This paper is organized as follows. Section II introduces the canonical form of PML models. Section III presents a dynamic feedback linearization of the car-like robot. Section IV proposes a trackingcontroller design using dynamic feedback linearization based on PML model of the tricycle robot. Section V shows examples demonstrating the feasibil- ity of the proposed methods. Finally, section VI summarizes conclusions.
the mobile part of the robot is reduced to the three legs and the mobile platform. Consequently, higher velocities and ac- celerations of the mobile platform can be achieved. Another benefit is that the legs are made of only thin rods, thus, reducing the risk of leg interference. Further, the geomet- rical/physical parameters of the manipulator are also opti- mized for a given constant orientation workspace. The in- verse dynamic model is obtained using the Lagrangian dy- namic formulation method (Abdellatif and Heimann, 2009). The proposed robust task-space trajectorytrackingcontroller is based on a centralized proportional-integral-derivative (PID) control along with a nonlinear disturbance observer. The control schemes for parallel manipulator may be prin- cipally separated into two types, joint-space control estab- lished in joint-space coordinates (Davliakos and Papadopou- los, 2008; Honegger et al., 2000; Kim et al., 2000; Nguyen et al., 1992; Yang et al., 2010), and task-space control designed based on the task-space coordinates (Kim et al., 2005; Ting et al., 2004; Wu and Gu, 2005). The joint-space control ap- proach can be readily employed as an assemblage of several independent single-input single-output (SISO) control sys- tems using the data on each actuator feedback only. A classi- cal PID control in joint-space along with gravity compensa- tion has been employed in industry, but it does not always as- sure a great performance for parallel manipulators. However, the proposed robust task-space control approach improves the overall control performance by rejecting the uncertainty and nonlinear effects in motion equations. The rejections of system or model uncertainty, unknown external disturbance and nonlinear effects in the system motions have been com- pleted in the proposed control scheme with the help of an equivalent control law; a feed-forward control scheme and a nonlinear disturbance observer along with the nonlinear PID control scheme. In the proposed task-space control method, the desired motion of the end effector in task-space is used directly as the reference input of the control scheme. That is, the motion of the end effector can be obtained from the sys- tem sensors and compared with the reference input to form a feedback error in task-space. Therefore, an exact kinemat- ics model is not required in the task-space control, and thus this method is sensitive to joint-space errors or end effector pose errors due to joint clearances and other mechanical in- accuracies. The validity of the proposed control scheme is demonstrated with the help of virtual prototype experiments. The performance of the proposed control scheme including closed-loop stability, precision, sensitivity and robustness is analysed in theory and simulation.
A bibliographic review of some important related work that embraces different approaches of AGV trajectorytracking is provided. Padhy et al. (2010) designed a traditional PID controller for trajectorytracking. The structure and implementation of the PID was simple and yet valid for tracking performance. However, the proposed controller is not sufficient for applications that require high trajectorytracking accuracy. Guo et al. (2014) reported the trajectorytrackingcontroller of closed-loop control structure is derived using an integral back-stepping method to construct a new virtual variable. The Lyapunov theory is utilised to analyse the stability of the proposed trackingcontroller. Pawlowski et al. (2001) implemented a fuzzy logic for a mobile robot. The kinematic model of the mobile robot was introduced in the implementation. Antonelli et al. (2007) also proposed a fuzzy logic approach to deal with the trajectorytracking problem. In this approach, the input to fuzzy system is represented by approximate information concerning the next bend ahead the vehicle; the corresponding output is the cruise velocity that the vehicle needs to attain in order to safely drive on the path.
tilt angle and orientation angle as 0, π/6 rad and 0 respectively. Fig. 3 through 7 shows the simulation results. From fig. 3 it can be seen that the two controllers balanced the robot, both controllers settled the tilt angle in about 8.5 seconds, with an overshoot of 12.8%. Fig. 4 shows the velocity tracking response for the two controllers, both controllers settled in about 10.5 seconds, with an overshoot of about 25.9%. Both controllers’ tracks the reference orientation as shown in fig. 5, both controllers settled the orientation angle in about 1.5 seconds, with an overshoot of 0%. Figures 6 and 7 shows the control inputs for wheel 1 and wheel 2 required for the two controllers, from the result it can be seen that the proposed controller require slightly higher starting torque compare to the conventional controller under this condition.
