Adaptive CLF-MPC With Application to Quadrupedal Robots

Minniti, Maria Vittoria; Grandia, Ruben; Farshidian, Farbod; Hutter, Marco

doi:10.1109/lra.2021.3128697

Cited by 39 publications

(13 citation statements)

References 27 publications

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“…In addition, 𝑬 𝒄 = 𝛾𝑬 𝒆 +(1 − 𝛾) 𝑬 𝒓 , and 𝑉 (•) is the Lyapunov-like function. According to [20], [27], if 𝑉 ≤ −𝑄, 𝑬 𝒆 and 𝑬 𝒓 will gradually converges to 0. 𝑬 𝒆 converges to 0 also means that the uncertainties have been fully compensated according to III.A and III.B.…”

Section: Stability Analysismentioning

confidence: 99%

“…In terms of AGV, [9], [19] design neural network or Kalman filter to estimate the uncertainties in a single-level system. [20] combines their previous control Lyapunov function-MPC (CLF-MPC) with a uncertainties estimation matrix. The problem is that this method only suits the dynamic-model-based controller as its pivotal regressor and skew-symmetry matrix come from the significant properties of the dynamic model that is proposed in [21].…”

Section: Introductionmentioning

confidence: 99%

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Direction and Trajectory Tracking Control for Nonholonomic Spherical Robot by Combining Sliding Mode Controller and Model Prediction Controller

Liu

Wang

Guan

et al. 2022

IEEE Robot. Autom. Lett.

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Owing to uncertainties in both kinematics and dynamics, the current trajectory tracking framework for mobile robots like spherical robots cannot function effectively on multiple terrains, especially uneven and unknown ones. Since this is a prerequisite for robots to execute tasks in the wild, we enhance our previous hierarchical trajectory tracking framework to handle this issue. First, a modified adaptive RBF neural network (RBFNN) is proposed to represent all uncertainties in kinodynamics. Then the Lyapunov function is utilized to design its adaptive law, and a variable step-size algorithm is employed in the weights update procedure to accelerate convergence and improve stability. Hence, a new adaptive model prediction controlbased instruction planner (VAN-MPC) is proposed. Without modifying the bottom controllers, we finally develop the multi-terrain trajectory tracking framework by employing the new instruction planner VAN-MPC. The practical experiments demonstrate its effectiveness and robustness.

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Section: Stability Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Direction and Trajectory Tracking Control for Nonholonomic Spherical Robot by Combining Sliding Mode Controller and Model Prediction Controller

Liu

Wang

Guan

et al. 2022

IEEE Robot. Autom. Lett.

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“…The control problems of hybrid manipulation have been analyzed through different aspects, such as control equilibrium [13], [24], flatness [25], [26] and controllability [27]. The adaptive control problem for cable-based manipulation tasks with a single quadrupedal robot has also been discussed in [28]. In the path planning problem for hybrid manipulation, mixed-integer optimization [29] or general nonlinear programming with force-based complementarity constraints [30] can be applied but these approaches suffer from nonlinear hybrid dynamics and can only be solved offline.…”

Section: A Related Workmentioning

confidence: 99%

Collaborative Navigation and Manipulation of a Cable-towed Load by Multiple Quadrupedal Robots

Yang¹,

Sue²,

Li³

et al. 2022

Preprint

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This paper tackles the problem of robots collaboratively towing a load with cables to a specified goal location while avoiding collisions in real time. The introduction of cables (as opposed to rigid links) enables the robotic team to travel through narrow spaces by changing its intrinsic dimensions through slack/taut switches of the cable. However, this is a challenging problem because of the hybrid mode switches and the dynamical coupling among multiple robots and the load. Previous attempts at addressing such a problem were performed offline and do not consider avoiding obstacles online. In this paper, we introduce a cascaded planning scheme with a parallelized centralized trajectory optimization that deals with hybrid mode switches. We additionally develop a set of decentralized planners per robot, which enables our approach to solve the problem of collaborative load manipulation online. We develop and demonstrate one of the first collaborative autonomy framework that is able to move a cable-towed load, which is too heavy to move by a single robot, through narrow spaces with real-time feedback and reactive planning in experiments.

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“…In our previous work [1] we presented a Nonlinear Model Predictive Control (NMPC) formulation, which runs at 25 Hz and allows the Hydraulically actuated Quadruped (HyQ) [6] robot to perform omni-directional motions and detect a pallet and step on it with improved leg mobility. Minniti et al [7] integrated Control Lyapunov Functions into their MPC to guarantee stability when the robot Fig. 1: Overview of our locmotion framework.…”

Section: A Related Workmentioning

confidence: 99%

Optimization-Based Reference Generator for Nonlinear Model Predictive Control of Legged Robots

Bratta¹,

Focchi²,

Rathod³

et al. 2022

Preprint

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Model Predictive Control (MPC) approaches are widely used in robotics, since they allow to compute updated trajectories while the robot is moving. They generally require heuristic references for the tracking terms and proper tuning of parameters of the cost function in order to obtain good performance. When for example, a legged robot has to react to disturbances from the environment (e.g., recover after a push) or track a certain goal with statically unstable gaits, the effectiveness of the algorithm can degrade. In this work we propose a novel optimization-based Reference Generator, named Governor, which exploits a Linear Inverted Pendulum model to compute reference trajectories for the Center of Mass, while taking into account the possible under-actuation of a gait (e.g. in a trot). The obtained trajectories are used as references for the cost function of the Nonlinear MPC presented in our previous work [1]. We also present a formulation that can guarantee a certain response time to reach a goal, without the need to tune the weights of the cost terms. In addition, foothold locations are corrected to drive the robot towards the goal. We demonstrate the effectiveness of our approach both in simulations and experiments in different scenarios with the Aliengo robot.

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Adaptive CLF-MPC With Application to Quadrupedal Robots

Cited by 39 publications

References 27 publications

Direction and Trajectory Tracking Control for Nonholonomic Spherical Robot by Combining Sliding Mode Controller and Model Prediction Controller

Direction and Trajectory Tracking Control for Nonholonomic Spherical Robot by Combining Sliding Mode Controller and Model Prediction Controller

Collaborative Navigation and Manipulation of a Cable-towed Load by Multiple Quadrupedal Robots

Optimization-Based Reference Generator for Nonlinear Model Predictive Control of Legged Robots

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