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This paper presents two intelligent adaptive controllers, called self-balancing and speed controllers, for self-balancing and motion control, respectively, of an electric unicycle using fuzzy basis function networks (FBFN), which are employed to approximate model uncertainties and unknown friction between the wheel and the terrain surface. Both controllers are established based on the linearized model of the vehicle whose model uncertainties and parameter variations are caused by different riders and terrain. An adaptive backstepping controller together with online learning FBFN and sensing information of the rider's body inclination then is presented to achieve self-balancing motion control. By adding an electronic throttle as the input device of speed commands, a decoupling sliding-mode controller with online learning FBFN is proposed to accomplish self-balancing and speed control. The performance and merit of the two proposed control methods are exemplified by conducting four simulations and three experiments on a laboratory-built electric unicycle.

This paper presents techniques for adaptive steering control of an electric unicycle with a steering turning mechanism, in order to achieve self-balancing and speed tracking simultaneously. A state feedback control method using the linear quadratic regulator approach is first presented to simultaneously achieve self-balancing and speed tracking, and then an adaptive friction compensating term is proposed to overcome frictions caused by different terrain surfaces. The performance and merit of the proposed control method are well exemplified by conducting two simulations and five experiments on a laboratory-built electric unicycle.

This article presents a robust tracking controller for an uncertain mobile manipulator system. A rigid robotic arm is mounted on a wheeled mobile platform whose motion is subject to nonholonomic constraints. The sliding mode control (SMC) method is associated with the fuzzy neural network (FNN) to constitute a robust control scheme to cope with three types of system uncertainties; namely, external disturbances, modelling errors, and strong couplings in between the mobile platform and the onboard arm subsystems. All parameter adjustment rules for the proposed controller are derived from the Lyapunov theory such that the tracking error dynamics and the FNN weighting updates are ensured to be stable with uniform ultimate boundedness (UUB).

This article presents an adaptive H ∞ nonlinear velocity control for a linear DC brushless motor. A simplified model of this motor with friction is briefly recalled. The friction dynamics is described by the Lu Gre model and the online tuning radial basis function neural network (RBFNN) is used to parameterise the nonlinear friction function and un-modelled errors. An adaptive nonlinear H ∞ control method is then proposed to achieve velocity tracking, by assuming that the upper bounds of the ripple force, the changeable load and the nonlinear friction can be learned by the RBFNN. The closed-loop system is proven to be uniformly bounded using the Lyapunov stability theory. The feasibility and the efficacy of the proposed control are exemplified by conducting two velocity tracking experiments.

This paper presents adaptive robust regulation methods for self-balancing and yaw motion of a two-wheeled human transportation vehicle (HTV) with varying payload and system uncertainties. The proposed regulators are aimed at providing consistent driving performance for the HTV with system uncertainties and parameter variations caused by different drivers. By decomposing the overall system into the yaw motion subsystems and the wheeled inverted pendulum, two proposed adaptive robust regulators are synthesized to achieve self-balancing and yaw motion control. Numerical simulations and experimental results on different terrains show that the proposed adaptive robust controllers are capable of achieving satisfactory control actions to steer the vehicle.

This paper presents an adaptive polar-space motion controller for trajectory tracking and stabilization of a three-wheeled, embedded omnidirectional mobile robot with parameter variations and uncertainties caused by friction, slip and payloads. With the derived dynamic model in polar coordinates, an adaptive motion controller is synthesized via the adaptive backstepping approach. This proposed polar-space robust adaptive motion controller was implemented into an embedded processor using a field-programmable gate array (FPGA) chip. Furthermore, the embedded adaptive motion controller works with a reusable user IP (Intellectual Property) core library and an embedded real-time operating system (RTOS) in the same chip to steer the mobile robot to track the desired trajectory by using hardware/software co-design technique and SoPC (system-on-a-programmable-chip) technology. Simulation results are conducted to show the merit of the proposed polar-space control method in comparison with a conventional proportional-integral (PI) feedback controller and a non-adaptive polar-space kinematic controller. Finally, the effectiveness and performance of the proposed embedded adaptive motion controller are exemplified by conducting several experiments on steering an embedded omnidirectional mobile robot.

