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This work tackles the tracking control problem of robotic manipulators where the robot dynamics contains uncertain parameters and joint velocity measurements are not available. Specifically when the robotic manipulator is required to perform a periodic task repetitively, as in most industrial applications, a repetitive learning controller is proposed that does not require joint velocity measurements and can compensate the uncertainties of the robot dynamical parameters and additive disturbances caused due to the periodic joint motion. The proposed solution is achieved via the use of a novel learning component in the controller design in conjunction with a novel model‐free joint velocity observer design. The stability of the closed‐loop system and the convergence of both the joint position tracking error and the joint velocity observation error to the origin are guaranteed via Lyapunov based arguments. Experimental results performed on a 2 degree of freedom robot manipulator are presented to demonstrate the performance of the proposed observer–controller couple. [ABSTRACT FROM AUTHOR]

This work introduces an innovative robust partial state feedback controller designed for robotic manipulators actuated by brushless DC motors. The proposed controller/observer structure relies solely on actuator current and robot joint position measurements, while effectively compensating for dynamic uncertainties in both the electrical (actuator dynamics) and mechanical (robot's dynamical terms) subsystems. Specifically, a model–based robust observer, eliminating the need of joint velocity measurements, is combined with a backstepping‐type controller design. As opposed to the previous model based observers that depend on the actual model parameters, the proposed observer structure utilizes the best guest estimates of the system parameters supported with a robust compensation term. This approach eliminates the need for precise knowledge of system parameters of the observer design which is a significant improvement over most of the previous results. This approach ensures the semi‐global, uniformly ultimately bounded joint tracking error signal. The overall stability of the closed‐loop system is affirmed through Lyapunov‐based arguments. Experimental studies conducted on an in‐house‐built two‐link robotic manipulator, actuated by brushless DC motors, are presented to demonstrate the effectiveness and feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

In this study, an algorithm is presented that aims to extend the use of Euler-Lagrange back integration method to optimal control of linearizable, affine-in-the-input nonlinear systems having more than two states. The algorithm generates optimal paths by selecting a starting point iteratively on the ellipsoid close to the equilibrium point, representing the optimal borderline of the linearized system, and backward integrating the optimal nonlinear dynamics. The generated paths are then utilized in the optimal control loop. [ABSTRACT FROM AUTHOR]

In this paper, we present an inverse optimal tracking controller for a class of Euler–-Lagrange systems having uncertainties in their dynamical terms under the restriction that only the output state (i.e. position for robotic systems) is available for measurement. Specifically, a nonlinear filter is used to generate a velocity substitute, then a controller formulation ensuring a globally asymptotically stable closed-loop system while minimizing a performance index despite the presence of parametric uncertainty, is proposed. The stability proof is established using a Lyapunov analysis of the system with proposed optimal output feedback controller. Inverse optimality is derived via designing a meaningful cost function utilizing the control Lyapunov function. Numerical simulations are presented to illustrate the viability and performance of the derived controller. [ABSTRACT FROM AUTHOR]

This work presents the design and the corresponding stability analysis of a desired model-based, joint position constrained, controller formulation for robotic manipulators. Specifically, provided that the initial joint position tracking error signal starts within some predefined region, the proposed controller ensures that the joint tracking error signal remains inside this region and approaches to zero asymptotically. Extensive numerical simulations and experimental studies performed on a two-link direct-drive planar robot are provided in order to illustrate the effectiveness and feasibility of the proposed controller. [ABSTRACT FROM AUTHOR]

This paper considers the problem of position/orientation tracking control of wheeled mobile robots via visual servoing in the presence of parametric uncertainty associated with the mechanical dynamics and the camera system. Specifically, we design an adaptive controller that compensates for uncertain camera and mechanical parameters and ensures global asymptotic position/orientation tracking. Simulation and experimental results are included to illustrate the performance of the control law. Index Terms--Adaptive control, visual-servoing, wheeled mobile robot.

This paper presents the design of a differentiable, kinematic control law that achieves global asymptotic tracking. In addition, we also illustrate how the proposed kinematic controller provides global exponential tracking provided the reference trajectory satisfies a mild persistency of excitation (PE) condition. We also illustrate how the proposed kinematic controller can be slightly modified to provide for global asymptotic regulation of both the position and orientation of the mobile robot. Finally, we embed the differentiable kinematic controller inside of an adaptive controller that fosters global asymptotic tracking despite parametric uncertainty associated with the dynamic model. Experimental results are also provided to illustrate the performance of the proposed adaptive tracking controller. Index Terms--Exponential tracking, mobile robot, nonholonomic, persistency of excitation, underactuated.