Discrete-Time Computed Torque Control with PSO-Based Tuning for Energy-Efficient Mobile Manipulator Trajectory Tracking

Mobile manipulators are being deployed for transporting parts between machines and work stations in warehouses and shop floors. To increase the efficiency of operations, these mobile manipulators are required to complete the tasks as fast as possible. Picking up parts with the manipulator while the mobile base is moving decreases the time required to complete the transportation task and increases the efficiency of operations. However, motions of the manipulator on a moving platform can be risky, and hence, it is desired that the manipulator starts and ends its motions as close as possible to the part being picked up. In this article, we present a bidirectional sampling-based scheme for generating such manipulator trajectories for a given mobile base trajectory for pickup and transportation. Our approach implicitly determines the location of the mobile base where the manipulator motion starts and ends as well as where grasping happens. It also determines which grasping pose to use for picking up the part. Furthermore, we have presented the techniques to reduce the manipulator motion time (span time) and the computation time. Our approach enables us to reduce span time on average by 35% with a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$16\times $ </tex-math></inline-formula> reduction in the computation time compared to the RRT-based baseline methods. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Transportation of objects is a crucial application in industrial and warehouse environments. Conveyor belts and AGVs are typically used for such applications as they provide an efficient mode of transportation of a large number of objects. However, they may not provide the flexibility which mobile manipulators bring in for small-scale operations. The method presented in this article provides a way to increase the efficiency of operation for mobile manipulators for transportation tasks. We attempt to reduce the risks of the manipulator colliding with expensive equipment and moving obstacles by making sure that the manipulator moves only when needed for picking up objects. Moreover, the method can be used to pick up and transport a variety of parts in different ways using a two-fingered gripper. The method can also easily incorporate different types of grippers and mobile platforms.

This article describes an adaptive robust control algorithm of the mobile manipulator based on robust neural network to solve disturbances, nonlinearity, nonholonomic constraints between the mobile platform and the mounted manipulator. According to the nonlinear mapping ability of neural network and fast learning ability, neural network and robust control strategies are integrated into the adaptive control algorithm for the mobile manipulator with nonlinear and nonholonomic constraints. Basis on the mobile manipulator dynamics model and its modeling analysis, the controller can drive the trajectory tracking error of the mobile manipulator to converge to zero. The simulation results show that the algorithm has higher precision of trajectory tracking control and can achieve good control effect.

Mobile manipulators have attracted a lot of interest recently. This paper discusses the development of mobile manipulators in recent years in the aspect of motion planning and coordinated control between mobile platform and manipulator. An intelligent controller based on RBF neural network is introduced for the coordinating control of the mobile manipulator, which mimics human behavior. Simulation results was given out and the method has been applied on the Hebut-II mobile robot platform. Experimental results verified the validity of the method.

In this paper, Linear Parameter Varying-Model Predictive Control (LPV-MPC) for trajectory tracking for Autonomous Vehicles (AVs) is proposed. This method is based on the time-varying LPV is the form of the state space representation from the mathematical model of the vehicle. The LPV representation form which uses the dynamic model of the vehicle allows the incorporation of time-varying dynamics, providing a more accurate representation of the vehicle's behavior. The designed LPV-MPC controller for AVs is specifically designed to handle constraints in trajectory tracking. To enhance its performance, Particle Swarm Optimization (PSO) is employed as an optimization technique. PSO is used to tune the weighting matrices of the control parameters, optimizing the system response and improving trajectory tracking performance. To evaluate the effectiveness of the LPV-MPC system, extensive simulations are conducted and results are compared with Linear and Non-Linear MPCs. The main benefit of using the LPV-MPC method is its ability to calculate solutions almost as good as the non-linear MPC version yet significantly reducing the computational cost. The capability of the LPV-MPC controller as compared to the linear version is in its effective tracking, particularly for the non-linear reference trajectories.

Mobile manipulators, which are intrinsically redundant when the manipulator and mobile base are moving together, are known for their capabilities to carry out multiple tasks at the same time. This paper presents a whole-body control framework, inspired by legged bio-robots, for a velocity controlled non-holonomic mobile manipulator based on task priority. Control primitives, such as manipulability optimization, trajectory tracking of the end-effector and mobile base, and collision avoidance, are considered in the framework and arranged at different priorities. Lower priority tasks are projected into the null space of control tasks with higher priorities. As a result, lower level tasks are completed without affecting the performance of higher priority tasks. Several experiments are implemented to verify the effectiveness of the proposed controller. The proposed method is proved to be an effective way to solve the whole-body control problem of velocity controlled mobile manipulators.