This paper presents a nonlinear adaptive aggressive controller to provide the small scale helicopter with full authority of a variety of flight conditions. Adaptive backstepping technique is employed to systematically synthesize the proposed controller with the online parameter adaptation rule to the vehicle mass variations and with the recurrent neural network (RNN) approximation to the coupling effect between the force and moment controls. This single and systematic design methodology is shown to achieve the semi-global ultimate boundedness of the closed-loop helicopter dynamics and accommodate the aggressive control of flight maneuvers from hovering to trajectory tracking. The high-fidelity and well-validated nonlinear model of a small scale helicopter incorporating with unmodeled dynamics and measurement uncertainties is adopted in the numerical simulations. The performance and merits of the proposed controller are exemplified by conducting three simulation scenarios including the slalom maneuver described in the ADS33.

This paper presents an adaptive control using radial-basis-function neural networks (RBFNNs) for a two-wheeled self-balancing scooter. A mechatronic system structure of the scooter driven by two dc motors is briefly described, and its mathematical modeling incorporating two frictions between the wheels and the motion surface is derived. By decomposing the overall system into two subsystems (yaw motion and mobile inverted pendulum), one proposes two adaptive controllers using RBFNN to achieve self-balancing and yaw control. The performance and merit of the proposed adaptive controllers are exemplified by conducting several simulations and experiments on a two-wheeled self-balancing scooter.

This paper presents an embedded adaptive robust controller for trajectory tracking and stabilization of an omnidirectional mobile platform with parameter variations and uncertainties caused by friction and slip. Based on a dynamic model of the platform, the adaptive controller to achieve point stabilization, trajectory tracking, and path following is synthesized via the adaptive backstepping approach. This robust adaptive controller is then implemented into a high-performance field-programmable gate array chip using hardware/software codesign technique and system-on-a-programmable-chip design concept with a reusable user intellectual property core library. Furthermore, a soft-core processor and a real-time operating system are embedded into the same chip for realizing the control law to steer the mobile platform. Simulation results are conducted to show the effectiveness and merit of the proposed control method in comparison with a conventional proportional-integral feedback controller. The performance and applicability of the proposed embedded adaptive controller are exemplified by conducting several experiments on an autonomous omnidirectional mobile robot.

This paper presents a stochastic model reference predictive control (SMRPC) approach to achieving accurate temperature control for an industrial oil-cooling process, which is experimentally modeled as a simple first-order system model with given long time delay. Based on this model, the stochastic model reference predictive controller with control weighting and integral action is derived based on the minimization of an expected generalized predictive control (GPC) performance criteria. A real-time adaptive SMRPC algorithm is proposed and then implemented into a stand-alone digital signal processor (DSP). Experimental results show that the proposed control method is capable of giving accurate and satisfactory control performance under set-point changes, fixed load and load changes.

An adaptive predictive control with recurrent neural network prediction for industrial processes is presented. The neural predictive control law with integral action is derived based on the minimization of a modified predictive performance criterion. The stability and steady-state performance of the closed-loop control system are well studied. Numerical simulations reveal that the proposed control gives satisfactory tracking and disturbance rejection performance for two illustrative nonlinear systems with time-delay. Experimental results for temperature control of a variable-frequency oil-cooling process show the efficacy of the proposed method for industrial processes with set-points changes and load disturbances.

This paper presents an adaptive robust control method for trajectory tracking and path following of an omni-directional wheeled mobile platform with actuators' uncertainties. The polar-space kinematic model of the platform with three independent driving omnidirectional wheels equally spaced at 120 from one another is briefly introduced, and the dynamic models of the three uncertain servomotors mounted on the driving wheels are also described. With the platform's kinematic model and the motors' dynamic model associated two unknown parameters, the adaptive robust controller is synthesized via the integral backstepping approach. Computer simulations and experimental results are conducted to show the effectiveness and merits of the proposed control method in comparison with a conventional PI feedback control method.

