Heading Control System Design for a Micro-USV Based on an Adaptive Expert S-PID Algorithm

Miao, Runlong; Dong, Zaopeng; Wan, Lei; Zeng, Jiangfeng

doi:10.2478/pomr-2018-0049

Cited by 23 publications

(16 citation statements)

References 16 publications

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“…where W T is output of GD-FNN_B; KE is the pre-adjusted output of improved bacterial foraging optimization algorithm, which could be designed as: (19) Combining (2), (17) and (18), the UMV heading control error system can be designed as follows:…”

Section: Umv Heading Controller Designmentioning

confidence: 99%

“…Proportion integral derivative (PID) algorithm and various improved PID algorithm are the most applied algorithms in UMV heading control, at present, many researchers have done relevant research and obtained rich research results [13]- [23]. Many improved algorithms, such as Kalman filter [13], H-infinity control [14], self adaptive fuzzy control [15], [16], backstepping control [17], constrained self-tuning control [18], expert control and S surface control [19], adaptive control and hybrid control [20]- [23] are integrated into PID algorithm to improve the reliability, stability and accuracy of the heading control system and the whole UMV control system. A numerical method for minimum time heading control of an fixed speed UMV is proposed by Rhoads et al [24], while a Sugeno fuzzy inference system and Kalman filter are integrated into the heading control system by Toe et al [25], and a self-tuning fuzzy control algorithm is proposed by Fang et al [26].…”

Section: Introductionmentioning

confidence: 99%

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Heading Control of Unmanned Marine Vehicles Based on an Improved Robust Adaptive Fuzzy Neural Network Control Algorithm

et al. 2019

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A robust adaptive fuzzy neural network control (RAFNNC) algorithm is proposed based on a generalized dynamic fuzzy neural network (GDFNN), proportion-integral-differential (PID), and improved bacterial foraging optimization (BFO) algorithm, for heading the control of the unmanned marine vehicle (UMV) in the presence of a complex environment disturbance. First, the inverse dynamic model of the motion control of UMV is established based on the GDFNN for the uncertain disturbance caused by the complex environment disturbance. Then, the adaptive rate of the fuzzy neural network is designed based on the error between the real UMV heading angle and designed reference heading angle, so as to further adjust the weight parameter of the GDFNN, and then, the output control value of the neural network is obtained. In order to further reduce the computation amount and computation time of the RAFNNC, the parameters of the PID control algorithm were optimized in advance by using the improved BFO algorithm. The fractal dimension step size and the intelligent probe operation are integrated into the BFO algorithm, in order to optimize the operation time and accuracy of the algorithm. Stability of the designed RAFNNC algorithm for the heading control of the UMV in the presence of complex marine environment disturbance is proved by the Lyapunov stability theory, and the effectiveness and accuracy of the control algorithm proposed are verified by semi-physical simulation experiment carried out in our laboratory. INDEX TERMS Unmanned marine vehicle (UMV), heading control, robust adaptive fuzzy neural network control (RAFNNC), generalized dynamic fuzzy neural network (GDFNN), bacterial foraging optimization (BFO).

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Section: Umv Heading Controller Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Heading Control of Unmanned Marine Vehicles Based on an Improved Robust Adaptive Fuzzy Neural Network Control Algorithm

et al. 2019

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“…[1][2][3][4] Although much advancement have been realized in this area, the demand for more advanced navigation, guidance, and control systems for UMVs continues to grow, as more and more vehicle autonomy is required. [5][6][7][8] In practical implementation, many UMVs are designed of underactuated configurations due to practical considerations, for example, reducing weight and/or cost. [9][10][11][12] Point stabilization is the most basic case of the motion control of UMV, where the desired position and attitude are chosen to be constant, and it is the important foundation of path following and trajectory tracking of UMV.…”

Section: Introductionmentioning

confidence: 99%

Robust exponential point stabilization control of the high-speed underactuated unmanned marine vehicle with model asymmetry

Dong

Wang

Song

et al. 2019

International Journal of Advanced Robotic Systems

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Point stabilization control of a class of asymmetric underactuated high-speed unmanned marine vehicle is discussed, and a robust exponential stabilization control algorithm is proposed based on homogeneous theory, average system theory, and nonlinear backstepping technology. Firstly, point stabilization control problem of a high-speed underactuated unmanned marine vehicle with model asymmetry is formulated, and then global differential homeomorphism transformation is designed, in order to overcome the difficulties caused by unmanned marine vehicle with model asymmetry. Secondly, the control system is transformed into the standard form of homogeneous interference system by output state variable transformation design and input transformation design. A novel interference function is designed, and then difficulties caused by the higher order velocities in damping coefficients are solved, via homogeneous stability design and homogeneity degree analyzing the expansion of the designed new state variables. Thirdly, by introducing the virtual input of backstepping and the average system theory, point stabilization controller for the underactuated high-speed unmanned marine vehicle is proposed based on homogeneous theory, which could achieve global and periodic time-varying robust exponential stability, and then stability of the point stabilization control algorithm is proved by using homogeneous stability theory and average system stability theory. At last, the effectiveness and accuracy of the control algorithm proposed is verified by semi-physical simulation experiment carried out in our laboratory.

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“…Wu et al [21] studied the heading control problem of a USV based on the S-surface control method and carried out experimental verifications. Miao et al [22], by combining expert control technology, S-plane, and PID control algorithms, developed an adaptive expert S-PID control algorithm for the heading control of a micro-USV, and field tests were conducted.…”

Section: Introductionmentioning

confidence: 99%

Heading Control of Unmanned Surface Vehicle With Variable Output Constraint Model-Free Adaptive Control Algorithm

Jiang

Liao

et al. 2019

IEEE Access

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Based on model-free adaptive control (MFAC) theory, this paper presents a variable output constraint MFAC (VOC-MFAC) algorithm to enhance the robustness of an unmanned surface vehicle's (USV's) heading subsystem. The contributions of this paper are as follows. First, a controller output constraint function is proposed to solve the system's control performance sensitivity to the redefined output gain when the redefined compact format model free adaptive control (RO-CFDL-MFAC) method is used to control an unmanned surface vehicle's heading. Second, the compact format dynamic linearization data models for a USV's angular velocity subsystem and heading subsystem are established, and the convergence of the closed-loop system under environmental disturbances is proven through rigorous theoretical analysis. Finally, the control algorithm proposed in this paper is simulated and tested in the field using the ''Dolphin IB'' unmanned surface vehicle platform developed by our research group, and the effectiveness of the VOC-MFAC algorithm is verified by the experimental results. INDEX TERMS Unmanned surface vehicle, heading control, model-free adaptive control, output constraint function, field trial.

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Heading Control System Design for a Micro-USV Based on an Adaptive Expert S-PID Algorithm

Cited by 23 publications

References 16 publications

Heading Control of Unmanned Marine Vehicles Based on an Improved Robust Adaptive Fuzzy Neural Network Control Algorithm

Heading Control of Unmanned Marine Vehicles Based on an Improved Robust Adaptive Fuzzy Neural Network Control Algorithm

Robust exponential point stabilization control of the high-speed underactuated unmanned marine vehicle with model asymmetry

Heading Control of Unmanned Surface Vehicle With Variable Output Constraint Model-Free Adaptive Control Algorithm

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