Author name cluster

Zhong-Ping Jiang

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4 papers

2 author rows

ICRA Conference 2021 Conference Paper

Balance Control of a Novel Wheel-legged Robot: Design and Experiments

Shuai Wang 0007
Leilei Cui 0002
Jingfan Zhang
Jie Lai
Dongsheng Zhang
Ke Chen
Yu Zheng 0001
Zhengyou Zhang

This paper presents a balance control technique for a novel wheel-legged robot. We first derive a dynamic model of the robot and then apply a linear feedback controller based on output regulation and linear quadratic regulator (LQR) methods to maintain the standing of the robot on the ground without moving backward and forward mightily. To take into account nonlinearities of the model and obtain a large domain of stability, a nonlinear controller based on the interconnection and damping assignment - passivity-based control (IDA-PBC) method is exploited to control the robot in more general scenarios. Physical experiments are performed with various control tasks. Experimental results demonstrate that the proposed linear output regulator can maintain the standing of the robot, while the proposed nonlinear controller can balance the robot under an initial starting angle far away from the equilibrium point, or under a changing robot height.

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AAAI Conference 2021 Conference Paper

Robust Reinforcement Learning: A Case Study in Linear Quadratic Regulation

Bo Pang
Zhong-Ping Jiang

This paper studies the robustness of reinforcement learning algorithms to errors in the learning process. Specifically, we revisit the benchmark problem of discrete-time linear quadratic regulation (LQR) and study the long-standing open question: Under what conditions is the policy iteration method robustly stable from a dynamical systems perspective? Using advanced stability results in control theory, it is shown that policy iteration for LQR is inherently robust to small errors in the learning process and enjoys small-disturbance input-to-state stability: whenever the error in each iteration is bounded and small, the solutions of the policy iteration algorithm are also bounded, and, moreover, enter and stay in a small neighbourhood of the optimal LQR solution. As an application, a novel off-policy optimistic least-squares policy iteration for the LQR problem is proposed, when the system dynamics are subjected to additive stochastic disturbances. The proposed new results in robust reinforcement learning are validated by a numerical example.

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IROS Conference 2020 Conference Paper

Gain Scheduled Controller Design for Balancing an Autonomous Bicycle

Shuai Wang 0007
Leilei Cui 0002
Jie Lai
Sicheng Yang
Xiangyu Chen 0001
Yu Zheng 0001
Zhengyou Zhang
Zhong-Ping Jiang

In this paper, the gain scheduling technique is applied to design a balance controller for an autonomous bicycle with an inertia wheel. Previously, two different balance controllers are needed depending on whether the bicycle is stationary or dynamic. The switch between the two different controllers may cause the instability of the autonomous bicycle. Our proposed gain scheduled controller can balance the autonomous bicycle in both stationary and dynamic cases. A physical system is built and experiments are carried out to demonstrate the effectiveness of the gain scheduled controller.

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IROS Conference 2020 Conference Paper

Nonlinear Balance Control of an Unmanned Bicycle: Design and Experiments

Leilei Cui 0002
Shuai Wang 0007
Jie Lai
Xiangyu Chen 0001
Sicheng Yang
Zhengyou Zhang
Zhong-Ping Jiang

In this paper, nonlinear control techniques are exploited to balance an unmanned bicycle with enlarged stability domain. We consider two cases. For the first case when the autonomous bicycle is balanced by the flywheel, the steering angle is set to zero, and the torque of the flywheel is used as the control input. The controller is designed based on the Interconnection and Damping Assignment Passivity Based Control (IDA-PBC) method. For the second case when the bicycle is balanced by the handlebar, the bicycle’s velocity is high, and the flywheel is turned off. The angular velocity of the handlebar is used as the control input and the balance controller is designed based on feedback linearization. In these cases, the global stability of the closed-loop unmanned bicycle is theoretically proved based on Lyapunov theory. The experiments are conducted to validate the efficacy of the proposed nonlinear balance controllers.

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