This paper presents an direct adaptive control for an uncertain electric seatless unicycle using wavelet fuzzy cerebella model articulation controller (WFCMAC). The WFCMAC is proposed to approximate the equivalent control part and uncertainties. A nonlinear backstepping sliding-mode controller with on-line adaptive laws is presented to accomplish adaptive robust self-balancing and speed control of the seatless electric unicycle based on the rider's body inclination and the speed of the unicycle. Simulations results indicate that the performance and merit of the proposed method are well illustrated by comparing to two existing controllers.

This paper develops a novel control methodology for voltage regulation and implementation of a buck DC‐DC converter using a digital signal processor (DSP). Such a converter is modeled as a linear averaged state‐space system model with an adjustable load. An adaptive backstepping voltage regulator is presented based on the measurements of an output voltage and a capacitor current. An approximate averaged circuit model is derived in order to show that the fast and stable mode of the capacitor current can be ignored and the buck DC‐DC converter can be well approximated by the averaged circuit model. With the approximate averaged model, an adaptive backstepping control with integral action is proposed to regulate a stand‐alone buck DC‐DC converter. This proposed control method has been verified by computer simulation and implemented utilizing a stand‐alone digital signal processor (DSP) TMS320C542 from Texas Instruments. Experimental results show that the proposed control method is capable of giving ...

In order to achieve good handling (or flying) qualities in longitudinal axis for all flight conditions, an adaptive backstepping flight control law is presented. With the backstepping technique, an adaptive controller is synthesized for the purpose of accomplishing desired responses under a wide range of flight envelope. The specification assignments for flying qualities are used for selecting essential parameters of the proposed adaptive controller for all flight conditions. In comparison with other conventional control methods, the proposed adaptive control law is much simpler and easier to construct and implement. Simulation results show that the proposed method is capable of giving desired closed-loop dynamic performance and robustness against uncertainties within the subsonic and supersonic flight conditions

This paper develops methodologies for an adaptive robust path tracking control of a nonholonomic Wheeled Mobile Robot (WMR) with nonlinear driving characteristics and unknown dynamic parameters. To solve the problem of position/orientation tracking control of WMR, a novel robust kinematics control law is developed to steer the vehicle to asymptotically follow the desired trajectories. To compensate for dynamic effects associated with the dynamic models, an adaptive backstepping path tracking control law with robustness is designed to ensure asymptotic path tracking for the vehicle with unknown dynamic parameters and changeable time-varying payload. Simulation results are included to illustrate feasibility and effectiveness of the proposed control laws.

This paper develops methodologies and techniques for the design, analysis, and implementation of a model reference adaptive predictive temperature controller for a variable-frequency oil-cooling machine, suited for cooling high-speed machine tools. The oil-cooling process is modeled experimentally as a first-order system model with a time delay and its system parameters are identified using the recursive least-square method. Based on this model, a model reference adaptive predictive controller is proposed for achieving set-point tracking and robustness. A real-time model reference adaptive predictive control algorithm is then presented and implemented utilizing a stand-alone digital signal processor TMS320F243 from Texas Instruments Incorporated. The experimental results show that the proposed control method is proven capable of giving satisfactory performance under set-point changes, fixed loads, and load changes.

This paper presents an adaptive decoupling temperature control for an extrusion barrel in a plastic injection molding process. After establishing a stochastic polynomial matrix model of the system, a corresponding decoupling system representation was then developed. The decoupling control design was derived based on the minimization of a generalized predictive performance criterion. The set-point tracking, disturbance rejection, and robustness capabilities of the proposed method can be improved by appropriate adjustments to the tuning parameters in the criterion function. A real-time control algorithm, including the recursive least-squares method, is proposed which was implemented using a digital signal processor TMS320C31 from Texas Instruments. Through the experimental results, the proposed method has been shown to be powerful under set-point changes, load disturbances, and significant plant uncertainties. The proposed control law is shown to be less computational and more effective than other well-known multivariable control strategies, and more powerful than single-loop temperature-zone control policies.

