Abstract
The nonlinearities of the robotic manipulators and the uncertainties of their parameters represent big challenges against the controller design. Moreover, the tracking of regular and irregular trajectories with fewer overshoots, short settling time, and small steady-state error is the main target for the robotic response. The model predictive control (MPC) is an efficient controller to handle the performance requirements. However, the conventional MPC requires the linearization of the system model. The linearization of the model does not cover all dynamics of the robotic system. Thus, this paper introduces the nonlinear MPC (NLMPC) as a proper control method for the nonlinear systems instead of the conventional MPC. Specifically, this work proposes the use of NLMPC for controlling robotic manipulators. However, the NLMPC gains need proper tuning to attain good performance rather than the conventional methods. The neural network algorithm (NNA) considers a sufficient adaptive intelligent technique that can be utilized for this purpose. The restriction in a local optimum reveals the main issue versus artificial intelligence techniques. This paper suggests a new improvement to reinforce the exploration behavior of the NNA to overcome the local restriction issue. This modification is carried out by utilizing the polynomial mutation as an effective method to promise the exploration manner of the intelligence techniques. The proposed system can estimate all states from only the output to reduce the cost of the required sensors to measure all states. The results confirm the superiority of the proposed systems with the estimator with negligible change in the output response. The proposed modified NNA (MNNA) is evaluated with the main NNA, genetic algorithm-based PID control scheme, besides the cuckoo search algorithm-based PID control scheme from other works. The results confirm the robustness and effectiveness of the suggested MNNA-based NLMPC to track regular and irregular trajectories compared with other techniques.
Highlights
The robotic manipulator is utilized for diverse purposes, e.g. aiding the industry and human routine duties
The previous objective function is presented as the figure of demerit (FOD) performance index in [50], the first formulation for it is created by Gaing [51]
The results of the proposed nonlinear MPC (NLMPC) based on modified NNA (MNNA) are affirmed by comparing them with the genetic algorithm (GA)-based PID control scheme in [42], the cuckoo search algorithm (CSA)-based PID control scheme in [43]
Summary
The robotic manipulator is utilized for diverse purposes, e.g. aiding the industry and human routine duties. The linearization is created based on the approximated procedure and it does not take into account the full dynamics of the real system Among these control techniques, the model predictive control (MPC) provided good performance for a lot of engineering applications [14]. This paper introduces an intelligent design for the NLMPC parameters based on a new modified neural network algorithm (NNA) rather than the conventional methods. To track regular and irregular trajectories by the robotic manipulator; The proposed MNNA is applied to adjust the parameters of the robot control scheme rather than the conventional methods; The suggested MNNA-based NLMPC method is evaluated with the main NNA [33], the GA-PID control scheme [42], and CSA-PID control [43]; The robustness and efficiency of the suggested technique are confirmed to track regular and irregular trajectories.
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