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This paper presents a method for real-time collision-free path planning that uses a new navigation function for environments in which humans and robots cooperate or coexist, which guarantees the sa...

This paper presents a novel real-time collision-free speed alteration strategy using a danger index and an elite real-coded genetic algorithm (ERGA) for environments in which humans and robots coexist or cooperate, in order to guarantee the safety of an operator who works with a collaborative robot. A danger index based on ellipsoid modeling of the operator and robot describes the degree of safety during human-robot interactions. The ERGA and a penalty function are used to solve the constrained nonlinear optimization problem to change the handling speed of the robot. Comparative simulation results show the superiority of the proposed method by comparing to two existing methods. The applicability of the proposed method is verified using two experiments involving a 6-DoF industrial manipulator with an EtherCAT network protocol, an RGB-D sensor and a real-time operation system.

This paper presents a leader-follower consensus formation control method using backstepping sliding-mode control for a group of uncertain, networked heterogeneous nonholonomic wheeled mobile robots (NWMRs), in order to achieve formation keeping and trajectory tracking, respectively. The NWMRs are composed of two kind of wheeled mobile robots, including nonholonomic self-balancing two-wheeled mobile robots (NSBTWMRs) and nonholonomic wheeled differential-driving mobile Robots (NWDDMRs). The dynamic behavior of each NWMR is governed by its second-order dynamic model, and the networked multi-NWMR system is modeled by a directed graph. By using the Lyapunov stability and sliding-mode control theories, one intelligent adaptive, distributed consensus control approach is respectively presented to carry out formation keeping and trajectory tracking in the presence of uncertainties, in order to not only keep all the robots in formation, but also let them track their desired trajectories and maintain them in formation, respectively. Simulations are conducted to show the effectiveness and merits of the proposed methods.

This paper presents an intelligent predictive PI temperature control using particle swarm optimization - real coded genetic algorithm (PSO-RGA) for transfer molding modules in semiconductor die packaging machines. The system parameters of the transfer molding process are obtained by using the well-known reaction curve method. The best control parameters of the PI controller are offline tuned by using PSO-RGA algorithm. The set-point tracking, disturbance rejection, and robustness capabilities of the proposed method are well exemplified by conducting simulations and experiments on a real transfer mold process.

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 an intelligent adaptive trajectory tracking controller using fuzzy basis function networks FBFN for an autonomous small-scale helicopter. The FNFN is used online to learn the vehicle mass and the coupling effect between the force and the moments, and the intelligent adaptive controller then is synthesized systematically using a backstepping technique. This controller is designed to accomplish the ultimate boundedness of the closed-loop helicopter dynamics and accommodate agile flight maneuvers. Two nonlinear simulations on hovering and trajectory tracking are conducted to show the effectiveness and merit of the proposed controller, which is also shown to be superior by performance comparison with a well-known neural-network controller.

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 paper presents an distributed consensus formation control using recurrent fuzzy wavelet neural networks (RFWNN) for a group of heterogeneous mobile three-Swedish-wheeled omnidirectional robots (TSWORs) with uncertainties. The dynamic behavior of each TSWOR is modelled by a three-input-three-output second-order state equation and the multirobot system is modeled by a directed graph. By online learning the system uncertainties using RFWNN and using the Lyapunov stability theory, an intelligent adaptive distributed consensus formation control approach is presented to carry out formation control in the presence of heterogeneity and uncertainties. Simulations are conducted to show the effectiveness and merits of the proposed method.

This paper presents a decentralized cooperative transportation control method with obstacle avoidance using fuzzy wavelet neural networks (FWNN) and consensus algorithm for a group of mobile Mecanum-wheeled omnidirectional robots (MWORs) with uncertainties, in order to get together to move a large payload. The dynamic behavior of each uncertain MWOR is modelled by a reduced three-input-three-output second-order system model and the uncertain multi-MWOR system is modeled by graph theory. By online learning the system uncertainties using FWNN and using the Lyapunov stability theory, an intelligent adaptive, cooperative consensus-based control approach is presented to carry out transportation control with uncertainties. An obstacle avoidance method is proposed to modify the generating trajectory of the virtual leader, in order to avoid any collisions between the robots and the environment. Two simulations are conducted to show the effectiveness of the proposed method.