This paper presents a direct adaptive motion controller using fuzzy wavelet neural networks (FWNN) for speed control of an electric two-wheeled robotic vehicle (ETWRV) with unknown parameters and uncertainties. With the decomposition of the overall system into two subsystems: yaw motion control and mobile inverted pendulum, two direct adaptive FWNN motion controllers are respectively proposed to achieve station keeping, speed following and yaw motion control. Asymptotic stabilities of the two controllers with their FWNN weighting updating rules are derived via the Lyapunov stability theory. Simulation results indicate that the proposed controllers are capable of providing satisfactory control actions to steer the vehicle.

This paper presents an intelligent adaptive steering control using backstepping, aggregated hierarchical sliding-mode control approach and fuzzy basis function networks (FBFN) for seatless electric unicycles. The backstepping hierarchical sliding-mode control approach is used to simultaneously achieve self-balancing and speed control, while the FBFN is employed to on-line learn uncertainties caused by different riders and unknown frictions between the wheel and the terrain surfaces. The performance and merit of the proposed method are well exemplified by conducting two simulations on a laboratory-built electric unicycle. Experimental results show consistent steering performance of the proposed controller for distinct riders and terrain surfaces.

This paper presents an adaptive robust motion control using fuzzy wavelet neural networks (FWNN) for a electric two-wheeled robotic vehicles (ETWRV). A mechatronic system structure driven by two DC motors is briefly described, and its nonlinear mathematical modeling incorporating the friction between the wheels and the motion surface is derived. With the decomposition of the overall system into two subsystems: yaw control and inverted pendulum, two intelligent adaptive FWNN controllers are proposed to achieve self-balancing, speed tracking and yaw motion control. Simulation results indicate that the proposed controllers are capable of providing appropriate control actions to steer the vehicle in desired manners.

This paper presents a direct adaptive RIT2FNN-based control for motion control of a ball robot with a four-motor inverse mouse-ball driving mechanism actuated by four independent brushless motors simultaneously. A dynamic model of the robot with viscous and Coulomb frictions is derived using Lagrangian mechanics. With the model, a direct adaptive RIT2FNN-based control with the backstepping slidingmode methodology is proposed to accomplish robust self-balancing and trajectory tracking of the robot in the presence of mass variations, viscous and Coulomb frictions with unknown parameters and uncertainties. The proposed motion controller is proven asymptotically stable using Lyapunov stability theory. Computer simulations are conducted for illustration of the effectiveness of the proposed control method.

This paper presents an intelligent adaptive steering control using linear quadratic regulation (LQR) approach and fuzzy cerebella model articulation control (CMAC) method for an electrical unicycle. The fuzzy CMAC is employed to on-line learn unknown frictions between the wheel and the terrain surfaces. The LQR approach is used to design a state feedback controller, in order to simultaneously achieve self-balancing and velocity control for the unicycle with different riders. The performance and merit of the proposed method are well exemplified by conducting simulations on a laboratory-built electric unicycle.

This paper presents an intelligent adaptive trajectory tracking controller using fuzzy basis function networks (FBFN) for an autonomous small-scale helicopter. With the on-line FBFN approximation to the vehicle mass and the coupling effect between the force and the moments, the intelligent adaptive controller is systematically synthesized using backstepping technique. This controller is shown to achieve the semi-global ultimate boundedness of the closed-loop helicopter dynamics and accommodate agile flight maneuvers in trajectory tracking. The effectiveness and merit of the proposed method are exemplified by performing one nonlinear simulation and by performance comparison with a well-known controller.

This paper presents an intelligent adaptive backstepping sliding-mode motion controller using fuzzy basis function networks (FBFN) method for trajectory tracking of a self-balancing two-wheeled robot (SBTWR) with parameter variations. A decoupling method is proposed to decouple the robot's dynamic model such that the tracking controller can be synthesized using backstepping and sliding-mode control in both kinematic and dynamic levels. The FBFN is employed to on-line learn the uncertain parts of the tracking controller, thus achieving adaptive capability. Simulations results indicate that the proposed adaptive tracking controller is capable of providing satisfactory trajectory tracking performance.

