Adaptive Control Design for Underactuated Cranes with Guaranteed Transient Performance: Theoretical Design and Experimental Verification

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Fast and accurate trajectory tracking control of an autonomous surface vehicle with unmodeled dynamics and disturbances

In this paper, fast and accurate trajectory tracking control of an autonomous surface vehicle (ASV) with complex unknowns including unmodeled dynamics, uncertainties and/or unknown disturbances is addressed within a proposed homogeneity-based finite-time control (HFC) framework. Major contributions are as follows: (1) In the absence of external disturbances, a nominal HFC framework is established to achieve exact trajectory tracking control of an ASV, whereby global finitetime stability is ensured by combining homogeneous analysis and Lyapunov approach; (2) Within the HFC scheme, a finite-time disturbance observer (FDO) is further nested to rapidly and accurately reject complex disturbances, and thereby contributing to an FDO-based HFC (FDO-HFC) scheme which can realize exactness of trajectory tracking and disturbance observation; (3) Aiming to exactly deal with complicated unknowns including unmodeled dynamics and/or disturbances, a finite-time unknown observer (FUO) is deployed as a patch for the nominal HFC framework, and eventually results in an FUO-based HFC (FUOHFC) scheme which guarantees that accurate trajectory tracking can be achieved for an ASV under harsh environments. Simulation studies and comprehensive comparisons conducted on a benchmark ship demonstrate the effectiveness and superiority of the proposed HFC schemes

Nonlinear Model-Based Control for Neuromuscular Electrical Stimulation

Neuromuscular electrical stimulation (NMES) is a technology where skeletal muscles are externally stimulated by electrodes to help restore functionality to human limbs with motor neuron disorder. This dissertation is concerned with the model-based feedback control of the NMES quadriceps muscle group-knee joint dynamics. A class of nonlinear controllers is presented based on various levels of model structures and uncertainties. The two main control techniques used throughout this work are backstepping control and Lyapunov stability theory. In the first control strategy, we design a model-based nonlinear control law for the system with the exactly known passive mechanical that ensures asymptotical tracking. This first design is used as a stepping stone for the other control strategies in which we consider that uncertainties exist. In the next four control strategies, techniques for adaptive control of nonlinearly parameterized systems are applied to handle the unknown physical constant parameters that appear nonlinearly in the model. By exploiting the Lipschitzian nature or the concavity/convexity of the nonlinearly parameterized functions in the model, we design two adaptive controllers and two robust adaptive controllers that ensure practical tracking. The next set of controllers are based on a NMES model that includes the uncertain muscle contractile mechanics. In this case, neural network-based controllers are designed to deal with this uncertainty. We consider here voltage inputs without and with saturation. For the latter, the Nussbaum gain is applied to handle the input saturation. The last two control strategies are based on a more refined NMES model that accounts for the muscle activation dynamics. The main challenge here is that the activation state is unmeasurable. In the first design, we design a model-based observer that directly estimates the unmeasured state for a certain activation model. The second design introduces a nonlinear filter with an adaptive control law to handle parametric uncertainty in the activation dynamics. Both the observer- and filter-based, partial-state feedback controllers ensure asymptotical tracking. Throughout this dissertation, the performance of the proposed control schemes are illustrated via computer simulations

Adaptive fuzzy control for coordinated multiple robots with constraint using impedance learning

In this paper, we investigate fuzzy neural network (FNN) control using impedance learning for coordinated multiple constrained robots carrying a common object in the presence of the unknown robotic dynamics and the unknown environment with which the robot comes into contact. First, an FNN learning algorithm is developed to identify the unknown plant model. Second, impedance learning is introduced to regulate the control input in order to improve the environment-robot interaction, and the robot can track the desired trajectory generated by impedance learning. Third, in light of the condition requiring the robot to move in a finite space or to move at a limited velocity in a finite space, the algorithm based on the position constraint and the velocity constraint are proposed, respectively. To guarantee the position constraint and the velocity constraint, an integral barrier Lyapunov function is introduced to avoid the violation of the constraint. According to Lyapunov's stability theory, it can be proved that the tracking errors are uniformly bounded ultimately. At last, some simulation examples are carried out to verify the effectiveness of the designed control

Fixed-time safe tracking control of uncertain high-order nonlinear pure-feedback systems via unified transformation functions

summary:In this paper, a fixed-time safe control problem is investigated for an uncertain high-order nonlinear pure-feedback system with state constraints. A new nonlinear transformation function is firstly proposed to handle both the constrained and unconstrained cases in a unified way. Further, a radial basis function neural network is constructed to approximate the unknown dynamics in the system and a fixed-time dynamic surface control (FDSC) technique is developed to facilitate the fixed-time control design for the uncertain high-order pure-feedback system. Combined with the proposed unified transformation function and the FDSC technique, an adaptive fixed-time control strategy is proposed to guarantee the fixed-time tracking. The novel original results of the paper allow to design the independent unified flexible fixed-time control strategy taking into account the actual possible constraints, either present or missing. Numerical examples are presented to demonstrate the proposed fixed-time tracking control strategy