This paper presents a metaheuristic-based control framework, called Beetle Antennae Olfactory Recurrent Neural Network (BAORNN), for simultaneous tracking control and obstacle avoidance of a redundant manipulator. The ability to avoid obstacles while tracking a predefined reference path is critical for any industrial manipulator. The formulated control framework unifies the tracking control and obstacle avoidance into a single constrained optimization problem by introducing a penalty term into the objective function, which actively rewards the optimizer for avoiding the obstacles. One of the significant features of the proposed framework is the way that the penalty term is formulated following a straightforward principle: maximize the minimum distance between manipulator and obstacle. The distance calculations are based on GJK (Gilbert-Johnson-Keerthi) algorithm, which calculates the distance between manipulator and obstacle by directly using their 3D-geometries. Which also implies that our algorithm works for arbitrarily shaped manipulator and obstacle. Theoretical treatment proves the stability and convergence, and simulations results using LBR IIWA 7-DOF manipulator are presented to analyze the performance of the proposed framework.

Based on a bio-heuristic algorithm, this paper proposes a novel path planner called obstacle avoidance beetle antennae search (OABAS) algorithm, which is applied to the global path planning of unmanned aerial vehicles (UAVs). Compared with the previous bio-heuristic algorithms, the algorithm proposed in this paper has advantages of a wide search range and breakneck search speed, which resolves the contradictory requirements of the high computational complexity of the bio-heuristic algorithm and real-time path planning of UAVs. Besides, the constraints used by the proposed algorithm satisfy various characteristics of the path, such as shorter path length, maximum allowed turning angle, and obstacle avoidance. Ignoring the z-axis optimization by combining with the minimum threat surface (MTS), the resultant path meets the requirements of efficiency and safety. The effectiveness of the algorithm is substantiated by applying the proposed path planning algorithm on the UAVs. Moreover, comparisons with other existing algorithms further demonstrate the superiority of the proposed OABAS algorithm.

Zeroing neural dynamics (ZND) can be seen as an effective controller to solve various challenging scientific and engineering problems. Computing Lyapunov equation is a kind of important issue in nonlinear systems for stability analysis in control. This paper presents a systematic and constructive procedure on using ZND to design control laws based on the efficient solution of dynamic Lyapunov equation. We particularly address three important aspects in the design: 1. the global stability of ZND, to guarantee the effectiveness of the solution; 2. the robustness against additive noises, to ensure the capability of ZND for using in harsh environments; 3. the finite-time convergence of ZND, to endow ZND for real-time solution of dynamical problems. To do so, a novel formula is first designed in a unified manner of ZND. Differing from the conventional formula appeared in ZND, the proposed formula simultaneously has finite-time convergence and noise robustness property. According to this novel formula, a novel control law (termed nonlinear neural dynamics, NND) is established to compute dynamic Lyapunov equation in the presence of various additive noises. Both theoretical and simulative results ensure the finite-time convergence and noise robustness property of the NND model for computing dynamic Lyapunov equation in the front of various additive noises. As compared to the conventional ZND model for computing dynamic Lyapunov, the superior property of the NND model is further demonstrated.

This paper proposes a novel active vibration damping mechanism for soft robots. In recent years, soft robots have gained increasing research attention for robotic researchers and industrial developers alike. Soft robots offer a significant number of advantages when it comes to the handling of fragile objects, clinical rehabilitation tasks, and human-machine interaction. Soft robots demonstrate a high degree of compliance and safety because of their inherent softness, achieving the same with rigid robots will require intricate controller design and sensing mechanisms. However, the most commonly used soft robots use pneumatic systems for actuation. These pneumatic soft robots undergo large amplitude vibrations when deactuated suddenly. These vibrations not only decrease the accuracy of these soft robots but also compromise their structural integrity, which results in a decrease in their useable lifespan. An active vibration damping mechanism is very much needed to increase the utility of soft robots in industrial applications. To accurately control the dynamic behavior of soft robots, we propose a sliding mode based controller with PID sliding surface. The proposed controller uses feedback error to define a PID sliding surface, and a nonlinear sliding mode controller works to keep the system attached to the sliding surface. The coefficients of the PID sliding surface determine the dynamic behavior of the soft robot. The performance of the proposed controller is verified by using a multi-chambered parallel soft robot. The experimental results demonstrate that the proposed controller can suppress vibration amplitude to a decidedly smaller range.

The problem of portfolio management relates to the selection of optimal stocks, which results in a maximum return to the investor while minimizing the loss. Traditional approaches usually model the portfolio selection as a convex optimization problem and require the calculation of gradient. Note that gradient-based methods can stuck at local optimum for complex problems and the simplification of portfolio optimization to convex, and further solved using gradient-based methods, is at a high cost of solution accuracy. In this paper, we formulate a nonconvex model for the portfolio selection problem, which considers the transaction cost and cardinality constraint, thus better reflecting the decisive factor affecting the selection of portfolio in the real-world. Additionally, constraints are put into the objective function as penalty terms to enforce the restriction. Note that this reformulated problem cannot be readily solved by traditional methods based on gradient search due to its nonconvexity. Then, we apply the Beetle Antennae Search (BAS), a nature-inspired metaheuristic optimization algorithm capable of efficient global optimization, to solve the problem. We used a large real-world dataset containing historical stock prices to demonstrate the efficiency of the proposed algorithm in practical scenarios. Extensive experimental results are presented to further demonstrate the efficacy and scalability of the BAS algorithm. The comparative results are also performed using Particle Swarm Optimizer (PSO), Genetic Algorithm (GA), Pattern Search (PS), and gradient-based fmincon (interior-point search) as benchmarks. The comparison results show that the BAS algorithm is six times faster in the worst case (25 times in the best case) as compared to the rival algorithms while achieving the same level of performance.