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We propose a novel robust Model Predictive Control (MPC) scheme for nonlinear multi-input multi-output systems of relative degree one with stable internal dynamics. The proposed algorithm is a combination of funnel MPC, i.e., MPC with a particular stage cost, and the model-free adaptive funnel controller. The new robust funnel MPC scheme guarantees output tracking of reference signals within prescribed performance bounds--even in the presence of unknown disturbances and a structural model-plant mismatch. We show initial and recursive feasibility of the proposed control scheme without imposing terminal conditions or any requirements on the prediction horizon. Moreover, we allow for model updates at runtime. To this end, we propose a proper initialization strategy, which ensures that recursive feasibility is preserved. Finally, we validate the performance of the proposed robust MPC scheme by simulations. [ABSTRACT FROM AUTHOR]

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We propose a novel sampled-data output-feedback controller for nonlinear systems of arbitrary relative degree that ensures reference tracking within prescribed error bounds. We provide explicit bounds on the maximum input signal and the required uniform sampling time. A key strength of this approach is its capability to serve as a safety filter for various learning-based controller designs, enabling the use of learning techniques in safety-critical applications. We illustrate its versatility by integrating it with two different controllers: a reinforcement learning controller and a non-parametric predictive controller based on Willems et al.'s fundamental lemma. Numerical simulations illustrate effectiveness of the combined controller design. [ABSTRACT FROM AUTHOR]

The model predictive control (MPC) scheme funnel MPC enables output tracking of smooth reference signals with prescribed error bounds for nonlinear multi-input multi-output systems with stable internal dynamics. Earlier works achieved the control objective for system with relative degree restricted to one or incorporated additional feasibility constraints in the optimal control problem. Here we resolve these limitations by introducing a modified stage cost function relying on a weighted sum of the tracking error derivatives. The weights need to be sufficiently large and we state explicit lower bounds. Under these assumptions we are able to prove initial and recursive feasibility of the novel funnel MPC scheme for systems with arbitrary relative degree — without requiring any terminal conditions, a sufficiently long prediction horizon or additional output constraints. [ABSTRACT FROM AUTHOR]

This work proposes Autonomous Iterative Motion Learning (AI-MOLE), a method that enables systems with unknown, nonlinear dynamics to autonomously learn to solve reference tracking tasks. The method iteratively applies an input trajectory to the unknown dynamics, trains a Gaussian process model based on the experimental data, and utilizes the model to update the input trajectory until desired tracking performance is achieved. Unlike existing approaches, the proposed method determines necessary parameters automatically, i.e., AI-MOLE works plug-and-play and without manual parameter tuning. Furthermore, AI-MOLE only requires input/output information, but can also exploit available state information to accelerate learning. While other approaches are typically only validated in simulation or on a single real-world testbed using manually tuned parameters, we present the unprecedented result of validating the proposed method on three different real-world robots and a total of nine different reference tracking tasks without requiring any a priori model information or manual parameter tuning. Over all systems and tasks, AI-MOLE rapidly learns to track the references without requiring any manual parameter tuning at all, even if only input/output information is available. [ABSTRACT FROM AUTHOR]

In this paper, we propose a performance-based activation mechanism for anti-windup compensation. The signals representing the transient performance of the closed-loop system are passed through a static performance compensator and then injected into the traditional anti-windup activation module and a performance-based anti-windup design framework is developed. The resulting equivalent controller contains the information of the anti-windup compensator and has the potential for improving the performance of the closed-loop systems. Under this new anti-windup design framework, sufficient conditions are established for estimating the domain of attraction and the nonlinear L 2 gain of saturated systems. Based on these conditions, we formulate optimization problems to determine the optimal gains of anti-windup and performance compensators for enlarging the domain of attraction and reducing the nonlinear L 2 gain. Simulation results show that, compared with the existing methods, our proposed approach has the ability to acquire a significantly larger estimate of the domain of attraction, a significantly tighter estimate of the nonlinear L 2 gain, and significantly higher transient quality in reference tracking. [ABSTRACT FROM AUTHOR]