Coot optimization algorithm-tuned neural network-enhanced PID controllers for robust trajectory tracking of three-link rigid robot manipulator

This paper present an optimal Fractional Order PID (FOPID) controller based on Particle Swarm Optimization (PSO) for controlling the trajectory tracking of Wheeled Mobile Robot(WMR).The issue of trajectory tracking with given a desired reference velocity is minimized to get the distance and deviation angle equal to zero, to realize the objective of trajectory tracking a two FOPID controllers are used for velocity control and azimuth control to implement the trajectory tracking control. A path planning and path tracking methodologies are used to give different desired tracking trajectories. PSO algorithm is using to find the optimal parameters of FOPID controllers. The kinematic and dynamic models of wheeled mobile robot for desired trajectory tracking with PSO algorithm are simulated in Simulink-Matlab. Simulation results show that the optimal FOPID controllers are more effective and has better dynamic performance than the conventional methods.

Since the traditional Model Predictive Control(MPC), which is widely used for trajectory tracking of autonomous vehicle, cannot analyze and determine specific parameters of the controller by mathematical methods. In this paper, a trajectory tracking control method based on model prediction is proposed to solve the problem of unmanned vehicle trajectory tracking, and the controller is optimized in a performance objective driven way. Specifically, the cost function of the model predictive controller is parameterized. And the global optimal performance in a specific scenario as the goal to build the global performance cost function. Then, the global performance cost is expressed as a Gaussian process, and new parameters of the next optimization are inferred by Bayesian optimization. The controller parameters of global performance optimization are found with a small learning cost through multiple iterations to improve tracking performance. To verify the effectiveness of this data-driven optimization algorithm, lane-changing experiments with Carsim and Matlab/Simulink are carried out. According to the test data, it is proved that the performance of trajectory tracking under this data-driven MPC algorithm is optimized.

Dynamic modelling and motion control of a newly developed 5-DOF robot manipulator is presented here. Optimized PID gains are obtained using PSO (Particle Swarm Optimization) method. a PID and a PID-CTC (Computed Torque Control) controller with optimized gains are examined for reference trajectory tracking control for the manipulator. Controllers’ performance for unit step inputs are evaluated using computer simulations. It is shown that this PSO optimized gain PID-CTC controller makes the manipulator follow its trajectory with no steady-state error. Controllers lead to small tracking errors, which is suitable for intended application; the PID-CTC controller provides better overall transient responses. The simulation model presented here, can be used for other applications. Contributions of this paper is mostly on application of PSO for the large 5-DOF manipulator with uncertain settings.

A mobile manipulator is a mobile platform with a mounted serial manipulator. A Mobile Manipulator is a system subject to its kinematic restrictions and the degrees of freedom of the manipulator arm mounted on it. These systems combine the advantages of mobile platforms and robotic arms, and reduce their disadvantages. For example, a mobile manipulator has larger working space when it has a mobile platform, as it offers more functionality during operation. For the previously mentioned in this work will be shown the implementation in the robotic simulation software Webots, a mobile manipulator that allows to determine its operation in a virtual environment

The robotic airship can be used as an aerostatic platform for many potential applications, for example, communication, hovering payload deliveries, data-gathering for research studies, etc. These applications require a fully autonomous perspective of an airship. One of the important aspects of airship autonomy is trajectory tracking control. An airship has complex and uncertain nonlinear dynamics which pose a major challenge for designing a precise trajectory tracking control. This paper addresses the airship trajectory tracking control problem under model uncertainties and wind disturbance. We propose a lumped model uncertainties and wind disturbance estimation approach based on an unscented Kalman filter. The estimated lumped uncertainty is used by the Sliding Mode Controller (SMC) for ultimate control of airship trajectory tracking. This comprehensive algorithm, Unscented Kalman filter-based Sliding Mode Controller (USMC), is used as a robust adaptive control solution to track the desired trajectory. The stability and convergence of the proposed method are investigated using the Lyapunov stability analysis. Simulation results show that the proposed method efficiently tracks the desired trajectory. The method solves the stability, convergence, and chattering problem of SMC without the bound constraint of model uncertainties and wind disturbance.