This paper presents a direct adaptive motion control using fuzzy basis-function networks (FBFN) for a self-balancing two-wheeled transporter (SBTWT) with unknown parameters and uncertainties. With the decomposition of the overall system into two subsystems: yaw motion control and mobile inverted pendulum, two direct adaptive FBFN motion controllers are respectively proposed to achieve posture maintenance, speed following and yaw motion control. Asymptotic stabilities of the two controllers with their FBFN weighting updating rules are derived via the Lyapunov stability theory. Simulation results indicate that the proposed controllers are capable of providing satisfactory control actions to steer the vehicle

This paper presents an adaptive backstepping sliding-mode motion controller using fuzzy basis function networks (FBFN) method for trajectory tracking of a self-balancing two-wheeled robot (SBTWR) with parameter variations. A decoupling method is proposed to decouple the robot's dynamic model such that the tracking controller can be synthesized using backstepping and sliding-mode control in both kinematic and dynamic levels. The FBFN is employed to on-line learn the uncertain parts of the tracking controller, thus achieving adaptive capability. Simulations results indicate that the proposed adaptive tracking controller is capable of providing satisfactory trajectory tracking performance.

This paper presents an intelligent adaptive motion control using fuzzy basis-function networks (FBFN) for a self-balancing two-wheeled transporter (SBTWT). A mechatronic system structure driven by two DC motors is briefly described, and its nonlinear mathematical modeling incorporating the friction between the wheels and the motion surface is derived. With the decomposition of the overall system into two subsystems: yaw control and inverted pendulum, two intelligent adaptive FBFN controllers are proposed to achieve self-balancing, speed tracking and yaw motion control. Simulation results indicate that the proposed controllers are capable of providing appropriate control actions to steer the vehicle in desired manners.

This paper develops an adaptive dynamic motion controller for position control and trajectory tracking of the omnidirectional mobile robot equipped with four independent omnidirectional wheels equally spaced at 90 degrees from one to another. Such a controller is synthesized by backstepping and will be is proven globally asymptotically stable via the Lyapunov stability theory. The controller is particularly adequate for the robot while navigating over its working environment at any speeds. The effectiveness and merit of the proposed control method is exemplified by conducting several simulations.

This paper presents a stochastic adaptive model reference predictive control (SMRPC) approach to achieving accurate temperature control for a polyethylene-terephthalate (PET) blow molding machine, which is experimentally modeled as a simple second-order system model with given long time delay. Based on this model, the adaptive model reference predictive controller with control weighting is derived based on the minimization of an expected generalized predictive control (GPC) performance criteria. A real-time adaptive SMRPC algorithm is proposed. Computer simulations show that the proposed control method is capable of giving accurate and satisfactory control performance under set-point changes and exogenous disturbances.

This paper presents adaptive sliding-mode control methods for self-balancing and yaw rate control of a dynamically two-wheeled human transportation vehicle (HTV) with mass variations and system uncertainties. The proposed controllers aim to provide consistent driving performance for system uncertainties and different drivers whose weights cause parameter variations of the HTV. By decomposing the overall system into the yaw subsystems and the self-balancing subsystems with parameters variations with respect to different riders, two adaptive sliding mode controls are proposed to achieve self-balancing and yaw control. Numerical simulations and experimental results on different terrains show that the proposed adaptive sliding mode controllers are capable of achieving satisfactory control actions to steer the vehicle.

Passivity and its applications to mechatronic systems have been widely investigated and continuously improved for many years. This paper presents methodologies for adaptive global stabilization and disturbance attenuation of a class of weekly minimum-phase nonlinear uncertain systems, which can be either of nonlinear affine or of relative degree 1. Adaptive passivity-based control (PBC) methods are developed to achieve adaptive stabilization and disturbance attenuation for the systems with some unknown but constant parameters. Sufficient conditions and state regulations are established for showing the closed-loop system to be globally stable. The merit and performance of the proposed methods are exemplified by conducting several simulations on a 2-linked manipulator. The developed techniques may be of interest to professionals working in the field of nonlinear passive control and its applications.

