This article develops a diffeomorphism-based robust bounded control (DRBC) for the permanent magnet linear synchronous motor system subject to inequality constraints (i.e., bounded input and position constraints) and uncertainties. The uncertainties, including parameter uncertainties and external disturbances, are potentially nonlinear and fast time varying. The bound of the uncertainty is described by a fuzzy set. To overcome the bounded input constraint, a robust bounded control is proposed based on a novel input diffeomorphism scheme, which is in a deterministic form and not if–then rule based. Furthermore, to overcome the bounded position constraint, a transformed system is formulated by a state diffeomorphism scheme, which transforms the bounded state-constrained system to an unconstrained one. Thus, the output of the controlled system can be restricted to a prescribed range. The DRBC guarantees both the uniform boundedness and the uniform ultimate boundedness of the transformed system. A fuzzy performance index, which combines the steady-state performance (the average fuzzy performance) and control effort, is then established based on the fuzzy description of uncertainty. As a result, the design parameter optimization can be solved by minimizing the performance index. Experimental results demonstrate that the DRBC is of superior tracking performance and robustness without violating the prescribed inequality constraints.

With environment becoming deteriorated, packaging industry is expected to take the circular economy (CE) approach through packaging design which is estimated to effect up to 80% of environmental sustainability impacts. This research aims to investigate the practical design guidance and considerations throughout the design process for developing CE-oriented packaging design solutions. Qualitative methods including interviews, workshops and secondary data were adopted to explore the circular packaging design practices which have been proven effective from 9 internationally leading firms. A ‘Design for Circular Packaging (DfCP)’ framework for packaging industry has been developed to guide firms and designers to create effective circular packaging design solutions. Major considerations in the CE packaging concepts, material selection, design process and manufacture are also addressed.

This work investigates a game-theoretic optimization approach towards robust control of uncertain dynamical systems with state and input constraints. The uncertainty involved is possibly rapidly time-varying but bounded within a prescribed fuzzy set. For this, the associated fuzzy dynamical system is appropriately established and constructed based on fuzzy set theory. To cope with the bounded state and input constraints, a novel state-and-input diffeomorphism technique is proposed, where a transformed system is formulated such that the prescribed inequality constraints are innovatively merged into the stabilization and trajectory tracking problems. Furthermore, a diffeomorphism-based robust control (DBRC) strategy is developed to ensure the uniform boundedness (UB) and uniform ultimate boundedness (UUB) of the transformed system. Under this proposed control architecture, the constraint satisfaction of the original system is thus always analytically ensured based on the rigorous properties of the diffeomorphism technique. The resulting control parameter optimization problem then has to take into account the multiple considerations (and compromise) amongst the factors of the steady-state performance; the finite convergence time; and the control effort. For this, a two-player Nash game is formulated and solved in an effective manner. The Nash equilibrium is obtained and the existence of the solution is also proved theoretically. With this methodology, and with the resulting attainment of the desired Nash equilibrium, the attendant outcome of superior system performance is achieved. Finally, numerical simulations on a steer-by-wire (SBW) system demonstrate the effectiveness of the proposed approach.

This paper investigates a cooperative motion planning problem for large-scale connected autonomous vehicles (CAVs) under limited communications, which addresses the challenges of high communication and computing resource requirements. Our proposed methodology incorporates a parallel optimization algorithm with improved consensus ADMM considering a more realistic locally connected topology network, and time complexity of $\mathcal {O}(N)$ is achieved by exploiting the sparsity in the dual update process. To further enhance the computational efficiency, we employ a lightweight evolution strategy for the dynamic connectivity graph of CAVs, and each sub-problem split from the consensus ADMM only requires managing a small group of CAVs. The proposed method implemented with the receding horizon scheme is validated thoroughly, and comparisons with existing numerical solvers and approaches demonstrate the efficiency of our proposed algorithm. Also, simulations on large-scale cooperative driving tasks involving up to 100 vehicles are performed in the high-fidelity CARLA simulator, which highlights the remarkable computational efficiency, scalability, and effectiveness of our proposed development. Demonstration videos are available at https://henryhcliu.github.io/icadmm_cmp_carla .

The management of uncertainty and inequality constraint is essential for the high-speed and high-precision motion control of a permanent magnet linear motor (PMLM). This article develops a robust control with a dual-parameters optimization for the constrained PMLM system. The uncertainties considered in this article, including parameter uncertainties and external disturbances, are bounded within prescribed fuzzy sets. The tracking error is desired to maintain within a specified range (i.e., the inequality constraint) due to the limited travel range and the demand for bounded tracking performance of the PMLM system. By employing a state transformation, the bounded state is converted into an unbounded one. Therefore, the inequality constraint is creatively integrated into the trajectory tracking control problem. Based on the transformed system, an optimal robust control is established which can be divided into two steps. First, a deterministic robust control is proposed which guarantees the practical stability: uniform boundedness and uniform ultimate boundedness. Second, a two-player Stackelberg game is formulated to seek the optimal control parameters. The cost function of each player in the game reflects both the control performance and the control cost such that the tradeoff problem between the two conflicting criteria is further clarified. Finally, experimental results demonstrate the superior performance of the proposed optimal robust control over two existing control schemes.

In this letter, we propose a robust constraint-following control approach for uncertain mechanical systems under both equality and inequality constraints. Particularly, both the global and local inequality constraints are systematically incorporated into the Udwadia-Kalaba (U-K) equation leveraging the diffeomorphism technique, wherein a novel smooth approximation of the local inequality constraints is proposed to address the non-differentiability resulting from its spatiotemporal dependence nature. Based on this development, the generalized U-K equation is mathematically established. With this, we develop a robust constraint-following control strategy to ensure satisfying system performance in the presence of uncertainties and various constraints. Moreover, by Lyapunov minimax approach, the proposed control strategy guarantees both uniform boundedness (UB) and uniform ultimate boundedness (UUB) of the system. Finally, numerical simulations on the lateral motion control of an autonomous vehicle demonstrate the effectiveness of the proposed approach.