Dynamic Tracking Control Research Articles

Dynamic adaptive trajectory tracking control of nonholonomic mobile robots using multiple models approach

This paper presents an approach for the trajectory tracking control of nonholonomic wheeled mobile robots (WMR) by combining one of the existing adaptive control methods and multiple identification models. The overall system includes two types of controllers in the control scheme. A kinematic controller developed by using kinematic model produces the required linear and angular velocities of the robot for tracking a reference trajectory. These required velocities are used to calculate the torques using an adaptive dynamic controller with multiple models. The proposed method uses the multiple models of the WMR for the identification of the dynamic parameters and performs switching between the given models. The models used in the identification are identical, except for the initial estimates of the parameters. By using an adaptive dynamic controller with multiple models of the WMR, enhancement in transient response is obtained. Stability analysis of the overall system is given, and simulation results are presented to demonstrate the effective performance of the adaptive control by using multiple models approach.

Decentralized Output Feedback Adaptive NN Tracking Control for Time-Delay Stochastic Nonlinear Systems With Prescribed Performance.

This paper studies the dynamic output feedback tracking control problem for stochastic interconnected time-delay systems with the prescribed performance. The subsystems are in the form of triangular structure. First, we design a reduced-order observer independent of time delay to estimate the unmeasured state variables online instead of the traditional full-order observer. Then, a new state transformation is proposed in consideration of the prescribed performance requirement. Using neural network to approximate the composite unknown nonlinear function, the corresponding decentralized output tracking controller is designed. It is strictly proved that the resulting closed-loop system is stable in probability in the sense of uniformly ultimately boundedness and that both transient-state and steady-state performances are preserved. Finally, a simulation example is given, and the result shows the effectiveness of the proposed control design method.

Dynamic Surface Fault Tolerant Tracking Control Design for UAV with Transient Performance

In this paper, a novel fault tolerant control (FTC) approach is proposed for the attitude control systems of unmanned aerial vehicle (UAV) using dynamic surface control technique. Firstly, the nonlinear attitude dynamic models of UAV with actuator faults are given, which represent the dynamic characteristics of UAV. Then, a fault tolerant control scheme is proposed, which guarantees the asymptotic tracking and ultimate uniform boundedness of the closed-loop attitude control systems of UAV in the presence of an actuator fault. Further, a transient performance is considered in the designing of FTC, which characterizes the convergence rate and maximum overshoot of the attitude tracking error. Finally, simulation results are given to illustrate the effectiveness of the developed FTC algorithm.

A Dynamic Fuzzy Neural Networks-Based Surface Vessels Course Tracking Controller

A Dynamic Fuzzy Neural Networks Course Tracking Controller (DFNNCTC) for Surface Vessels is presented to solve the uncertainties coursing by the wide and wave. A Dynamic Fuzzy Neural Networks (DFNN) combines with a PID controller to integrate the DFNNCTC, in which the structure and parameters are adjusted online, and the fuzzy rules are automatically generated when being trained. The intelligent algorithm conquers the disadvantage of either overfitting or overtraining in traditional static fuzzy neural networks-based control methods. Simulation results of a container’s course tracking control validate the effectiveness of the proposed algorithm.

Design and stability analysis of adaptive fuzzy feedback controller for nonlinear systems by Takagi–Sugeno model-based adaptation scheme

This paper focuses on the design of a real-time adaptive Takagi---Sugeno (T---S) fuzzy-based dynamic feedback tracking controller to deal with the metallic sphere position control of a magnetic levitation system (MLS), which is an intricate and highly nonlinear system involving plant uncertainties and external disturbances. The dynamic model of this MLS is first constructed based on the concepts of geometry and motion dynamics. The objective of this proposed control strategies is to design a real-time adaptive controller with the help of Takagi---Sugeno type fuzzy-based output feedback techniques and to directly ensure the asymptotic stability of the closed-loop controlled system by Lyapunov stability theorem without the requirement of strict constraints, detailed system information, and auxiliary compensated controllers. Proposed adaptive tracking controller is developed in such a way such that all the states and signals of the closed-loop system are bounded and the trajectory tracking error is as small as possible. In this paper, the controller consists of adaptive and robustifying components whose role is to nullify the effect of uncertainties and achieve a desired tracking performance. Here, separate adaptive control laws have been proposed to automatically take care of external disturbance and uncertainties by designing a two-port controller. The first part stabilizes the nominal plant; without modeling uncertainties. The second part of the controller is to reject modeling uncertainties. The good transient control performance and robustness to uncertainties of the proposed adaptive control scheme for the MLS is verified by numerical simulations and real-time experimental results. These results demonstrate that, the proposed adaptive controller yields favorable control performance superior to that of PID and and Neuro-fuzzy network controller in terms of overshoot, settling time, mean square error and steady-state error and also it can guarantee the system stability and parameter convergence with a pole placement algorithm.