This paper presents an adaptive nonlinear control using radial-basis-function neural network (RBFNN) for an electric unicycle. A mechatronic system structure of the unicycle is constructed and its simplified mathematical modeling is then established by using Newtonian mechanics and incorporating the frictions between the wheel and the terrain surface. An adaptive nonlinear control together with RBFNN is developed based on adaptive backstepping technique, in order to simultaneously achieve self-balancing and forward motion. Simulation results are conducted to illustrate feasibility and effectiveness of the proposed control method. The performance and merit of the proposed method are well exemplified by real riding test.

This paper presents a planned navigation control architecture of a self-balancing two-wheeled platform for autonomous service robots. Based on the linearized mathematical modeling incorporating the frictions between the wheels and the motion surface, a kinematical trajectory tracker and two adaptive controllers are designed to track speed and yaw rate commands. A human-aware trajectory generator is used to provide desired trajectories between humans and the robot. Experimental results indicate that the proposed planned navigation control architecture is capable of providing appropriate control actions to satisfactorily achieve trajectory tracking and obstacle-avoidance.

This paper presents methodologies and techniques for multi-sensory fuzzy hybrid navigation of an active mobile robot assistant for the elderly people in known, cluttered and dynamic indoor environments. For self-localization, the geometric and topological representations are combined together to maintain the advantages of them and counteract their disadvantages. By using RFID system, the estimation of position hypothesis for similar regions in a known environment can be reduced. The global optimal path composed of via points is determined by using Dijkstra's dynamic programming approach. Fuzzy adaptive speed control law is designed to make the robot adapt to various users' speeds. Safe fuzzy hybrid navigation is achieved by fusing adaptive speed, goal-seeking and obstacle- avoidance from the sensing data of laser scanner and ultrasonic rangers. In addition, Experimental results are conducted to show the effectiveness and merits of the proposed methods.

This paper presents a predictive control based on recurrent neural network (RNN) for a class of nonlinear systems and investigates its application to temperature control of plastic injection molding processes. The RNN is used as a model identifier for approximating the nonlinear discrete-time systems and the multivariable predictive control based on the RNN is derived from a generalized predictive performance criterion. The adaptive learning rates of the RNN model and the controller are investigated via the discrete Lyapunov stability theorem, which are respectively used to guarantee the convergences of both the RNN model and the predictive controller. Finally, numerical simulations and experimental results are provided to demonstrate the effectiveness of the proposed control strategy under setpoint changes and bounded disturbances.

The paper presents an adaptive integral position controller usingRBF (Radial Basis Function) neural networks (NNs) for a brushless DC linear motor. By assuming that the upper bounds of the nonlinear friction and force ripple, an RBF NN is used for approximating the friction, the force ripple and the load; an adaptive backstepping control with integral action is then proposed to achieve position tracking of the linear motor. The parameter adjustment rules for the overall controller are derived via the Lyapunov stability theory. Based on the LaSalle-Yoshizawa lemma, the proposed controller is proven asymptotically stable. Experimental results are conducted to show the efficacy and usefulness of the proposed control method.

This paper develops a methodology for trajectory tracking control of a nonholonomic wheeled mobile manipulator with parameter uncertainties and external load changes. Based on backstepping technique, the proposed control law consists of two levels: kinematics and dynamic levels. First, the auxiliary kinematic velocity control laws for the mobile platform and the onboard arm are separately proposed. Second, a robust tracking control system based on hybrid sliding-mode fuzzy neural networks (HSMFNN) is presented to ensure the velocity tracking ability under dynamic uncertainties. To achieve the goal, a fuzzy neural network (FNN) controller is developed to act as an equivalent control law in the sliding-mode control, a robust controller is designed to incorporate the system dynamics into the sliding surface for guaranteeing the asymptotical stability, and the proportional controller is designed to improve the transient performance for randomly initializing FNN. All the adaptive learning algorithms of the proposed controller are derived from the Lyapunov stability theory so that the close-loop system tracking ability can be guaranteed no matter the uncertainties occur or not. Simulation results illustrate the feasibility as well as usefulness of the proposed control strategy in comparison with other strategies