This paper presents a decentralized consensus formation control using fuzzy wavelet neural networks (FWNN) for a group of homogenous, mobile four-Mecanum-wheeled omnidirectional vehicles (MWOVs) with uncertainties. The dynamic behavior of each MWOV is remodeled by a three-input-three-output second-order system model and the multi-MWOV system is modeled by graph theory. By online learning the system uncertainties using FWNN and using backstepping techniques, an intelligent adaptive, cooperative consensus-based control approach is presented to carry out formation control in the presence of uncertainties. Simulations are conducted to show the effectiveness and merits of the proposed method.

SUMMARY This paper presents an intelligent control approach that incorporates sliding mode control (SMC) and fuzzy neural network (FNN) into the implementation of back-stepping control for a path tracking problem of a dual-arm wheeled mobile manipulator subject to dynamic uncertainties and nonholonomic constraints. By using the back-stepping technique, the system equations are reformulated into two levels: the kinematic level and the dynamic level. A sliding manifold is constructed by considering the disturbance free kinematic level equations only. With all the system uncertainties concentrated in the dynamic level, an FNN controller associated with a switching type of control law is employed to enforce sliding mode on the prescribed manifold. All parameter adjustment rules for the proposed controller are derived from the Lyapunov theory such that uniform ultimate boundedness for both the tracking error and the FNN weighting updates is ensured. A simulation study, which compares different control design approaches, is included to illustrate the promise of the proposed SMC–FNN method. Copyright © 2012 John Wiley & Sons, Ltd.

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 paper presents a parallel elite genetic algorithm (PEGA) and its application to global path planning for autonomous mobile robots navigating in structured environments. This PEGA, consisting of two parallel EGAs along with a migration operator, takes advantages of maintaining better population diversity, inhibiting premature convergence, and keeping parallelism in comparison with conventional GAs. This initial feasible path generated from the PEGA planner is then smoothed using the cubic B-spline technique, in order to construct a near-optimal collision-free continuous path. Both global path planner and smoother are implemented in one field-programmable gate array chip utilizing the system-on-a-programmable-chip technology and the pipelined hardware implementation scheme, thus significantly expediting computation speed. Simulations and experimental results are conducted to show the merit of the proposed PEGA path planner and smoother for global path planning of autonomous mobile robots.

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 a neural-network-based predictive control (NPC) method for a class of discrete-time multi-input multi-output (MIMO) systems. A discrete-time mathematical model using a recurrent neural network (RNN) is constructed and a learning algorithm adopting an adaptive learning rate (ALR) approach is employed to identify the unknown parameters in the recurrent neural network model (RNNM). The NPC controller is derived based on a modified predictive performance criterion, and its convergence is guaranteed by adopting an optimal algorithm with an adaptive optimal rate (AOR) approach. The stability analysis of the overall MIMO control system is well proven by the Lyapunov stability theory. A real-time control algorithm is proposed which has been implemented using a digital signal processor, TMS320C31fromTexasInstruments.Twoexamples,includingthecontrolofa MIMO nonlinear system and the control of a plastic injection molding process, are used to demonstrate the effectiveness of the proposed strategy. Results from both numerical simulations and experiments show that the proposed method is capable of controlling MIMO systems with satisfactory tracking performance under setpoint and load changes.

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 a backstepping control method using radial-basis-function neural network (RBFNN) for improving trajectory tracking performance of a robotic helicopter. Many well-known nonlinear controllers for robotic helicopters have been constructed based on the approximate dynamic model in which the coupling effect is neglected; their qualitative behavior must be further analyzed to ensure that the unmodeled dynamics do not destroy the stability of the closed-loop system. In order to improve the controller design process, the proposed controller is developed based on the complete dynamic model of robotic helicopters by using an RBFNN function approximation to the neglected dynamic uncertainties, and then proving that all the trajectory tracking error variables are globally ultimately bounded and converge to a neighborhood of the origin. The merits of the proposal controller are exemplified by four numerical simulations, showing that the proposed controller outperforms a well-known controller in (J. Robust Nonlinear Control 2004; 14(12):1035–1059). Copyright © 2009 John Wiley & Sons, Ltd.