<abstract> <bold>Abstract.</bold> For the spraying mobile robot working in greenhouse, due to the inconsistency of drive motors and the rough walking surface, it is easy to track off. For the liquidity of pesticide, the load always changes even the speed jumps. Because of these uncertainties, external disturbances and the difficulty of constructing the system dynamic model, it is hard to implement the trajectory tracking control of the spraying mobile robot steadily, precisely and quickly. In order to solve the problem, a smooth sliding mode trajectory tracking control method is proposed based on the distribute control strategy for each branch. Moreover, its stability is proved using the Lyapunov function. The simulation results show that the proposed method can track the desired trajectories precisely, quickly and steadily under the strong white noise. The chattering phenomenon of the control is restrained compared to the conventional sliding mode control. The trajectory tracking performance is better than that of the fuzzy control. The designed method is easy to realize and doesn't need to construct the precise mathematical model, so, it affords an economical and convenient control method for solving the trajectory tracking problem of the greenhouse spraying mobile robot under various uncertain interferences.

With the rapid development of today’s society, the traffic environment has become more and more complex. As an essential part of intelligent vehicles, trajectory tracking has attracted significant attention for its stability and safety. It is prone to poor accuracy and instability in extreme working conditions like high speed. In this paper, a trajectory tracking control strategy to ensure lateral stability at a high speed and low attachment limit conditions is proposed for distributed drive vehicles. The model predictive controller (MPC) was used to control the front wheel angle, and the particle swarm optimization (PSO) algorithm was designed to optimize the MPC control parameters adaptively. The sliding mode controller controls the rear wheel angle, and the vehicle instability degree is judged by analyzing the β − β˙ phase plane. The controllers of different instability degrees are designed herein. Finally, a torque divider is designed to consider the actuation anti-slip. The designed controller is verified by Carsim and MATLAB-Simulink co-simulation. The results show that the trajectory tracking controller designed in this paper effectively improves the tracking accuracy under the premise of ensuring stability.

This research investigates the improvement of trajectory tracking in robotic systems by employing a model-based controller and a fractional-order iterative learning controller (FOILC). Robotic systems are subject to high uncertainty in the mathematical model, which directly impacts the quality of the model-based control strategy. This study focuses on parametric uncertainty in the robot model. The proposed solution capitalizes on the synergies between model-based control and fractional-order iterative learning strategy. FOILC controller's task is to mitigate the influence uncertainty has on trajectory tracking iteratively. Central to this investigation is the tuning of control parameters, a task undertaken by utilizing the Particle Swarm Optimization (PSO) algorithm. By leveraging PSO, the FOILC control parameters are optimized to maximize the system's performance by reducing the trajectory tracking error according to the chosen objective function. The efficiency of the proposed control algorithms is evaluated through extensive simulation on a three degree of freedom robot arm. The findings underscore the performance improvement of the tuned fractional-order iterative learning controller over its integer-order counterpart in trajectory tracking. These results not only validate the efficiency of the FOILC approach but also highlight the significance of fine parameter tuning methodologies in advancing the capabilities of robotic systems based on the dynamics model.

The development of autonomous robots for agricultural applications includes motion planning, fruit picking, and collision avoidance with surrounding environments, and these become challenging tasks. For harvesting applications, robust control of the manipulator is needed for the effective motion of the robot. Several combinations of Proportional(P)- Integrative(I)- Derivative(D) controllers are modelled and a simulation study was performed for trajectory tracking of a redundant manipulator in virtual agricultural environments. The article presents a comprehensive study on kinematic modelling and dynamic control of redundant manipulator for fruit-picking applications in virtual environments. The collisions with surrounding environment were eliminated using ‘bounding box technique’. The joint variables are obtained by constructing Inverse Kinematics (IK) problem and are determined using a classical optimization technique. Different controllers are modelled in the ‘Simulink’ environment and are tuned to generate error-free trajectory tracking during harvesting. The task space locations (TSLs) are considered as via-points, and joint variables at each TSLs are obtained by Sequential Quadratic Programming (SQP) technique. Joint-level trajectories are generated using Quintic and B-spline polynomials. For effective trajectory tracking, torque variations are controlled using the PID and Feedforward (FF) controller. The dynamic simulations of the robot manipulator are performed in Simscape Multibody software. Results show that the during the trajectory tracking of the manipulator, the Feed-forward controller performs best with Quintic polynomial trajectory.