This paper presents a recurrent neural network based predictive control for a class of nonlinear discrete time systems. The neural predictive control law is developed from the minimization of a generalized predictive performance criterion. A real time adaptive control algorithm, including a neural predictor and a neural predictive controller, is proposed; the adaptive learning rates for both the neural predictor and controller are determined based on Lyapunov stability theory. Simulation results reveal that the proposed control gives satisfactory tracking and disturbance rejection performance for two illustrative nonlinear systems. Experimental results for a variable frequency oil-cooling control process are performed which have shown effectiveness of the proposed method under the conditions of setpoint and load changes.

This paper develops a methodology for trajectory tracking control of a nonholonomic wheeled mobile manipulator with uncertainties and external load changes. The proposed control law consists of two levels: kinematics and dynamic levels. First, the auxiliary kinematic velocity control laws for the mobile platform and the onboard manipulator are separately proposed via backstepping. Then, a hybrid robust tracking control system is presented to ensure the velocity tracking ability in spite of the uncertainties. To achieve the goal, a neural network controller is developed to mimic an equivalent control law in the sliding-mode control, a robust controller is designed to incorporate the system dynamics into the sliding surface for guaranteeing the asymptotical stability, and the proportional controller is designed to improve the transient performance for randomly initializing neural network. All the adaptive learning algorithms for sliding-mode neural networks (SMNN) are derived from the Lyapunov stability theory so that the system tracking ability can be guaranteed in the close-loop system no matter the uncertainties occur or not. Simulation results illustrate the feasibility as well as efficacy of the proposed control strategy in comparison with the backstepping method.

This paper develops methodology and technique for design, analysis and PC-based implementation of an adaptive backstepping position control system for a linear DC brushless motor. A simplified model of the linear DC brushless motor is introduced. With this model, an adaptive backstepping control law with integral action is proposed to achieve position tracking control, assuming that the upper bounds of the ripple force and nonlinear friction are known. Based on the Lyapunov theorem and Barbalat Lemma, the closed-loop controlled system is proven to be asymptotically stable. Both computer simulations and experimental results demonstrate the feasibility and efficacy of the proposed scheme for position control of a linear DC brushless motor.

This paper develops the control methodologies under the specific assumption of the data source environments, for trajectory tracking of a wheeled mobile robot (WMR) with vision data. To solve the problem of trajectory tracking control of WMR, one novel kinematics control law is presented to steer the vehicle to asymptotically follow the desired trajectories. To compensate for dynamic effects associated with the dynamic models, an adaptive nonlinear trajectory tracking control law is presented to ensure asymptotic trajectory tracking for the vehicle. The experimental system is composed of a wireless RS232 modem, a digital signal processor (DSP), a wheeled mobile robot, and a vision system with a host computer. A computation-effective and high-performance controller using the digital signal processor is constructed for executing the developed sophisticated trajectory tracking laws. Simulation and experimental results are included to illustrate the feasibility and effectiveness of the proposed control method.

This paper develops a novel methodology for voltage regulation and equal current distribution of parallel-buck DC-DC converters. Such converters are modeled as linear averaged state-space system models with adjustable loads. Two measurements, such as output voltage and inductor current, are used to accomplish the design of an adaptive backstepping sliding mode controller for the parallel-buck DC-DC converters. Based on the singular perturbation method, a reduced order model is derived in order to show that the effect of equivalent series resistance (ESR) of the capacitor can be ignored and the parallel converters can be well approximated by the reduced order model. With the reduced order model, an adaptive sliding mode control utilizing backstepping approach is proposed to control the parallel-buck DC-DC converters. This proposed control method has been verified by computer simulations, and has also been proven capable of giving satisfactory voltage regulation performance under input voltage variations and load changes.