An observer-based consensus tracking control and application to event-triggered tracking

Leader–follower mechanism provides an important framework for multi-agent consensus problems. In this paper, a consensus tracking problem is investigated for a second-order multi-agent system with a self-active leader. The input (acceleration) to the leader is assumed to be time-varying and unavailable to followers. An observer-based consensus tracking control is designed on the basis of a novel distributed velocity estimation technique. The ultimate boundedness and the stability of the tracking error system are analyzed by virtue of an Input-State-Stability (ISS) Lyapunov function approach. Then, the dynamic consensus tracking control is applied to solve an event-triggered tracking problem. Finally, some numerical simulation results are presented to demonstrate the effectiveness of the proposed consensus tracking control.

Dynamic Hysteresis Based Modeling Of Piezoelectric Actuators

Piezoelectric actuators are popularly applied as actuators in high precision systems due to their small displacement resolution, fast response and simple construction. However, the hysteresis nonlinear behavior limits the dynamic modeling and tracking control of piezoelectric actuators. This paper studies a dynamic model of a moving stage driven by piezoelectric stack actuator. The Bouc-Wen model is introduced and analyzed to express the nonlinear hysteresis term. Two triangular actuating voltages with frequency of 1 Hz and amplitudes of 80 V and 90 V are applied to drive the piezoelectric stack actuator. The results demonstrate the existence of the hysteresis phenomenon between the input voltage and the output displacement of the piezoelectric stack actuator, and validate the correctness of the model.

Reinforcement learning-based intelligent tracking control for wheeled mobile robot

This paper proposes a new method to design a reinforcement learning-based integrated kinematic and dynamic tracking control algorithm for a non-holonomic wheeled mobile robot without knowledge of the system’s drift tracking dynamics. The actor critic structure in the control scheme uses only one neural network to reduce computational cost and storage resources. A novel tuning law for a single neural network is designed to learn an online solution of a tracking Hamilton–Jacobi–Isaacs (HJI) equation. The HJI solution is used to approximate an H∞ optimal tracking performance index function and an intelligent tracking control law in the case of the worst disturbance. The laws guarantee closed-loop stability in real time. The convergence and stability of the overall system are proved by Lyapunov techniques. The simulation results on a non-linear system and wheeled mobile robot verify the effectiveness of the proposed controller.

The Quadrotor Dynamic Modeling and Indoor Target Tracking Control Method

A reliable nonlinear dynamic model of the quadrotor is presented. The nonlinear dynamic model includes actuator dynamic and aerodynamic effect. Since the rotors run near a constant hovering speed, the dynamic model is simplified at hovering operating point. Based on the simplified nonlinear dynamic model, the PID controllers with feedback linearization and feedforward control are proposed using the backstepping method. These controllers are used to control both the attitude and position of the quadrotor. A fully custom quadrotor is developed to verify the correctness of the dynamic model and control algorithms. The attitude of the quadrotor is measured by inertia measurement unit (IMU). The position of the quadrotor in a GPS-denied environment, especially indoor environment, is estimated from the downward camera and ultrasonic sensor measurements. The validity and effectiveness of the proposed dynamic model and control algorithms are demonstrated by experimental results. It is shown that the vehicle achieves robust vision-based hovering and moving target tracking control.

Optimal pricing policy for a deteriorating product by dynamic tracking control

We study the optimal selling price of a deteriorating product under a deterministic situation in a finite time horizon where the time horizon is either known or unknown. Inventory holding cost is expressed as a quadratic function of the current inventory level. Given a known time horizon, we develop a model by considering the deterioration dynamics of the product, and show its equivalence to a generalised optimal control problem of a linear quadratic form, i.e. an optimal dynamic tracking problem with constraints on the control variable. An optimal pricing policy is derived based on the maximum value principle. The control policy takes a state feedback form; it exhibits a closed-loop relationship between the optimal selling price (control variable) and the optimal inventory level (state variable). Given an unknown time horizon, an optimal pricing policy is derived through a similar approach when the initial inventory level meets certain conditions. Numerical situations are conducted to illustrate the effectiveness of the derived price control policies. Some interesting managerial insights are discussed.

