This paper presents a model-based controller consisting of a feedback linearization scheme and a state-dependent proportional derivative (PD) controller adapted to a parallel flight simulator Stewart mechanism. This parallel robot is considered to emulate motions of highly maneuverable aircrafts, which require well-trained pilots. The simulations are based upon a reduced-model prototype built in order to verify kinematic design aspects and control laws. Indeterminacies in the mass distribution of the system will generally affect model-based controllers, necessitating compensation or the employment of robust control methods. Through introducing the pilot's sensorial feedback of acceleration, the pilot's behavior in giving commands is emulated via an optimization process, which tunes the controller coefficients accordingly. Stability of the designed control system is guaranteed via the Lyapunov approach. To further explore the system through perilous flight scenarios, three pre-designed maneuvers are selected as test cases. It is expected that closed-loop control tasks in which a pilot tracks a target, while at the same time the controller rejects disturbances and adapts itself to the pilot's progressive skills, are ameliorated through this arrangement. Numerical results show that the proposed method is found robust in the training process in conditions of parameters indeterminacy.

This paper reports the design, analysis, and control of a miniature vibration-driven planar locomotion robot called Shell. A vibration-driven system is able to achieve locomotion based on internal oscillations and anisotropic friction forces. In this robot design, two parallel oscillators are employed to provide propelling forces, and a blade-like support is designed to generate anisotropic frictional contact with the ground. If the two parallel oscillators are of different frequencies and amplitudes, two-dimensional locomotion of the robot can be achieved. To predict the robot's planar locomotion, a dynamic model is developed. Controlling the robot's locomotion and especially, the locomotion modes can be achieved by adjusting the vibration frequencies of the two internal oscillators. Experimental results show that Shell can be controlled to move rectilinearly and along circles with certain curvatures. In addition, by combining these basic trajectories, Shell can move freely on a horizontal plane.

The problem of robust adaptive control of a robotic manipulator subjected to uncertain dynamics and joint space constraints is addressed in this paper. Command filters are used to overcome the time derivatives of virtual control, thus reducing the need for desired trajectory differentiations. A barrier Lyapunov function is used to deal with the joint space constraints. A robust adaptive support vector regression architecture is used to reduce filtering errors, approximation errors and handle dynamic uncertainties. The stability analysis of the closed-loop system using the Lyapunov theory permits to highlight adaptation laws and to prove that all signals of the closed-loop system are bounded. Simulations show the effectiveness of the proposed control strategy.

A non-holonomic robot with a bounded control input travels in a dynamic unknown 3D environment with moving obstacles. We propose a 3D navigation strategy to reach a given final destination point while avoiding collisions with obstacles. A formal analysis of the proposed 3D robot navigation algorithm is given. Computer simulation results and experiments with a real flying autonomous vehicle confirm the applicability and performance of the proposed guidance approach.

We present an approach that allows a robot to generate trajectories to perform a set of instances of a task using few physical trials. Specifically, we address manipulation tasks which are highly challenging to simulate due to complex dynamics. Our approach allows a robot to create a model from initial exploratory experiments and subsequently improve it to find trajectory parameters to successfully perform a given task instance. First, in a model generation phase, local models are constructed in the vicinity of previously conducted experiments that explain both task function behavior and estimated divergence of the generated model from the true model when moving within the neighborhood of each experiment. Second, in an exploitation-driven updating phase, these generated models are used to guide parameter selection given a desired task outcome and the models are updated based on the actual outcome of the task execution. The local models are built within adaptively chosen neighborhoods, thereby allowing the algorithm to capture arbitrarily complex function landscapes. We first validate our approach by testing it on a synthetic non-linear function approximation problem, where we also analyze the benefit of the core approach features. We then show results with a physical robot performing a dynamic fluid pouring task. Real robot results reveal that the correct pouring parameters for a new pour volume can be learned quite rapidly, with a limited number of exploratory experiments.

Variable stiffness can improve the capability of human–robot interacting. Based on the mechanism of a flexible rack and gear, a rotational joint actuator named vsaFGR is proposed to regulate the joint stiffness. The flexible gear rack can be regarded as a combination of a non-linear elastic element and a linear adjusting mechanism, providing benefits of compactness. The joint stiffness is in the range of 217–3527 N.m/rad, and it is inversely proportional to the 4th-order of the gear displacement, and nearly independent from the joint angular deflection, providing benefits of quick stiffness regulation in a short displacement of 20 mm. The gear displacement with respect to the flexible gear rack is perpendicular to the joint loading force, so the power required for stiffness regulating is as low as 14.4 W, providing benefits of energy saving. The working principles of vsaFGR are elaborated, followed by presentation on the mechanics model and the prototype. The high compactness, great stiffness range and low power cost of vsaFGR are proved by simulations and experiments.

This paper presents a fractional-order sliding mode control scheme equipped with a disturbance observer for robust impedance control of a single-link flexible robot arm when it comes into contact with an unknown environment. In this research, the impedance control problem is studied for both unconstrained and constrained maneuvers. The proposed control strategy is robust with respect to the changes of the environment parameters (such as stiffness and damping coefficient), the unknown Coulomb friction disturbances, payload, and viscous friction variations. The proposed control scheme is also valid for both unconstrained and constrained motions. Our novel approach automatically switches from the free to the constrained motion mode using a simple algorithm of contact detection. In this regard, an impedance control scheme is proposed with the inner loop position control. This means that in the free motion, the applied force to the environment is zero and the reference trajectory for the inner loop position control is the desired trajectory. However, in the constrained motion the reference trajectory for the inner loop is determined by the desired impedance dynamics. Stability of the closed loop control system is proved by Lyapunov theory. Several numerical simulations are carried out to indicate the capability and the effectiveness of the proposed control scheme.