This paper develops novel methodologies and techniques for voltage-regulation and implementation of a buck DC-DC converter. This type of converter is modeled as a linear averaged state-space system model with an adjustable load. The output voltage and the inductor current are employed to achieve design of an adaptive backstepping sliding mode controller for the buck DC-DC converter. Using singular perturbation method, a reduced order model is derived in order to show that the effect of equivalent series resistance (ESR) of the capacitor can be ignored and the buck DC-DC converter can be well approximated by the reduced order model. Based on the reduced order model, adaptive sliding mode control based on backstepping approach is proposed to control a stand-alone buck DC-DC converter. This proposed control method has been verified by computer simulation and implemented utilizing a stand-alone digital signal processor (DSP) TMS320C542 from Texas Instruments. Experimental results show that the proposed control method is proven to be capable of giving satisfactory voltage regulation performance under input voltage variations and load changes.

This work presents a neural predictive control for a class of single-input single-output nonlinear systems with known time-delay. A neural predictive control law is developed based on the minimization of a generalized predictive performance criterion. A real-time adaptive control algorithm, including a neural predictor and a neural predictive controller, is proposed, and the local stability of the closed-loop control system is investigated as well. Simulation results reveals that the proposed control gives satisfactory tracking and disturbance rejection performance for two illustrative time-delay nonlinear systems. Experimental results for a variable-frequency oil-cooling control process are performed which have shown effectiveness of the proposed method under the conditions of setpoint and load changes.

This paper develops an adaptive backstepping flight control law in order to bestow good handling (or flying) qualities in longitudinal axis for all fight conditions. Using the backstepping procedure, we synthesize a new type of controller in order to accomplish deadbeat responses under a wide range of flight envelope. Adaptive parameters tuning of the backstepping controller is employed to achieve the deadbeat performance for all flight conditions. In comparison with convectional control methods, the proposed control law is much simple and easy to construct and implement. Simulation results demonstrate that the proposed method is capable of giving desired closed-loop deadbeat pitch rate responses within the subsonic and supersonic flight conditions.

This paper develops methodologies and techniques for design, analysis and implementation of a direct self-tuning model following predictive temperature controller for a variable-frequency oil-cooling machine, used for cooling high-speed machine tools. The oil- cooling process is modeled experimentally as a first-order system model with a time-delay and its system parameters are identified using the recursive least-squares method. Based on this model, a novel adaptive predictive controller is proposed for achieving set-point tracking and disturbance rejection. A real-time direct self-tuning model following predictive control algorithm is then presented and implemented utilizing a standalone digital signal processor (DSP) TMS320F243 from Texas Instruments. The experimental results show that the proposed control method is proven capable of giving satisfactory performance under set-point changes, fixed loads and load changes.

This work presents an adaptive predictive control with neural prediction for a class of single-input single- output nonlinear systems with known time-delay. The well-known linear dynamic modeling approach together with the neural modeling method is employed to approximate these nonlinear systems. The predictive control law with integral action is derived based on the minimization of a generalized predictive performance criterion. A real-time adaptive control algorithm, including a recursive least-squares estimator and a proposed neural predictor, is successfully applied to achieve the control performance specifications. Stability and properties of the closed-loop control systems are investigated as well Simulation results reveal that the proposed control gives satisfactory tracking and disturbance rejection performance for two illustrative time-delay nonlinear systems. Experimental results for a variable-frequency oil-cooling control process are performed which have shown effectiveness of the proposed method under the conditions of set-points and load changes.

This paper develops methodologies and techniques for the design, analysis and implementation of a model reference adaptive predictive temperature controller for a variable frequency oil-cooling machine, suited for cooling high-speed machine tools. The oil-cooling process is modeled experimentally as a first-order system model with a time-delay, and its system parameters are identified using the recursive least-square method. Based on this model, a model-reference adaptive predictive controller is proposed for achieving set-point tracking and disturbance rejection. A real-time model-reference adaptive predictive control algorithm is then presented and implemented utilizing a standalone digital signal processor TMS320F243 from Texas Instruments. The experimental results show that the proposed control method is proven capable of giving satisfactory performance under set-point changes, fixed loads and load changes.