In the last years a large interest has risen for the four rotor concept since it appears to present simultaneously hovering, orientation and trajectorytracking capabilities of interest in many practical applications . The flight mechanics of rotorcraft are highly non linear and different control approaches (integral LQR techniques, integral sliding mode control , reinforcement learning ) have been considered with little success to achieve not only autonomous hovering and orientation, but also trajectorytracking In this paper, after introducing some simplifying assumptions, the flight dynamics equations for a four rotor aircraft with fixed pitch blades are considered.
Abstract: Exoskeleton robots are a rising technology in industrial contexts to assist humans in onerous applications. Mechanical and control design solutions are intensively investigated to achieve a high performance human-robot collaboration (e.g., transparency, ergonomics, safety, etc.). However, the most of the investigated solutions involve high-cost hardware, complex design solutions and standard actuation. In the presented work, an industrial exoskeleton for lifting and transportation of heavy parts is proposed. A low-cost mechanical design solution is proposed, exploiting compliant actuation at the shoulder joint to increase safety and transparency in human-robot cooperation. A hierarchic model-based controller is then proposed (including the modeling of the compliant actuator) to actively assist the human while executing the task. An inner optimal controller is proposed for trajectorytracking, while an outer fuzzy logic controller is proposed to online deform the task trajectory on the basis of the human’s intention of motion. A gain scheduler is also designed to calculate the optimal control gains on the basis of the performed trajectory. Simulations have been performed in order to validate the performance of the proposed device, showing promising results. The prototype is under realization. Keywords: Industrial exoskeleton design; industrial exoskeleton control; human-robot collaboration; optimal control; empowering fuzzy control.
In this communication a nonlinear inverse control technique applied to rotorcraft trajectorytracking has been considered. This approach leads to the design of a two level control structure based on analytical laws. However the possibility of actuators saturation has led to the design of a supervision layer whose objective is to modify references values for the nonlinear inverse control laws so that the tracking performance is maintained as much as possible. The applicability of the proposed approach appear acceptable since the complexity of the resulting optimization problems to be solved online appear to be rather low. Then the proposed approach should enlarge the field of applications for rotorcraft. This approach could be adapted to the supervision of actuators saturation with other autonomous aircraft.
Abstract: In the paper, a four-dimensional model of cyclic reactions of the type Prigogine’s Brusselator is considered. It is shown that the corresponding dynamical system does not have a closed trajectory in the positive orthant that will make it inadequate with the main property of chemical reactions of Brusselator type. Therefore, a new modified Brusselator model is proposed in the form of a four-dimensional dynamic system. Also, the existence of a closed trajectory is proved by the DN-tracking method for a certain value of the parameter which expresses the rate of addition one of the reagents to the reaction from an external source.
Fuzzy logic controller shows adaptive character in nature as it has robust response to a system with uncertainty, parameter variation and load disturbance. It is very much used to control an ill-defined, non-linear or imprecise system. It does not require accurate model. With this technique not only tight charging and discharging is achieved but fast dynamic response can also be achieved. Fuzzy logic controller uses membership functions assigned with some linguistic values and range in which they need to give output or to take input. They accept a set of rules for their functioning and processing of membership functions.
In this paper a simulink based approach is developed for trajectorytracking of robot manipulator. A robot manipulator is widely used in many industrial application.A robot manipulator moves the end effector to the configuration instructed by the user. The input from the main unit is transformed in to the desired configuration through forward kinematics. This configuration is sent to the robot controller to transform the configuration into joint angles. The simulink model is developed to provide basic block to model kinematics and trajectorytracking of robot manipulator. Availability of such library model of robot manipulator software, where the manipulator controller can be modelled using model library blocks and production can be automatically generated using existing code generators for simulink. In this the desired and the actual trajectory of the end effector under different conditions is shown with the help of MATLAB simulation.