This paper presents an auto-landing controller for glide-slope tracking and the flare maneuver via adaptive backstepping design and describes a flight path command generator for indirect altitude control in order to provide precise altitude trajectories for auto-landing of unmanned aerial vehicles (UAVs). Using the adaptive backstepping procedure to synthesize a glide-slope tracking and flare maneuver control law is being used differently from designing the guidance and control loops separately in autopilot. An adaptive controller is proposed to control aircraft from glide-slope to flare by following the flight path angle command for indirect altitude control via elevator and maintaining the constant airspeed control via throttle. Simulation results demonstrate that the adaptive auto-landing controller is capable of effectively guiding the UAV along the flight path angle command under the presence of the wind turbulence. Copyright © 2008 John Wiley & Sons, Ltd.

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.

The paper presents a design methodology for predictive control of industrial processes via recurrent neural networks (RNNs). A discrete‐time mathematical model using RNN is established and a multi‐step neural predictor is then constructed. With the predictor, a neural predictive control (NPC) law is developed from the generalized predictive performance criterion. Both the RNN model and the NPC controller are proven convergent based on Lyapunov stability theory. Two examples of a nonlinear process system and a physical variable‐frequency oil‐cooling machine are used to demonstrate the effectiveness of the proposed control method. Through the experimental results, the proposed method has been shown capable of giving satisfactory performance for industrial processes under setpoint changes, external disturbances and load changes.

This paper presents pragmatic techniques for mechatronic design and injection speed control of an ultra high-speed plastic injection molding machine. Practical rules are proposed to select specifications of key mechatronic components in the hydraulic servo system, in order to efficiently construct an industry-level machine. With reasonable assumptions, a mathematical model of the injection speed control system is established and open-loop experimental data are then employed to validate the system model. By the model, a gain-scheduling PI controller and a fuzzy PI controller are presented, compared and then implemented into a digital signal processor (DSP) using standard C programming techniques. Experimental results are conducted to show that the two proposed controllers are capable of achieving satisfactory speed tracking performance. These developed techniques may provide useful references for engineers and practitioners attempting to design pragmatic, low-cost but high-performance ultra high-injection speed controllers.

Control systems education often needs to design interesting hands-on exercises that keep students interested in the control theory presented in lectures. These exercises include system modeling, system analyses, controller syntheses, implementation, experimentation, and performance evaluation of a control system. This paper presents an interesting pedagogical tool, a self-balancing human transportation vehicle (HTV), for the teaching of feedback control concepts in undergraduate electrical, mechatronic, and mechanical engineering environments. Such a pedagogical tool can be easily and inexpensively constructed using low-tech commercial components and feedback control approaches. The effectiveness and performance of the proposed HTV system are examined by conducting several experiments on three different terrains. An education process, together with a pedagogical method, is presented to show how the developed HTV can be incorporated into the laboratory course. To increase students' hands-on experience and keep them interested in learning feedback control, this study also investigated how the enrolled students responded to this new pedagogical tool. This education method along with the HTV system is shown to be significantly effective in helping students to understand feedback control theory and practices, and also to result in more motivated and active learning.

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.

In this paper, a robust tracking controller is proposed for the trajectory tracking problem of a dual-arm wheeled mobile manipulator subject to some modeling uncertainties and external disturbances. Based on backstepping techniques, the design procedure is divided into two levels. In the kinematic level, the auxiliary velocity commands for each subsystem are first presented. A sliding-mode equivalent controller, composed of neural network control, robust scheme and proportional control, is constructed in the dynamic level to deal with the dynamic effect. To deal with inadequate modeling and parameter uncertainties, the neural network controller is used to mimic the sliding-mode equivalent control law; the robust controller is designed to compensate for the approximation error and to incorporate the system dynamics into the sliding manifold. The proportional controller is added to improve the system's transient performance, which may be degraded by the neural network's random initialization. All the parameter adjustment rules for the proposed controller are derived from the Lyapunov stability theory and e-modification such that uniform ultimate boundedness (UUB) can be assured. A comparative simulation study with different controllers is included to illustrate the effectiveness of the proposed method.

