Abstract
A novel model predictive control- (MPC-) based trajectory tracking controller for mobile robot is proposed using the event-triggering mechanism, and the aim is to solve the problem that the MPC optimization problem requires a large amount of online computation and communication resources. This method includes two different event-triggering strategies, namely, the event-triggering based on threshold curve and the event-triggering based on threshold band. The selection of the triggering threshold is achieved by applying the statistical method to the historical data of the trajectory tracking of the mobile robot under the classic MPC method. Simulation and experimental tests illustrate that the proposed approach is able to significantly reduce the computation and communication burdens without affecting the control performance. Furthermore, the experimental results show that compared with the classic MPC-based tracking method, the proposed two event-triggering strategies can reduce 28.1% and 75.7% of the computation load and 0.886 s and 2.385 s communication time.
Highlights
With the rapid development of computer engineering, electronic engineering, network communication engineering, and other disciplines, mobile robot technology has made great progress and has been widely used in scientific exploration, national defense and military, risk relief, life services, and other fields. erefore, the trajectory tracking method of mobile robot using a conventional control strategy is difficult to achieve the desired control performance, so it is urgent to study the trajectory tracking strategy of a robot based on advanced control algorithms.In recent years, many research achievements have been made on the trajectory tracking control of mobile robots. e mainstream tracking control algorithms at this stage included sliding mode control, model predictive control (MPC), and optimal control [1,2,3,4]
In [8], the authors designed a model predictive control- (MPC-)based trajectory tracking control method, which could ensure that the unmanned vehicle track the reference trajectory quickly and stably; the distance error and heading error are in a reasonable range, and the real-time performance meet the requirements. [9] proposed a real-time optimization scheme to reduce the control horizon and the control update frequency for MPCbased robot path tracking control, which could balance the realtime requirements and control accuracy
An MPC-based path tracking controller for the constrained under-driven autonomous underwater vehicle (AUV) system is designed in [10], and it is pointed out that a long prediction horizon could be chosen to ensure the final convergence of the control system
Summary
With the rapid development of computer engineering, electronic engineering, network communication engineering, and other disciplines, mobile robot technology has made great progress and has been widely used in scientific exploration, national defense and military, risk relief, life services, and other fields. erefore, the trajectory tracking method of mobile robot using a conventional control strategy is difficult to achieve the desired control performance, so it is urgent to study the trajectory tracking strategy of a robot based on advanced control algorithms. [16] proposed an event-based MPC approach for linear discrete systems subject to external perturbation, in which the optimization problem included a time-vary tightened state constraint. It should be emphasized that compared with the standard MPC applied in practical robots, the above eventtriggered MPC algorithms including additional items such as terminal and tightened constraints, which cannot be directly adopted, and the event-triggering mechanism should be further studied for MPC-based tracking control of robot systems. Aiming at solving the abovementioned problems, this study proposes a more practical event-triggered MPC-based trajectory tracking method for mobile robot subject to external disturbances. On the one hand, compared with the event-triggered MPC with terminal cost/ constraint, the proposed method employed the standard robot kinematic model with no extra assumption and has no additional terms in the MPC cost function, which can be directly applied to real robot systems. On the other hand, compared with event-triggered MPC with standard MPC framework, the proposed technique incorporated the external disturbances into the development of the triggering condition and adopted statistical methods to construct two different kinds of triggering strategies for performance improvement (Section 5.2)
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