzzy logic systems increases their applicability. A new stable on-line learning algorithm for interval type-2 Takagi–Sugeno–Kang (TSK) fuzzy neural networks is proposed in this paper. Differently from the other recently proposed variable structure system theory-based on-line learning approaches for the type-2 TSK fuzzy neural nets, where the adopted consequent part of the fuzzy rules consists solely of a constant, the developed algorithm applies the complete structure of the Takagi–Sugeno type fuzzy if–then rule base (i.e. first order instead of zero order output function is implemented). In addition it is able to adapt the existing relation between the lower and the upper membership functions of the type-2 fuzzy systems. This allows managing of non-uniform uncertainties. Simulation results from the identification of a nonlinear system with uncertainties and a non-bounded-input bounded-output nonlinear plant with added output noise have demonstrated the better performance of the proposed algorithm in comparison with the previously reported in the literature sliding mode on-line learning algorithms for both type-1 and type-2 fuzzy neural structures.

Adaptive Control of Electric Drives Using Sliding-Mode Learning Neural Networks

New sliding mode control theory-based method for on-line learning in multilayer neural controllers as applied to the speed control of electric drives is presented. The proposed algorithm establishes an inner sliding motion in terms of the controller parameters, leading the command error towards zero. The outer sliding motion concerns the controlled electric drive, the state tracking error vector of which is simultaneously forced towards the origin of the phase space. The equivalence between the two sliding motions is demonstrated. In order to evaluate the performance of the proposed control scheme and its practical feasibility in industrial settings, experimental tests have been carried out with electric motor drives.

Sliding mode algorithm for online learning in analog multilayer feedforward neural networks

A new dynamical sliding mode control algorithm is proposed for robust adaptive learning in analog multilayer feedforward networks with a scalar output. These type neural structures are widely used for modeling, identification and control of nonlinear dynamical systems. The zero level set of the learning error variable is considered as a sliding surface in the space of network learning parameters. The convergence of the algorithm is established and conditions are given. Its effectiveness is shown when applied to on-line learning of nonmonotonic function using a two-layered feedforward neural network.

Intelligent Sensor-Based Navigation and Control of Mobile Robot in a Partially Known Environment

Abstract Navigation and collision avoidance are major areas of research in mobile robotics that involve varying degrees of uncertainty. In this paper, a new approach is proposed for navigation and control of a wheeled mobile robot in a partially known environment. A collision-free path is calculated using an efficient neural motion planner. An additional fuzzy logic based navigation strategy is developed to adjust the moving information in the cases when the mobile robot encounters unexpected obstacles. The output of the navigation level is transformed into a time indexed data sequence which is fed into a tracking controller that takes into account the complete dynamics of the mobile base. The locomotion control structure is based on the integration of a kinematic controller and an adaptive fuzzy-net torque controller.

Intelligent Motion Planning and Control for a Simulated Mobile Robot

Abstract A complete motion planning and control procedure for mobile robot is presented. A collision-free path is calculated using a neural-net motion planner. The output of the planner is then transformed into a time indexed data sequence which is fed into a tracking controller that takes into account the complete dynamics of the mobile base. A locomotion control structure based on the integration of a kinematic controller and an adaptive fuzzy-net torque controller is proposed. An evolutionary feedback-error-learning method for automatic elicitation of knowledge in the form of fuzzy if-then rules is developed. The results of the simulations show the effectiveness of the proposed approach.

Neural net tracking control of a mobile platform in robotized wireless sensor networks

In the recent years sensor networks have emerged as an effective tool for monitoring environments that are large in size and have complex topology. They have been successfully applied to solve various problems such as monitoring of different environmental indicators, detection of floods and fires, temperature control in office buildings, to collect information about the health status of hospitalized patients, to monitor the activity of some species and many others. Based-on the wireless communications the unification of the sensor modules can be implemented as a wireless sensor network (WSN). A newly observed trend is the inclusion of mobile robots into the WSN structure. The present paper describes a work-in-progress aiming to build laboratory prototype of a robotized hybrid WSN. The latter can be used for various distributed control experiments over the network structure and its efficiency. A robotized wireless sensor node has been designed and its functionality has been experimentally tested using an advanced trajectory tracking control algorithm. The proposed robotic node consists itself of a mobile robot platform integrated with a wireless sensor node. The latter has been implemented with a Tiva C Series TM4C1294NCPDT LaunchPad evaluation board equipped with TI Sensor Hub and WiFi Booster Pack. The mobile robot platform is based on the iRobot Create additionally upgraded with an on-board control system built in with gumstix verdex pro XL6P embedded microprocessor.