This paper presents a two degree‐of‐freedom control method for achieving constant continuous positive airway pressure (CPAP) of an obstructive sleep apnea (OSA) treatment system. After constructing a closed‐loop DC brushless motor drive together with a blower and an airway module, we develop a pressure control system for achieving CPAP control. A mathematical model of the overall open‐loop system is established based on the input‐output data and its system parameters are sequentially determined using the recursive least‐squares (RLS) method. A two degree‐of‐freedom (DOF) controller, including a feedforward controller and a feedback proportional‐integral controller, is proposed to maintain and follow the desired pressure setpoints. Through experimental results, the proposed control method is shown useful and effective in achieving airway pressure tracking and regulation.

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 adaptive backstepping control method for trajectory tracking and stabilization of an omnidirectional wheeled mobile robot with parameter variations and uncertainties caused by friction and slip. The dynamic model of the robot with three independent driving omnidirectional wheels equally spaced at 120 degrees from one another is briefly introduced. With the dynamic model, the adaptive controller to achieve both trajectory tracking and stabilization is synthesized via adaptive backstepping approach. Experimental results are conducted to show the merit of the proposed control method.

The paper proposes a novel method for enhanced fusion adaptive fuzzy control in the two-wheeled balancing six degrees of freedom robotic arm. The two-wheeled mobile robot system and six degrees of freedom robotic arm are integrated into the mobile robot system. Due to the motion of the robot arm, the stability issue becomes more complex. This study employs the adaptive fuzzy control to provide suitable controller for the mobile robot system. In addition, a dynamic learning rate can effectively improve the learning performance. In order to show the superiority of the proposed controller, this paper compares our proposed method with other control methods as proportional-derivative (PD) controller, fuzzy controller, adaptive fuzzy controller, PD fuzzy controller, and adaptive PD controller. The experiment results clearly demonstrate that the proposed control method has much faster convergent speed and is very well performed.

This paper presents an adaptive intelligent steering controller using backstepping sliding-mode control and recurrent wavelet fuzzy cerebellar model articulation controller (RWFCMAC) and for an uncertain ball-riding human transporter in presence of significant system uncertainties. The proposed controller operates at two independent modes: self-balancing and station keeping. The self-balancing mode is used to balance by following the rider's two-dimensional inclination angles, while the station-keeping mode is aimed to permit the rider to keep the vehicle at a target position. The RWFCMAC is designed to online learn the uncertainties caused by riders' weights and different parameters. The superior performance and merit of the proposed control methods are well exemplified by conducting two simulations.

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.

Exploring an unknown environment for a multi-robot system (MRS) is the problem of controlling a team of robots over all points of a given region in an efficient and safety manner. In this paper a cooperative exploration approach is developed by using a robot-sensor-network (RSN) and communication system and the technique of minimal information entropy. The exploration strategy is able to be divided by two stages: collection and coverage stage. In the first stage, considering the neighbors of the ith robot of the MRS, a grid-based approach incorporating with a potential approach is proposed for covering the maximal area in the sense of balance force. Furthermore, in the second stage, a spiral approach incorporating with the nonholonomic trajectory tracking control design is employed. Theoretical proof shows the stability of the proposed cooperative exploration method. Finally, simulations are also conducted to show the effectiveness of the proposed approach.

This paper presents methodologies for dynamic modeling and trajectory tracking of a nonholonomic wheeled mobile manipulator (WMM) with dual arms. The complete dynamic model of such a manipulator is easily established using the Lagrange's equation and MATHEMATICA. The structural properties of the overall system along with its subsystems are also well investigated and then exploited in further controller synthesis. The derived model is shown valid by reducing it to agree well with the mobile platform model. In order to solve the path tracking control problem of the wheeled mobile manipulator, a novel kinematic control scheme is proposed to deal with the nonholonomic constraints. With the backstepping technique and the filtered-error method, the nonlinear tracking control laws for the mobile manipulator system are constructed based on the Lyapunov stability theory. The proposed control scheme not only achieves simultaneous trajectory and velocity tracking, but also compensates for the dynamic interactions caused by the motions of the mobile platform and the two onboard manipulators. Simulation results are performed to illustrate the efficacy of the proposed control strategy.

