Implementation of robotic ankle–foot orthosis with an impedance-based assist-as-needed control strategy

In this paper, a robotic ankle–foot orthosis (AFO) is developed for individuals with a paretic ankle, and an impedance-based assist-as-needed controller is designed for the robotic AFO to provide adaptive assistance. First, a description of the robotic AFO hardware design is presented. Next, the design of the finite state machine is introduced, followed by an introduction to the modelling of the robotic AFO. Additionally, the control of the robotic AFO is presented. An impedance-based high-level controller that is composed of an ankle impedance based torque generation controller and an impedance controller is designed for the high-level control. A compensated low-level controller that is composed of a braking controller and a proportional-derivative controller with a compensation part is designed for the low-level control. Finally, a pilot study is conducted, and the experimental results demonstrate that with the proposed control algorithm, the robotic AFO has the potential for ankle rehabilitation by providing adaptive assistance. In the assisted condition with a high level of assistance, reductions of 8% and 20.1% of the root mean square of the tibialis anterior and lateral soleus activities are observed, respectively.

Sistem Kontrol Swarm untuk Flocking Wahana NR-Awak Quadrotor dengan Optimasi Algoritma Genetik

<p class="Abstrak">Quadrotor merupakan wahana udara nir-awak jenis lepas landas atau pendaratan vertikal berbentuk silang dan memiliki sebuah rotor pada setiap ujung lengannya dengan kemampuan manuver yang tinggi. <em>Swarm</em> quadrotor yang terdiri dari sekumpulan quadrotor akan menjadi suatu <em>swarm</em> yang baik, sesuai dengan kriteria <em>swarm</em> oleh Reynold yaitu dapat menghindari tumbukan, menyamakan kecepatan, dan pemusatan <em>swarm</em>. Pengontrolan <em>s</em><em>warm</em> quadrotor memiliki tingkat kerumitan yang tinggi karena melibatkan banyak agen. Riset pengembangan <em>swarm </em>quadrotor masih belum banyak dilakukan dan masih membuka peluang untuk meneliti dengan metoda lain yang lebih baik dalam menghasilkan <em>swarm</em>. Makalah ini mengusulkan pengontrolan <em>swarm</em> quadrotor yang terdiri dari dua tingkat lup kontrol. Lup pertama adalah pengontrol sistem model <em>swarm</em> untuk membangkitkan lintasan <em>swarm</em> dan lup kedua merupakan pengontrol pada quadrotor untuk melakukan penjejakan lintasan <em>swarm</em>. Pengontrol pertama menggunakan pengontrol proporsional derivatif (PD), sedangkan pengontrol kedua menggunakan regulator linier kuadratik (RLK). Pengontrol yang dirancang memiliki parameter yang banyak, sehingga pemilihan parameter yang optimal sangat sulit. Pencarian parameter optimal pada pengontrol model <em>swarm</em> quadrotor membutuhkan teknik optimasi seperti algoritma genetik (AG) untuk mengarahkan pencarian menuju solusi yang menghasilkan kinerja terbaik. Pada makalah ini, penalaan dengan optimasi AG hanya dilakukan pada pengontrol PD untuk menghasilkan lintasan <em>swarm</em> terbaik, sedangkan matrik bobot RLK dilakukan secara uji coba. Hasil simulasi <em>swarm</em> pada model quadrotor menunjukkan parameter , . , dan yang diperoleh menggunakan AG menghasilkan pergerakan <em>swarm</em> yang baik dengan kesalahan RMS pelacakan 0,0094 m terhadap fungsi obyektif. Sedangkan ketika parameter , dan dicari menggunakan AG, tidak berpengaruh banyak dalam memperbaiki hasil simulasi swarm quadrotor.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstract"><em>The quadrotor is a type of take-off or vertical landing unmanned aerial vehicles with a cross shape and has one rotor at each end of its arm with high maneuverability. A quadrotor swarm consisting of a group of quadrotors leads to a good swarm, according to Reynold's swarm criteria, which accomplishes collision avoidance, velocity matching, and flock centering. Quadrotor swarm control has a high level of complexity because it involves many agents. Research on the development of quadrotor swarm has received insignificant attention and it still opens opportunities to research other methods that are better at producing swarm. The paper proposes the control of a quadrotor swarm consisted of two levels of control loops. The first loop controls the swarm model system to generate the swarm trajectory and the second loop is the controller on the quadrotor to track the swarm path. The first controller uses a proportional derivative controller (PD), while the second controller uses the linear quadratic regulator (LQR). The controller that is designed has many parameters, so the optimal parameter selection is very difficult. The search for optimal parameters in the swarm model controller requires optimization techniques such as the genetic algorithm (GA) to direct the search for solutions that produce the best performance. In this paper, tuning with the optimization of GA is only done for the PD controller in order to produce the best swarm trajectory, while the weight matrices of the LQR are done on a trial error basis. Swarm simulation results of a quadrotor model system show the parameters , . , and obtained using GA produce a good swarm movement with RMS error 0.0094 m of the objective function. Whereas when parameters , and are searched using GA, it does not have much effect in improving the quadrotor swarm simulation results.</em></p><p class="Abstrak"><em><strong><br /></strong></em></p>