Performance evaluation of platform-specific implementations of numerically complex control designs for nano-positioning applications

Developing machines capable of positioning and measuring in nano-metre dimensions is an overall challenging endeavour, which not at least includes providing a highly dynamic and precise trajectory tracking control system. To effectively employ such a control system an adequately performing implementation on an embedded real-time processing system is essential for process performance and quality. In this article an implementation study of a Kalman filter component is presented, comparing the solutions for general purpose processor and digital signal processor to a range of designs for field-programmble gate arrays. Evaluating the performance of the respective implementations for a PXI platform and thus indicating the feasibility of industrial and standard-compliant application is the emphasis of this contribution.

Dynamic Recurrent Wavelet Neural Network Observer Based Tracking Control for a Class of Uncertain Nonaffine Systems

In this paper, a dynamic recurrent wavelet neural network observer and tracking control strategy is presented for a class of uncertain, nonaffine systems. In proposed scheme a dynamic recurrent wavelet network is used to design a nonlinear observer .Adaptation laws are developed for the online tuning of wavelet parameters. Based on the estimated states, a state feedback control law is derived to achieve the desired tracking performance. The stability of closed loop system and ultimate upper boundedness all closed loop signals is proven in Lyapunov sense. Effectiveness of proposed scheme is demonstrated through numerical simulation.

Design of a robust neural network-based tracking controller for a class of electrically driven nonholonomic mechanical systems

This paper addresses the problem of designing robust tracking controls for a class of uncertain nonholonomic systems actuated by brushed direct current (DC) motors. This class of electrically driven nonholonomic mechanical systems can be perturbed by plant uncertainties, unmodeled time-varying perturbations, and external disturbances. An adaptive neural network-based dynamic feedback tracking controller will be developed such that all the states and signals of the closed-loop system are bounded and the trajectory tracking error is as small as possible. Consequently, for practical applications, the intelligent robust tracking control scheme developed here can be employed to handle a broader class of electrically driven nonholonomic systems in the presence of high-degree time-varying uncertainties. Finally, simulation examples are presented to demonstrate the effectiveness of the proposed control algorithms.

Model and control for four-powered-caster vehicle: a probability-based approach

A vehicle with four powered caster wheels can provide much more motion flexibility in a constrained environment. However, the dynamic modelling and control of such system is challenging due to its high redundancy. This paper investigates the dynamic model and tracking control for a four-powered-caster vehicle (4-PCV) on complex terrain without any additional sensor. The torques applied to the wheels are dynamically redistributed based on the real-time conditions of the whole wheel–ground interactions so that the vehicle can track the desired trajectory when moving on different terrains. A dynamic model considering the wheel–ground interaction is first derived. Then a novel approach based on a probability scheme is proposed to identify the status of the vehicle and the wheel slip ratio by only observing the velocity feedback from motors encoders. Based on this real-time perception information, a tracking controller and a torque distribution scheme are applied to make sure that each wheel can be self-adapted to meet a complex wheel–ground condition to eliminate or reduce the probability of slippage. The effectiveness of the proposed estimation approach and the performance of the torque distribution schemes are verified by simulation.

Dynamic Recurrent Wavelet Neural Network Observer Based Tracking Control for a Class of Uncertain Nonaffine Systems

In this paper, a dynamic recurrent wavelet neural network observer and tracking control strategy is presented for a class of uncertain, nonaffine systems. In proposed scheme a dynamic recurrent wavelet network is used to design a nonlinear observer .Adaptation laws are developed for the online tuning of wavelet parameters. Based on the estimated states, a state feedback control law is derived to achieve the desired tracking performance. The stability of closed loop system and ultimate upper boundedness all closed loop signals is proven in Lyapunov sense. Effectiveness of proposed scheme is demonstrated through numerical simulation.

Adaptive neural network based tracking control for electrically driven flexible-joint robots without velocity measurements

This paper addresses the motion tracking control for a class of flexible-joint robotic manipulators actuated by brushed direct current motors. This class of electrically driven flexible-joint robots is perturbed by plant uncertainties and external disturbances. Adaptive neural network systems are employed to approximate the behaviors of uncertain mechanical and electrical dynamics. A reduced-order observer is constructed to estimate the velocity signals. Only the measurements of link position and armature current are required for feedback. Consequently, an adaptive neural network-based dynamic feedback tracking controller without velocity measurements is developed such that all the states and signals of the closed-loop system are bounded and the trajectory tracking errors can be made as small as possible. Finally, simulation results are presented to demonstrate the effectiveness of the proposed control algorithms.