This paper presents a direct self-tuning model-following control with integral action for a variable-frequency oil-cooling process, suitable for cooling down high-speed machine tools. The oil-cooling process is experimentally modelled as a first-order system with a given time-delay Based on this model, a direct self-tuning model-following control with integral action is proposed for achieving set-point tracking and disturbance rejection. A real-time adaptive predictive control algorithm is then presented and implemented utilizing a standalone digital signal processor (DSP). In comparison with the control method of Tsai and Huang, the proposed method provides a more practical and less computationally intensive approach for achieving the desired high-precision set-point tracking and disturbance rejection. Experimental results show that the proposed control method is capable of giving a satisfactory set-point tracking performance under set-point changes, fixed loads, and load changes.

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 develops methodologies and techniques for design, analysis and implementation of a robust predictive temperature controller for a variable frequency oil‐cooling machine, suited for high‐speed machine tools. The approximately mathematical model of the oil‐cooling machine is obtained from its open‐loop step response data. Based on this model, the robust predictive PI control law is derived based on the minimization of a generalized predictive performance criterion. Stability Robustness of the overall system is studied in some detail. A real‐time robust predictive PI control algorithm is implemented utilizing a digital signal processor (DSP) TMS320F243 from Texas Instruments. Simulation and experimental results show that the proposed robust predictive control method is capable of giving satisfactory performance under set‐point changes and load changes.

This paper presents indirect adaptive self-balancing and station keeping control methods using recurrent Wavelet Fuzzy CMAC (RWFCMAC) for an omnidirectional ball-driven chair in presence of significant system uncertainties. By backstepping, sliding-mode control and RWFCMAC, the self-balancing controller is synthesized to follow the rider’s inclination angles in both two axes (x-z and y-z axis), and the station-keeping controller is designed to allow the rider to maintain the vehicle at the same place. The RWFCMAC is designed to online learning the uncertainties caused by riders’ weights and different unknown frictions between the ball and terrain surfaces. The superior performance and merit of the proposed control methods are well exemplified by comparing to two existing controllers.

This paper presents a biological inverse kinematics (IK) method and a trajectory tracking approach for a 7-DOF robotic manipulator. The system structure of the manipulator is first designed and its forward kinematics is then derived using its Denavit-Hartenberg (DH) parameters. Based on the DH model, an inverse kinematics method using the combination of particle-swarm optimization (PSO) and real coded genetic algorithm (RGA) is proposed to find the seven joint angles of the manipulator while moving from one point to another, and then a smooth continuous trajectory is planned by connecting several via points. For trajectory tracking, a Jacobian inverse kinematic method along with a P controller and PI speed joint controllers is used to achieve trajectory tracking control with an acceptable accuracy. The effectiveness and merit of the proposed trajectory planning and tracking methods are well exemplified by conducting two simulations.

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.

The paper describes the design of single‐loop fuzzy supervisory predictive PID controllers for a plastics extruder barrel. A fuzzy supervisory shell is proposed to improve the set‐point tracking performance of the proposed PID method by appropriate adjustment of the weighting term for the control effort. Experimental results show that the proposed method is capable of giving a good result on the barrel.

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 novel methodologies for modeling and backstepping aggregated sliding-mode motion control of a 3D overhead crane. Lagrangian mechanics is adopted to establish a mathematical model of the system with frictions. A backstepping sliding-mode control method is used to maintain the nutation angle at zero and achieve trajectory tracking simultaneously. The effectiveness and merit of the proposed controller are exemplified by conducting several simulations on a real 3D overhead crane.

The paper presents techniques and design methodologies for modeling and LQR motion control of a ball-riding robot driven by three omnidirectional wheels. A completely dynamic model of the robot moving on a flat terrain is derived using Lagrangian mechanics. Two LQR controllers are synthesized to achieve station keeping and point stabilization. Through computer simulations and experimental results, both proposed controllers together with the built ball-riding robot are successfully shown to give a satisfactory control performance.