Using the auto-landing data from Shen et al. [13,14] and the Takagi–Sugeno  inference system, the trajectorytracking of an aircraft landing operation was simulated. The MATLAB Fuzzy Logic Toolbox, a product of Math Works Inc., was used to carry out the simulations. For the first data set, the problem was simulated with 3, 4 and 6 membership functions, each with both zero-order and first-order Takagi–Sugeno inference systems. For the second to fifth data sets, only 4 and 6 membership functions were used, each with the zero-order and first-order inference systems. A total of 22 separate simulations were carried out.
In the mobile satellite communication, received systems are mounted on the movable device such as ship, train, car or airplane. In order to receive continuous signals, antenna system must be steered in both the azimuth and elevation angle to track a satellite. Tracking capabilities depend on the beam width of the antennas and the speed of mobile motions. Thus, high gain and directional antennas with narrow beams need to track the satellite both in elevation and azimuth directions. In the fact that, antennas should track the satellite only in the azimuth directions because the elevation angles to the satellite are almost constant. The satellite tracking system shown in Fig.1, which consists of a satellite antenna, a low-noise block-down converter (LNB), a set-top box tuner, antenna control unit (ACU) and mechanical system.
Abstract: This paper presents control of nonlinear system using Enhanced PID (EPID) controller. Spherical tank system is investigated as nonlinear system. The main aim is to control the liquid level in a spherical tank system for the variations in the area of cross section of tank with change in shape. The performance of the Enhanced PID controller is analysed and compared with conventional control Ziegler-Nichols PID controller (ZN-PID). The performance of the proposed controller is measured in terms of time domain specifications like overshoot, settling time, ISE(Integral Square Error) and IAE(Integral Absolute Error). The simulation results show that the proposed EPID controller provides consistent performance compared to ZN-PID controller.
Abstract- In this research, a differential drive wheeled mobile robot which can automatically track the trajectories was designed by using the fuzzy logic controller. The motion task of the mobile robot is motion through the points. When the user gives the desired point’s position (x, y, θ) from PC, the position error and orientation error are derived. The position error and orientation error is sent as input to the fuzzy controller and then the required velocity is given to the motor depending to the position and orientation angle between the start point and desired point. Based on the electrical section, PIC16F887 microcontroller, motor driver, magnetic encoder DC motor and LCD are mainly used. The purpose of the use of driver is to control the rotation of the motors - forward or backward. So, VNH2SP30 driver is used in this system for this purpose. The magnetic encoder is to measure wheel revolution and it can b e translated into linear displacement of the wheel. The design procedure consists of the kinematic structure of robot, hardware and software implementation and microcontroller programming. A simulation model of the control system is developed with MATLAB / SIMULINK block diagram. Simulation results of the proposed system are given to approve the effectiveness of the system.
University dynamic design lab . It is always challenging to determine the exact kinematic and dynamic models, and the uncertainties in these models cannot be avoided. In this regard, many researchers have presented adaptive and robust controllers for dealing with the issue of path tracking in wheeled mobile robots [10, 11]. Due to the existence of parametric and non-parametric uncertainties in system models and also the existence of non-holonomic constraints, the designing of output feedback controllers is a challenging task. Besides, the separation principle cannot be easily applied in this case. Both the trajectory-tracking and path-following problems have their own particular complexities when it comes to the kinematic model of car-like moving robots, which happens to be non-holonomic. The uncertainties associated with parametric modeling and unknown external disturbances constitute a significant concern in the development of advanced controllers for uncrewed vehicles. The unknown external disturbances may be caused by changing driving conditions. An adaptive neural controller for controlling the movement of an autonomous vehicle has been presented in this paper. The target of this work is to design an indirect adaptive output feedback controller by using an artificial neural network (NN) and considering model uncertainties and external disturbances. The network used in this indirect controller is a type of NN with radial basis functions (RBFs).
