How Imitation Learning and Human Factors Can Be Combined in a Model Predictive Control Algorithm for Adaptive Motion Planning and Control

Model Predictive Control (MPC) algorithms typically use the classical L2 cost function, which minimises squared differences of predicted control errors. Such an approach has good numerical properties, but the L1 norm that measures absolute values of the control errors gives better control quality. If a nonlinear model is used for prediction, the L1 norm leads to a difficult, nonlinear, possibly non-differentiable cost function. A computationally efficient alternative is discussed in this work. The solution used consists of two concepts: (a) a neural approximator is used in place of the non-differentiable absolute value function; (b) an advanced trajectory linearisation is performed on-line. As a result, an easy-to-solve quadratic optimisation task is obtained in place of the nonlinear one. Advantages of the presented solution are discussed for a simulated neutralisation benchmark. It is shown that the obtained trajectories are very similar, practically the same, as those possible in the reference scheme with nonlinear optimisation. Furthermore, the L1 norm even gives better performance than the classical L2 one in terms of the classical control performance indicator that measures squared control errors.

Section: Introductionmentioning

confidence: 99%

Computationally Efficient Nonlinear Model Predictive Control Using the L1 Cost-Function

Ławryńczuk

Nebeluk

2021

“…The LSTM layer of the network is typically added to a fully connected layer (Figure 3), with weight matrix W y with a dimensionality of 1 × n N and bias b y . Finally, the computation of the network's output at time instant k can be expressed as One can represent Equations ( 10)- (15) in scalar form, which will prove useful for the derivation of the MPC algorithm considered in Section 3. The scalar form expressions for the n-th elements of the gate and state vectors are…”

Section: Lstm Sub-modelmentioning

confidence: 99%

“…In the domain of vehicles, innovative solutions have emerged. The authors of [15] introduce a real-world example where a vehicle employs an external camera to detect obstacles and lane positions on the road. Additionally, it utilizes external rear-corner radars to identify objects approaching from the rear.…”

Section: Introductionmentioning

confidence: 99%

Long Short-Term Memory Neural Networks for Modeling Dynamical Processes and Predictive Control: A Hybrid Physics-Informed Approach

Zarzycki,

Ławryńczuk

2023

This work has two objectives. Firstly, it describes a novel physics-informed hybrid neural network (PIHNN) model based on the long short-term memory (LSTM) neural network. The presented model structure combines the first-principle process description and data-driven neural sub-models using a specialized data fusion block that relies on fuzzy logic. The second objective of this work is to detail a computationally efficient model predictive control (MPC) algorithm that employs the PIHNN model. The validity of the presented modeling and MPC approaches is demonstrated for a simulated polymerization reactor. It is shown that the PIHNN structure gives very good modeling results, while the MPC controller results in excellent control quality.

“…GAIL allows agents to overcome exploration challenges through the use of expert demonstrations, while also making it possible to achieve high asymptotic performance. GAIL has been applied in the certain fields, such as autonomous driving, mobile robot map coverage, robot motion planning, and joint control, and has achieved good results [ 45 , 46 ].…”

Section: Introductionmentioning

confidence: 99%

End-to-End AUV Motion Planning Method Based on Soft Actor-Critic

Sun

Wang

et al. 2021

This study aims to solve the problems of poor exploration ability, single strategy, and high training cost in autonomous underwater vehicle (AUV) motion planning tasks and to overcome certain difficulties, such as multiple constraints and a sparse reward environment. In this research, an end-to-end motion planning system based on deep reinforcement learning is proposed to solve the motion planning problem of an underactuated AUV. The system directly maps the state information of the AUV and the environment into the control instructions of the AUV. The system is based on the soft actor–critic (SAC) algorithm, which enhances the exploration ability and robustness to the AUV environment. We also use the method of generative adversarial imitation learning (GAIL) to assist its training to overcome the problem that learning a policy for the first time is difficult and time-consuming in reinforcement learning. A comprehensive external reward function is then designed to help the AUV smoothly reach the target point, and the distance and time are optimized as much as possible. Finally, the end-to-end motion planning algorithm proposed in this research is tested and compared on the basis of the Unity simulation platform. Results show that the algorithm has an optimal decision-making ability during navigation, a shorter route, less time consumption, and a smoother trajectory. Moreover, GAIL can speed up the AUV training speed and minimize the training time without affecting the planning effect of the SAC algorithm.