Trajectory Tracking with Adaptive Robust Control for Quadrotor

This work proposes three robust mechanisms based on the MIT rule and the sliding-mode techniques. These robust mechanisms have to tune the gains of an adaptive Proportional-Derivative controller to steer a quadrotor in a predefined trajectory. The adaptive structure is a model reference adaptive control (MRAC). The robust mechanisms proposed to achieve the control objective (trajectory tracking) are MIT rule, MIT rule with sliding mode (MIT-SM), MIT rule with twisting (MIT-Twisting), and MIT rule with high order sliding mode (MIT-HOSM).

Active vibration control of building structure using active disturbance rejection control

This article presents an active vibration control of seismically excited building structures. The control scheme is based on active disturbance rejection control, which is an attractive alternative technique for structural vibration suppression and practical motion control solution in the presence of parametric uncertainties and disturbances. The proposed active disturbance rejection control scheme uses a generalized proportional integral observer, which allows us to estimate in real time the unknown dynamics and disturbances in the building structure to cancel their effect using a part of the control signal. First, the active disturbance rejection control provides a proportional derivative controller with robustness to external disturbances and uncertainties, and its structure is expressed in a compact error-based form. An important advantage with respect to other methods is that the proposed scheme does not need the system parameters. Moreover, supposing that displacement and velocity cannot be measured directly, an online robust adaptive observer is introduced to estimate both data, required for the proportional derivative controller. The adaptive observer removes constant disturbance and attenuates measurement noise in acceleration data. Under this line, a second active disturbance rejection control scheme is introduced based on a proportional controller that, unlike proportional derivative, it only needs the velocities that can be directly estimated by integrating the acceleration signals and does not require the adaptive observer. An advantage of this scheme is its simplicity to be implemented because it only needs to tune the proportional gain. Furthermore, this scheme has a similar performance of the proportional derivative controller. The effectiveness of the proposed active disturbance rejection control schemes is demonstrated through experimental results of a reduced scale five-story building structure. The results are found to be a good step in that direction, confirming that the proposed method is promising for practical applications.

Autonomous Underwater Robot Fuzzy Motion Control System with Parametric Uncertainties

The paper describes the design of a fuzzy motion control system of an autonomous underwater vehicle. A mathematical model of the underwater vehicle is synthesized. A fuzzy regulator for controlling the depth of immersion autonomous underwater vehicle is designed. The quality of control for step control, harmonic control, as well as various types of exogenous disturbances, is investigated. The comparison of the functioning quality of the designed fuzzy controller with the proportional–derivative controller is made. It is shown that the designed fuzzy controller provides a higher quality of control compared to the proportional–derivative controller. The proposed fuzzy controller provides high-quality control of the plant under uncertainties.

Neural network inverse system decoupling fuzzy self-tuning proportional–derivative control strategy of a bearingless induction motor

In view of the problems that the analytical inverse system decoupling method of bearingless induction motor is sensitive to the change of motor parameters and is greatly affected by unmodeled dynamics, and that of traditional proportional–derivative controller lacking the self-adaptive regulation ability, a neural network inverse system decoupling fuzzy self-tuning proportional–derivative control strategy is proposed for a bearingless induction motor system. First, under the conditions of considering the stator current dynamic of torque winding, and by neural network inverse system method, the bearingless induction motor system is decoupled into four pseudo-linear integral subsystems. Second, the traditional proportional–derivative controller is improved, and the fuzzy control algorithm is used to adjust the parameters of improved proportional–derivative controller adaptively. Thus, a neural network inverse system decoupling fuzzy self-tuning proportional–derivative control system is constructed for a bearingless induction motor. The simulation experimental results show that the proposed control strategy not only can effectively improve the stability, robustness and steady-state control accuracy of bearingless induction motor system, but also can significantly improve the dynamic response speed and the ability to resist the influences of motor parameter change and load disturbance.