Abstract In this paper, we present a novel approach to keyframe-based tracking, called bi-directional tracking. Given two object templates in the beginning and end- ing keyframes, the bi-directional tracker outputs the MAP (Maximum A Posterior) solution of the whole state se- quence of the target object in the Bayesian framework. First, a number of 3D trajectory segments of the object are extracted from the input video, using a novel trajectory seg- ment analysis. Second, these disconnected trajectory seg- ments due to occlusion are linked by a number of inferred occlusion segments. Last, the MAP solution is obtained by trajectory optimization in a coarse-to-fine manner. Exper- imental results show the robustness of our approach with respect to sudden motion, ambiguity, and short and long pe- riods of occlusion.
Abstract— This paper addresses the lateral control of a vehicle during lane change maneuvers. The proposed design procedure aims to answer the questions of control using cost-effective sensors implementation, adaptation to measured variables and robustness to unmeasured varying parameters. This is achieved through a static output feedback controller with preview information. The only used measurements are the lateral displacement at sensor location and the yaw angle relative to the lane centerline. The vehicle lateral model is augmented with an integral action, the error signal and the preview reference signal. The controller is synthesized using the LMI framework thanks to a relaxation method that removes the nonlinear terms. Simulations are conducted for various scenarios showing the ability of the design method to handle different performance objectives.
Figs. 5 to 9 show the performance of the designed controllers in the absence of uncertainties and external disturbances. As shown in Fig. 5, using all of the controllers, the WMR has tracked the reference circular trajectory in a significant accuracy. It can be seen from Fig. 6 trough 8 that the FL controller shows a better tracking performance for both position and orientation trajectories compared to those of the sliding mode controllers. However, as shown in Fig. 8, the magnitude of the input torques to the driving wheels is significantly larger when the FL method has been used. Due to the natural conservativeness of the fuzzy systems, the FSMC has resulted in small tracking errors in comparison with that of the SMC. However, the importance of both fuzzy and pure SMCs is understood during affecting the WMR by modeling uncertainties and exogenous unknown but bounded inputs. The simulated performances of the FL and sliding mode controllers to trajectorytracking of the WMR under modeling uncertainties and Gaussian white noises have been shown in Figs. 10 through 14. According to Fig. 10, the FL controller is highly sensitive to measurement noises. However, the SMCs show a relatively high robustness against both uncertainties and measurement noises. From Fig. 11 through 13, a considerable small tracking error along both position and orientation trajectories are resulted using SMC and FSMC methods compared with the FL method. Furthermore, the FSMC has shown a more reliable trajectorytracking performance compared to the SMC. As the other superiority with respect to FL method, the SMC request small input torques of driving wheel. For instance, as it is shown in Fig. 14, the input torque to the right driving wheel of the WMR is considerably large due to using FL controller. However, the requested torques by the SMCs are bounded to a small upper level.
The first aspect was to simulate the system without any tracking mechanism attached to it to test the functionality of the designed PV cell array with the sole aim of studying the P-V (power-voltage) and I-V (current-voltage) characteristics of the solar cells. Thereafter, the two-axis tracking system was incorporated and tested with PID controllers for controlling the positions of the solar arrays in the horizontal and vertical planes. Besides, fuzzy logic controllers were introduced within the system to equally assess their performances. The final analogy was to briefly compare the results of this different tracking system with the sole aim of finding which of them has a better performance given all the developed models. It is important to display the values of the solar panel generated insolation and power as illustrated in tables 2 and 3
also can be controlled to its command (2, 0.1436), but the trajectory in comparison with the one in Fig. 5(a) is obviously sensitive to external disturbances. Besides, the responses of the capacitor voltages and inductor current as shown in 6(b)−(d) become slow, and their corresponding tracking responses degenerate due to the occurrence of external disturbances.