An adaptive back-stepping sliding mode controller (ABSMC) algorithm was developed for nonlinear uncertain systems based on a nonlinear disturbance observer (NDO). The developed ABSMC was applied to attitude control for the dual arm of a humanoid robot. Considering the system uncertainty and the unknown external disturbances, the ABSMC scheme was designed to eliminate the chattering phenomenon in the traditional sliding mode control and to reduce the tracking error closer to zero. The ABSMC algorithm solved problems related to the chattering of the system for both uncertainties and disturbances in the humanoid robotic system with an NDO in a two-dimensional environment. The algorithm was designed to work equally well with agents, with higher degrees of freedom in different applications. The method was appropriate for improving tracking performance. The ABSMC algorithm guaranteed global stability and improved the dynamic performance of the system. The algorithm inherited a low computational cost, probabilistic completeness, and asymptotic optimality from the fuzzy sliding mode control. This algorithm has a practical application in the dual arm of a humanoid robot with a circular trajectory. This paper showed the effectiveness and applicability of the proposed methods, which reduced the output of the controller and improved the control performance of the humanoid robotic system. The new combined control algorithm, ABSMC, was able to feasibly and efficiently weaken the chattering on the robot's closed-loop paths, starting and finishing at the same configuration.

This study deals with the problem of trajectory tracking of wheeled mobile robots (WMR's) under non-holonomic constraints and in the presence of model uncertainties. To solve this problem, the kinematic and dynamic models of a WMR are first derived by applying the recursive Gibbs–Appell method. Then, new kinematics- and dynamics-based multivariable controllers are analytically developed by using the predictive control approach. The control laws are optimally derived by minimizing a pointwise quadratic cost function for the predicted tracking errors of the WMR. The main feature of the obtained closed-form control laws is that online optimization is not needed for their implementation. The prediction time, as a free parameter in the control laws, makes it possible to achieve a compromise between tracking accuracy and implementable control inputs. Finally, the performance of the proposed controller is compared with that of a sliding mode controller, reported in the literature, through simulations of some trajectory tracking maneuvers.

Ankle inversion is a common injury of musculoskeletal system among athletes and also in the older population. Investigation into ankle inversion requires quantitative assessment of the smallest amount of height/angle change in the floor that can be perceived by human. Blocks of different thickness have been used to change floor height manually during tests. We aimed to develop an automatic apparatus that is able to provide improved height and angle resolutions for dynamic ankle proprioception. We designed and manufactured a five-bar planar robot with one coupler serving as the mobile platform. We used a stiffening rib to achieve consistent differences in deflection across the workspace of the mobile platform. The reported robot translates at the maximal speed 423 mm/s with a resolution at 0.21 mm under a maximal load of 358 kg. This robot allows for increased sensitivity, which may lead to further investigation of functional proprioceptive ability and reflect finely tuned sensory requirements for upright stance.

Exploration is a fundamental problem in robotics, and multi-robot systems exploration has been extensively studied in this field. In order to overcome the problem of a non-optimal target being selected in the exploration process, a revised single linkage clustering frontier cell (CFC) algorithm is proposed to calculate the exploration benefit of all available frontier cells. Moreover, there exist unexplored islands for most of the bidding-based multi-robot coordination algorithms in the exploration of unknown environments. To deal with this problem, some rules switched by emotion states are proposed. So, the proposed bidding coordination algorithm with CFC and an emotion switch has a hierarchical architecture. The upper level is modeled as an automaton, which is used to represent emotion status, and the emotion variables decide whether a robot will participate in a bid and explore an unknown area abiding by the walking rules. In the lower level, the robots perform bidding activities with CFC and the walking rules according to the emotion variables. We tested and evaluated our approach by means of experiments both in a simulated environment and with real robots. The experiments results demonstrate that the exploration efficiency is improved, and our algorithm has a greater coverage rate than classic bidding-based coordination algorithms.

In this paper, a walking pattern optimization procedure is implemented to yield the optimal heel-strike and toe-off motions for different goal functions. To this end, first, a full dynamic model of a humanoid robot equipped with active toe joints is developed. This model consists of two parts: multi-body dynamics of the robot which is obtained by Lagrange and Kane methods and power transmission dynamic model which is developed using system identification approach. Then, a gait planning routine is presented and consistent parameters are specified. Several simulations and experimental tests are carried out on SURENA III humanoid robot which is designed and fabricated at the Center of Advanced Systems and Technologies located in the University of Tehran. Afterward, a genetic algorithm optimization is adopted to compute the optimal walking patterns for five different goal functions including energy consumption, stability margin, joint velocity, joint torque and required friction coefficient. Also, several parametric analyses are performed to characterize the effects of heel-strike and toe-off angle and toe link mass and length on these five goal functions. Finally, it is concluded that walking pattern without heel-strike and toe-off motions requires less friction coefficient than the pattern with heel-strike and toe-off motions. Also, heavier toe link lowers tip-over instability and slippage occurrence possibility, but requires more energy consumption and joint torque.