The controller design for the robotic manipulator faces different challenges such as the system's nonlinearities and the uncertainties of the parameters. Furthermore, the tracking of different linear and nonlinear trajectories represents a vital role by the manipulator. This paper suggests an optimal design for the nonlinear model predictive control (NLMPC) based on a new improved intelligent technique and it is named modified multitracker optimization algorithm (MMTOA). The proposed modification of the MTOA is carried out based on opposition‐based learning (OBL) and quasi OBL approaches. This modification improves the exploration behavior of the MTOA to prevent it from becoming trapped in a local optimum. The proposed method is applied on the robotic manipulator to track different linear and nonlinear trajectories. The NLMPC parameters are tuned by the MMTOA rather than the trial and error method of the designer. The proposed NLMPC based on MMTOA is compared with the original MTOA, genetic algorithm, and cuckoo search algorithm in literature. The superiority and effectiveness of the proposed controller are confirmed to track different linear and nonlinear trajectories. Furthermore, the robustness of the proposed method is emphasized against the uncertainties of the parameters.

中文翻译：

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基于新改进多跟踪器优化算法的非线性模型预测控制器优化设计

机器人机械手的控制器设计面临不同的挑战，例如系统的非线性和参数的不确定性。此外，不同线性和非线性轨迹的跟踪代表了机械手的重要作用。本文提出了一种基于新的改进智能技术的非线性模型预测控制（NLMPC）的优化设计，它被命名为改进的多跟踪器优化算法（MMTOA）。MTOA 的拟议修改是基于基于对立的学习 (OBL) 和准 OBL 方法进行的。此修改改进了 MTOA 的探索行为，以防止其陷入局部最优。所提出的方法应用于机器人机械手以跟踪不同的线性和非线性轨迹。NLMPC 参数由 MMTOA 调整，而不是设计者的试错法。将提出的基于 MMTOA 的 NLMPC 与文献中原始的 MTOA、遗传算法和布谷鸟搜索算法进行了比较。所提出的控制器在跟踪不同的线性和非线性轨迹方面的优越性和有效性得到证实。此外，针对参数的不确定性，强调了所提出方法的鲁棒性。