Abstract
This paper presents the control modelling and synthesis using a coupled multivariable under-actuated nonlinear adaptive U-model approach for an unmanned marine robotic platform. A nonlinear marine robotics model based on the dynamic equation using the Newtonian method and derivation with respect to the kinematics equations and rigid-body mass matrixes are explained. This nonlinear marine robotics model represents the underwater thruster dynamics, marine robotics dynamics and kinematics related to the earth-fixed frame. Coupled multivariable nonlinear adaptive control synthesis using a U-model approach for the Remotely Operated Vehicle (ROV) and Unmanned Surface Vessel (USV) represent an unmanned marine robotics application. A comparison is presented for the proposed nonlinear control approach between the U-model control approach with nonlinear Fuzzy Logic Control and Sliding Mode Control for the ROV and USV platforms. The results show minimum mean square error values and tracking performance between the plant or system model with the proposed method. Lastly, robustness and stability analysis for the proposed U-Model nonlinear control approach are presented by implementing an adaptive learning rate value.
Highlights
The robotics platform has become popular in many applications that involve land, sea and space
The findings of the simulations demonstrate that adaptive coupled underactuated multivariable nonlinear control strategies can be implemented for the UMV platform with parameter uncertainties
The U-model internal model control (IMC) incorporated with the forward propagation of Radial basis function (RBF) neural networks approach includes adaptive and learning capabilities to reduce the error between the model and the multivariable nonlinear UMV system for better control synthesis to sustain the stability of the system
Summary
The robotics platform has become popular in many applications that involve land, sea and space. The shortcoming of this process is that it tends to reduce the efficiency of designing an excellent nonlinear controller This novel coupled underactuated modelling approach can overcome the nonlinearity due to the dynamic interaction between the parameters and the disturbances. It is desirable to obtain the estimation energy of the error upper bounded by disturbance energy in nature and in a practical system Another parameter that needs to have an optimum value is the learning rate that guarantees the system to converge and robustness in tracking. This ensures the tracking robustness of the U-Model under the influence of disturbance and noisy perturbations, resulting in an overall stability of the IMC loop This can be extended to find a bound on the learning rate that guarantees Eq (41). This will guarantee that the overall U-model based IMC structure will remain stable
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