Computed force and velocity control for spatial multi-DOF electro-hydraulic parallel manipulator

A novel dynamic trajectory tracking controller for spatial 6-DOF electro-hydraulic parallel manipulator considering system nonlinearity-computed force and velocity controller is proposed, with a view of improving the control performance with high computational efficiency of control algorithm. The dynamic model of electro-hydraulic parallel manipulator, both mechanical and hydraulic system, is described by using Kane and hydromechanics method. The requisite system states are estimated via forward kinematics based upon global Newton–Raphson with monotonic descent algorithms under the measured actuator position. The desired leg position and velocity required for the proposed controller are calculated by an analytical method corresponding to the desired generalized pose, and the desired driven force is computed with an effectively simplified inverse dynamics. Under feed-forward of the desired driven force and velocity, the computed force and velocity controller is developed with actual leg position as its feedback only, and the desired leg position, velocity and driven force as its input. The control performance of the proposed controller for multi-DOF parallel manipulator is evaluated in theory and experiment, especially for dynamic tracking performance. Experimental results show that the presented controller can greatly improve the dynamic trajectory tracking performance for high real time electro-hydraulic parallel manipulator.

Robust tracking control for a class of electrically driven flexible-joint robots without velocity measurements

This article addresses the motion tracking control for a class of flexible-joint robotic manipulators actuated by brushed direct current motors. This class of electrically driven flexible-joint robots is perturbed by time-varying parametric uncertainties and external disturbances. A novel observer-based robust dynamic feedback tracking controller without velocity measurements will be developed such that the resulting closed-loop system is locally stable, all the states and signals are bounded and the trajectory tracking errors can be made as small as possible. Only the measurements of link position and armature current are required for feedback and so the number of sensors in the practical implementation of the developed control scheme can be greatly reduced. The observer structure is of reduced order in the sense that the observer is constructed only to estimate the velocity signals and whose dimension is half of the dimension of flexible-joint robots. Especially, for the set-point regulation problem, the developed controller is simplified to a linear time-invariant controller. Consequently, the robust tracking control scheme developed in this study can be extended to handle a broader class of uncertain electrically driven flexible-joint robots and the developed robust control schemes possess the properties of computational simplicity and easy implementation. Finally, simulation results are presented to demonstrate the effectiveness of the proposed control algorithms.

Dynamic Simulation of Hybrid-Driven Planar Five-Bar Parallel Mechanism Based on SimMechanics and Tracking Control

This paper investigates dynamic simulation and trajectory tracking control of hybrid-driven planar five-bar parallel mechanism (HPPM). To begin with, a simulation model of dynamics based on MATLAB/SimMechanics is established. Then, traditional PD control and closed loop PD-type iterative learning control of the HPPM are designed. At the end, the simulation based on SimMechanics is carried out, which acquires angular, angular velocity, angular acceleration of two driving links and constraint reaction of kinematic pairs at any time. In addition, the performance of the closed loop PD-type iterative learning control is compared with that of the traditional PD controller through simulations of the HPPM in the presence of the model external disturbances. The simulation results indicate that a perfect trajectory tracking of end-effector of the HPPM is achieved by the closed loop PD-type iterative learning controller.

Control of flexible joint robot manipulators using a combined controller with neural network and linear regulator

This paper addresses the issue of dynamic trajectory tracking control of flexible joint robot manipulators. A new control strategy based on the technologies of linear feedback and neural network is proposed. It is well known that linear feedback controllers are simpler and easier to design, and neural networks have learning ability. These two advantages are utilized in order to deal with the control problems arising from non-linearity and dynamic instability of the flexible joint robot. The linear feedback control scheme is designed using optimal control approach, and the neural network is then designed based on the BP method and added to the control system. There are two different ways to add the neural network into the control system: being an additional regulator in parallel with the linear feedback controller, and being a dynamic compensator with some function of regulator. This leads to two different control systems. Performance of these two control systems are compared based on simulation results. Experiments are carried out using a two-link flexible joint robot as a test bed. The results demonstrate the effectiveness and usefulness of the proposed control method.