This article presents an adaptive neural network (ANN) control scheme based on a disturbance observer that can achieve trajectory tracking control of robotic manipulators under external disturbances and dynamic model uncertainties. Firstly, an ANN controller based on full-state feedback is derived using the backstepping technique to achieve an online approximation of uncertainty. The integral sliding mode surface with a position error is introduced into the controller, which reduces the steady-state error of the system and enhances robustness. Then, a novel disturbance observer is designed to estimate both the approximation errors of the ANN and external disturbances, and to provide compensation for the controller, effectively suppressing the trajectory tracking errors caused by approximation errors and disturbances. Subsequently, the Lyapunov stability theory is utilized to demonstrate the stability of the developed control strategy and the boundedness of all closed-loop signals. Finally, numerical simulations are used to confirm the efficacy of the proposed control method.

The optimal reactive power dispatch (ORPD) is a complex, optimal non-meritorious control issue with continuous and discontinuous control variables. This article exhibits a whale optimization algorithm (WOA) motivated by the whale’s bubble-net hunting tactic to resolve ORPD. The intention is to comply with certain constraints to promote the voltage transmission quality by adequately altering the parameters. The WOA not only equalizes exploitation and exploration to maximize the overall performance and eliminate search stagnation but also has remarkable sustainability and robustness to accomplish superior convergence speed and computation accuracy. The WOA is contrasted with MFO, BA, GOA, GWO, MDWA, SMA, SPBO and SSA by diminishing the fitness value to highlight the superiority and stability. The experimental results reveal that WOA exhibits a superior convergence level and computation precision to accomplish the minimum active power loss and superior control variables.

This work presents a rat optimization algorithm (ROA), which simulates the social behavior of rats and is a new nature-inspired optimization technique. The ROA consists of three operators that simulate rats searching for prey, chasing and fighting prey, and jumping and hunting prey to deal with optimization issues. The Levy flight strategy is introduced into the ROA to keep the algorithm from running into issues with slow convergence and local optimums. The ROA is tested with four real-world engineering optimization issues and twenty-two benchmark functions. Experiments show that the ROA is particularly effective at solving real-world optimization problems compared to other well-known optimization techniques.

In this work, an improved symmetric integral barrier function-based tracking control is addressed for a robotic manipulator in the presence of position error performance requirements and unknown uncertain terms. First of all, we construct an improved symmetric time-variant integral barrier function to solve the robotic system’s constrained requirements. The novel barrier function is constructed by developing an integral upper limit function that can be used in conjunction with existing performance envelope functions. Then, the equivalent conversion error is taken as the upper limit of the integral function to achieve the specified steady-state as well as transient behaviors of the system. Additionally, a disturbance observer based on the velocity tracking error is introduced to compensate for unknown uncertain terms. In the end, a numeral simulation study on a robot with two degrees is performed to indicate that the present scheme is feasible in handling the performance constraint and uncertain terms.

A time-synchronized (TS) convergence control method for robotic manipulators is proposed. Adversely to finite-time control, a notion of time-synchronization convergence is introduced based on the ratio persistence property, which can ensure that all system components converge simultaneously in a finite time. Firstly, a robust disturbance observer is constructed to be compatible with the time-synchronized control framework and precisely estimate system uncertainties. Furthermore, we design a (finite) time-synchronized controller to ensure that all states of the robotic manipulator simultaneously converge to an equilibrium point, irrespective of initial conditions. Stability analysis shows the feasibility of the proposed TS control method. At last, simulations are performed with a two-link rehabilitation robotic system, and the comparison results indicate its superiority.

This paper proposes a fractional-order chaotic system using a tri-stable locally active memristor. The characteristics of the memristor, dynamic mechanism of oscillation, and behaviors of the proposed system were analyzed, and then a visually meaningful image encryption scheme was designed based on the chaotic system, DNA encoding, and integer wavelet transform (IWT). Firstly, the mathematical model of the memristor was designed, which was nonvolatile, locally active, and tri-stable. Secondly, the stability, dynamic mechanism of oscillation, bifurcation behaviors, and complexity of the fractional-order memristive chaotic system were investigated and the conditions of stability were obtained. Thirdly, the largest Lyapunov exponent, bifurcation diagram, and complexity of the novel system were calculated and the coexisting bifurcation, coexisting attractors, spectral entropy, and so on are shown. Finally, a visually meaningful image encryption scheme based on the proposed system was designed, and its security was assessed by statistical analysis and different attacks. Numerical simulation demonstrated the effectiveness of the theoretical analysis and high security of the proposed image encryption scheme.